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Appl. Sci., Volume 8, Issue 12 (December 2018) – 371 articles

Cover Story (view full-size image): The physical arrangement of pitches in most traditional musical instruments—including the piano and guitar—is non-isomorphic, which means that a given spatial relationship between two keys, buttons, or fretted strings can produce differing musical pitch intervals. Since the nineteenth century, it has been widely considered that isomorphic pitch layouts, which do not have such inconsistencies, facilitate the learnability and playability of instruments—particularly when a piece needs to be transposed into a different key. However, prior to this paper, this has not been experimentally tested. The figure shows an example of one of the pitch layouts used in this experiment. View this paper.
December Issue contains the entire special issue Outstanding Topics in Ocean Optics
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8 pages, 3240 KiB  
Article
Interface Growth and Void Formation in Sn/Cu and Sn0.7Cu/Cu Systems
by Jieshi Chen, Yongzhi Zhang, Zhishui Yu, Peilei Zhang, Wanqin Zhao, Jin Yang and Di Wu
Appl. Sci. 2018, 8(12), 2703; https://doi.org/10.3390/app8122703 - 19 Dec 2018
Cited by 13 | Viewed by 4223
Abstract
In this work, the effects of electroplated Cu (EP Cu) and Cu addition (0.7%) in Sn solder on the intermetallic compounds (IMCs) growth and void formation were clarified by comparison with solder joints comprising of high purity Cu (HP Cu) substrate and pure [...] Read more.
In this work, the effects of electroplated Cu (EP Cu) and Cu addition (0.7%) in Sn solder on the intermetallic compounds (IMCs) growth and void formation were clarified by comparison with solder joints comprising of high purity Cu (HP Cu) substrate and pure Sn solder. After aging processes, a new IMC, Cu3Sn, was formed at the interface, in addition to Cu6Sn5 formed in the as-soldered joints. The EP Cu and Cu addition (0.7%) both had limited effects on the total IMCs thickness. However, the effects varied on the growth behaviors of different IMCs. Comparing to the void-free interface between Sn and HP Cu, a large number of voids were observed at the Cu3Sn/Cu interface in Sn/EP Cu joints. The formation of these voids may be induced by the impurities and fine grain, which were introduced during the electroplating process. The addition of Cu suppressed the inter-diffusion of Cu and Sn at the interface. Consequently, the growth of the Cu3Sn layer and formation of voids were suppressed. Full article
(This article belongs to the Special Issue Selected Papers from the NMJ2018)
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<p>Interfacial microstructure of Sn/HP Cu joints after aging at 150 °C for (<b>a</b>) 0 h, (<b>b</b>) 240 h, and (<b>c</b>) 480 h.</p>
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<p>Interfacial microstructure of Sn/EP Cu joints after aging at 150 °C for (<b>a</b>) 0 h, (<b>b</b>) 240 h, and (<b>c</b>) 480 h.</p>
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<p>Interfacial microstructure of Sn0.7Cu/EP Cu joints after aging at 150 °C for (<b>a</b>) 0 h, (<b>b</b>) 240 h, and (<b>c</b>) 480 h.</p>
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<p>Interfacial microstructure of Sn/EP Cu joints with different aging conditions, (<b>a</b>) 150 °C for 120 h, (<b>b</b>) 180 °C for 72 h.</p>
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<p>Relationship between aging time and average thickness of total IMCs (<b>a</b>), Cu<sub>6</sub>Sn<sub>5</sub> (<b>b</b>), and Cu<sub>3</sub>Sn (<b>c</b>) layer for the three kinds of solder joints after aging at 150 °C and 180 °C (<b>d</b>), respectively.</p>
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<p>The components of EP Cu substrates by XPS spectra.</p>
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<p>Schematic drawings showing the general process for interfacial behavior in Sn/HP Cu (<b>a</b>), Sn/EP Cu (<b>b</b>), and Sn0.7Cu/EP Cu (<b>c</b>) systems.</p>
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15 pages, 1930 KiB  
Article
Resistance of L. monocytogenes and S. Typhimurium towards Cold Atmospheric Plasma as Function of Biofilm Age
by Marlies Govaert, Cindy Smet, Maria Baka, Branimir Ećimović, James L. Walsh and Jan Van Impe
Appl. Sci. 2018, 8(12), 2702; https://doi.org/10.3390/app8122702 - 19 Dec 2018
Cited by 26 | Viewed by 4206
Abstract
The biofilm mode of growth protects bacterial cells against currently applied disinfection methods for abiotic (food) contact surfaces. Therefore, innovative methods, such as Cold Atmospheric Plasma (CAP), should be investigated for biofilm inactivation. However, more knowledge is required concerning the influence of the [...] Read more.
The biofilm mode of growth protects bacterial cells against currently applied disinfection methods for abiotic (food) contact surfaces. Therefore, innovative methods, such as Cold Atmospheric Plasma (CAP), should be investigated for biofilm inactivation. However, more knowledge is required concerning the influence of the biofilm age on the inactivation efficacy in order to comment on a possible application of CAP in the (food) processing industry. L. monocytogenes and S. Typhimurium biofilms with five different ages (i.e., 1, 2, 3, 7, and 10 days) were developed. For the untreated biofilms, the total biofilm mass and the cell density were determined. To investigate the biofilm resistance towards CAP treatment, biofilms with different ages were treated for 10 min and the remaining cell density was determined. Finally, for the one-day old reference biofilms and the most resistant biofilm age, complete inactivation curves were developed to examine the influence of the biofilm age on the inactivation kinetics. For L. monocytogenes, an increased biofilm age resulted in (i) an increased biomass, (ii) a decreased cell density prior to CAP treatment, and (iii) an increased resistance towards CAP treatment. For S. Typhimurium, similar results were obtained, except for the biomass, which was here independent of the biofilm age. Full article
(This article belongs to the Special Issue Cold Plasma Treatment for Food Safety and Quality)
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<p>Dielectric Barrier Discharge (DBD) electrode: graphical illustration (<b>left</b>); set-up used in this study (<b>middle</b>); and close-up of the discharge (<b>right</b>).</p>
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<p>Total biofilm mass, expressed as optical density (-), for the untreated (<b>a</b>) <span class="html-italic">L. monocytogenes</span> and (<b>b</b>) <span class="html-italic">S.</span> Typhimurium biofilms following 1, 2, 3, 7, and 10 day(s) of incubation. For each species, bars bearing different lowercase letters are significantly different (<span class="html-italic">p</span> ≤ 0.05).</p>
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<p>Cell density (log(CFU/cm<sup>2</sup>)) determined on general and selective medium for the untreated (<b>a</b>) <span class="html-italic">L. monocytogenes</span> and (<b>b</b>) <span class="html-italic">S.</span> Typhimurium biofilms following 1, 2, 3, 7, and 10 day(s) of incubation. Bars bearing different letters (no capital or small letters in common for the general and the selective medium, respectively) are significantly different (<span class="html-italic">p</span> ≤ 0.05).</p>
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<p>Log-reductions (log(CFU/cm<sup>2</sup>)) obtained on general and selective medium for (<b>a</b>) <span class="html-italic">L. monocytogenes</span> and (<b>b</b>) <span class="html-italic">S.</span> Typhimurium biofilms treated for 10 min with Cold Atmospheric Plasma (CAP) at optimal inactivation conditions (i.e., DBD, 0.0 (<span class="html-italic">v</span>/<span class="html-italic">v</span>) % oxygen, 21.88 V input voltage). Bars bearing different letters (no capital or small letters in common for the general and the selective medium, respectively) are significantly different (<span class="html-italic">p</span> ≤ 0.05).</p>
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<p>Cell density (log(CFU/cm<sup>2</sup>)) as function of the treatment time following CAP treatment of the one and seven day(s) old biofilms at optimal CAP conditions for biofilm inactivation. Experimental data (symbols) and global fit (line) of the Geeraerd et al. [<a href="#B27-applsci-08-02702" class="html-bibr">27</a>] model: total viable population on general medium (o, solid line) and uninjured viable population on selective medium (x, dashed line) (left). Percentage (%) of sub-lethally injured cells as function of the treatment time for both investigated biofilm ages (i.e., one and seven day(s)) (right). (<b>a</b>) Results obtained following CAP treatment of the <span class="html-italic">L. monocytogenes</span> biofilms; (<b>b</b>) Results obtained following CAP treatment of the <span class="html-italic">S.</span> Typhimurium biofilms.</p>
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18 pages, 3331 KiB  
Article
Dynamic Response Analysis of Rutting Resistance Performance of High Modulus Asphalt Concrete Pavement
by Chundi Si, Hang Cao, Enli Chen, Zhanping You, Ruilan Tian, Ran Zhang and Junfeng Gao
Appl. Sci. 2018, 8(12), 2701; https://doi.org/10.3390/app8122701 - 19 Dec 2018
Cited by 26 | Viewed by 4270
Abstract
In order to systematically study the rutting resistance performance of High-Modulus Asphalt Concrete (HMAC) pavements, a finite element method model of HMAC pavement was established using ABAQUS software. Based on the viscoelasticity theory of asphalt, the stress and deformation distribution characteristics of HMAC [...] Read more.
In order to systematically study the rutting resistance performance of High-Modulus Asphalt Concrete (HMAC) pavements, a finite element method model of HMAC pavement was established using ABAQUS software. Based on the viscoelasticity theory of asphalt, the stress and deformation distribution characteristics of HMAC pavement were studied and compared to conventional asphalt pavement under moving loads. Then, the pavement temperature field model was established to study the temperature variation and the thermal stress in HMAC pavement. Finally, under the condition of continuous temperature variation, the creep behavior and permanent deformation of HMAC pavement were investigated. The results showed that under the action of moving loads, the strain and displacement generated in HMAC pavement were lower than those in conventional asphalt pavement. The upper surface layer was most obviously affected by outside air temperature, resulting in maximum thermal stress. Lastly, under the condition of continuous temperature change, HMAC pavement could greatly reduce the deformation of asphalt material in each surface layer compared to conventional asphalt pavement. Full article
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<p>Finite element model of pavement structure.</p>
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<p>Diagram of loading regions.</p>
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<p>Comparison of vertical stress in the upper surface layer.</p>
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<p>Comparison of vertical displacement in the upper surface layer.</p>
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<p>Comparison of vertical strain in the upper surface layer.</p>
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<p>Comparison of vertical shear stress in the middle surface layer.</p>
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<p>Comparison of vertical shear strain in the middle surface layer.</p>
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<p>Temperature in each structural layer over 24 h.</p>
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<p>Thermal stress of each structural layer over 24 h.</p>
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<p>Distribution of creep strain at different depths under the center of the leftmost wheel.</p>
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<p>Distribution of creep strain at different depths under the left dual-wheel center.</p>
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<p>Comparison of vertical deformation at different depths underneath the center of the leftmost wheel.</p>
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<p>Comparison of vertical deformation at different depths underneath the dual-wheel center.</p>
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<p>Comparison of the relative maximum deformation of two types of pavement at different depths.</p>
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<p>Comparison of maximum relative deformation at different tire contact pressure.</p>
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12 pages, 1858 KiB  
Review
A Review of the Applications of OCT for Analysing Pharmaceutical Film Coatings
by Hungyen Lin, Zijian Zhang, Daniel Markl, J. Axel Zeitler and Yaochun Shen
Appl. Sci. 2018, 8(12), 2700; https://doi.org/10.3390/app8122700 - 19 Dec 2018
Cited by 33 | Viewed by 6827
Abstract
Optical coherence tomography (OCT) has recently attracted a lot of interest in the pharmaceutical manufacturing industry as a fast, contactless and non-destructive modality for quantifying thin film coatings on pharmaceutical dosage forms, which cannot be resolved easily with other techniques. In this topical [...] Read more.
Optical coherence tomography (OCT) has recently attracted a lot of interest in the pharmaceutical manufacturing industry as a fast, contactless and non-destructive modality for quantifying thin film coatings on pharmaceutical dosage forms, which cannot be resolved easily with other techniques. In this topical review, we present an overview of the research that has been performed to date, highlighting key differences between systems and outlining major challenges ahead. Full article
(This article belongs to the Special Issue Optical Coherence Tomography and its Applications)
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<p>Schematic diagram of a time-domain optical coherence tomography (TD-OCT) system. L—low-coherence light source; Ref—reference mirror; BS—beam-splitter.</p>
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<p>Schematic diagram of a spectral-domain optical coherence tomography SD-OCT system. L—low-coherence light source; Ref—reference mirror; BS—beam-splitter.</p>
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<p>A 100 × 100 B-scan of a biconvex-shaped pharmaceutical tablet with the tablet central region annotated by the dashed line (<b>left</b>) and the respective A-scan with the air-coating and coating–core interfaces annotated by the respective arrows (<b>right</b>). Adapted from [<a href="#B23-applsci-08-02700" class="html-bibr">23</a>].</p>
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<p>B-scan and <span class="html-italic">en face</span> images captured with full-field optical coherence tomography (FF-OCT) for characterising the micro-structure of a double-layer coated pellet. The FF-OCT B-scan in (<b>a</b>) and the <span class="html-italic">en face</span> results in (<b>b</b>) are validated by XμCT in (<b>b</b>,<b>d</b>) on the same pellet sample. Two dashed red lines in (<b>a</b>) indicate the upper and lower interfaces of the inner coating layer. Adapted from [<a href="#B35-applsci-08-02700" class="html-bibr">35</a>].</p>
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<p>B-scans captured with SD-OCT at an in-line setting for a lab-scale tablet coating process showing the measured tablets with highlighted coating interfaces using segmentation algorithm as part of automated thickness quantification. The title of each OCT image indicates the mean calculated from the detected coating layer interfaces. The <math display="inline"><semantics> <mi>x</mi> </semantics></math>-axis represents the width of the tablet measured and depends on the tablet movement speed. Adapted from [<a href="#B24-applsci-08-02700" class="html-bibr">24</a>].</p>
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13 pages, 1213 KiB  
Article
Analytical Method for Measurement of Tobacco-Specific Nitrosamines in E-Cigarette Liquid and Aerosol
by Yoon-Seo Lee, Ki-Hyun Kim, Sang Soo Lee, Richard J. C. Brown and Sang-Hee Jo
Appl. Sci. 2018, 8(12), 2699; https://doi.org/10.3390/app8122699 - 19 Dec 2018
Cited by 10 | Viewed by 4778
Abstract
An experimental method was developed and validated for the collection and analysis of tobacco-specific nitrosamines (TSNAs) that are present in electronic cigarette (EC) liquid or are released from aerosol samples using a liquid chromatography-tandem mass spectrometry (LC-MS/MS) system. As part of this study, [...] Read more.
An experimental method was developed and validated for the collection and analysis of tobacco-specific nitrosamines (TSNAs) that are present in electronic cigarette (EC) liquid or are released from aerosol samples using a liquid chromatography-tandem mass spectrometry (LC-MS/MS) system. As part of this study, the relative recovery of four target TSNAs was assessed by spiking standards in a mixture of propylene glycol and vegetable glycerin. Recovery was assessed against two major variables: (1) the chemical media (solution) selected for sample dilution (acetonitrile [ACN] vs. ammonium acetate [AA]) and (2) the type of sampling filter used (Cambridge filter pad [CFP] vs. quartz wool [QW] tube). The average recovery of TSNAs in terms of variable 1 was 134 ± 22.1% for ACN and 92.6 ± 8.27% for AA. The average recovery in terms of variable 2 was 83.4 ± 7.33% for QW and 58.5 ± 12.9% for CFP. Based on these conditions, the detection limits of N′-nitrosonornicotine (NNN), 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK), N′-nitrosoanatabine (NAT), and N′-nitrosoanabasine (NAB) were calculated as 4.40, 4.47, 3.71, and 3.28 ng mL−1, respectively. The concentration of TSNAs in liquid and aerosol samples of six commercial EC solutions was measured as below the detection limit. Full article
(This article belongs to the Section Chemical and Molecular Sciences)
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<p>Chromatograms of TSNA for the 10 ng mL<sup>−1</sup> of working standards by liquid chromatography-tandem mass spectrometry (LC-MS/MS) (<b>a</b>) extracted ion chromatogram (EIC) of quantifier ions for each target compound and (<b>b</b>) Chromatogram in Total ion current (TIC) mode with both quantifier and qualifier ions.</p>
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<p>Chromatograms of TSNA for the 10 ng mL<sup>−1</sup> of working standards by liquid chromatography-tandem mass spectrometry (LC-MS/MS) (<b>a</b>) extracted ion chromatogram (EIC) of quantifier ions for each target compound and (<b>b</b>) Chromatogram in Total ion current (TIC) mode with both quantifier and qualifier ions.</p>
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<p>Comparison of relative recovery (RR) for the four TSNAs between quartz wool (QW) filter and Cambridge filter pad (CFP). RR (%) = (Concentration of spiked sample detected by LC-MS/MS system)/(Theoretical concentration of spiked sample) × 100.</p>
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19 pages, 9360 KiB  
Article
A Full-Process Numerical Analyzing Method of Low-Velocity Impact Damage and Residual Strength for Stitched Composites
by Hongjian Zhang, Mingming Wang, Weidong Wen, Ying Xu, Haitao Cui and Jinbo Chen
Appl. Sci. 2018, 8(12), 2698; https://doi.org/10.3390/app8122698 - 19 Dec 2018
Cited by 1 | Viewed by 3327
Abstract
The failure and residual strength after low-velocity impact of stitched composites are very important in their service and maintenance phases. In order to capture the failure and residual strength more accurately, a full-process numerical analyzing method was developed in this paper. The full-process [...] Read more.
The failure and residual strength after low-velocity impact of stitched composites are very important in their service and maintenance phases. In order to capture the failure and residual strength more accurately, a full-process numerical analyzing method was developed in this paper. The full-process numerical analyzing method includes two parts: (1) Part 1 is the progressive low-velocity impact damage prediction method for stitched composites; (2) Part 2 is the progressive residual strength prediction method by introducing all types of damage that are caused by the low-velocity impact as the analysis presuppositions. Subsequently, the failure and residual strength of G0827/QY9512 stitched composites were simulated by the full-process numerical analyzing method. When compared with experiments, it is found that: (1) the maximum error of low-velocity impact damage areas was 17.8%, and their damage modes were similar; (2) the maximum error of residual strength was 8.9%. At last, the influence rules of stitched density and stitching thread thickness were analyzed. The simulation results showed that, if there is no suture breakage failure, stitched density affects the mechanical properties of the stitched composites, while stitching thread thickness has little effect on it; otherwise, both factors have a significant effect on the mechanical properties. Full article
(This article belongs to the Special Issue Damage Inspection of Composite Structures)
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<p>Transient analysis model of laminate and punch.</p>
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<p>Relationship between two coordinate systems.</p>
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<p>Simulation procedures of full-process analyzing method.</p>
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<p>Finite element model of the stitched laminate.</p>
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<p>Progressive damage process of the laminate and the damage projection diagrams obtained by experiment [<a href="#B18-applsci-08-02698" class="html-bibr">18</a>] and simulation after the impact of 16.8 J. Note: (1) DF: delamination failure; (2) FTF: fiber tensile failure; (3) FCF: fiber compressive failure; (4) MTF: matrix tensile failure; (5) MCF: matrix compressive failure; (6) FMSF: fiber-matrix shear failure; and, (7) SBF: suture breakage failure.</p>
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<p>Final failure of each layer after the impact of 16.8 J.</p>
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<p>Final failure of each interface after the impact of 16.8 J.</p>
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<p>Finite element model of the stitched laminate.</p>
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<p>Progressive damage process of the laminate after the impact of 16.8 J.</p>
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<p>Full-process of low-velocity impact damage and residual strength for laminates with different stitched densities.</p>
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<p>Distributions of different damage modes with different stitched densities after the low-velocity impact damage: (<b>a</b>) MTF; (<b>b</b>) FCF; (<b>c</b>) FTF; and, (<b>d</b>) DF.</p>
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<p>The predicted residual strengths with different stitched densities.</p>
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<p>SBF of the laminates after the impact of 10 J.</p>
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<p>Progressive damage processes of the laminates after the impact of 10 J.</p>
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<p>Distribution of different damage modes with different stitching thread thicknesses after the low-velocity impact damage: (<b>a</b>) MTF; (<b>b</b>) FCF; (<b>c</b>) FTF; and, (<b>d</b>) DF.</p>
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<p>The effect of stitching thread thickness on residual strength under the impact of 10 J.</p>
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11 pages, 885 KiB  
Article
Influence of Piano Key Vibration Level on Players’ Perception and Performance in Piano Playing
by Matthias Flückiger, Tobias Grosshauser and Gerhard Tröster
Appl. Sci. 2018, 8(12), 2697; https://doi.org/10.3390/app8122697 - 19 Dec 2018
Cited by 5 | Viewed by 4403
Abstract
In this study, the influence of piano key vibration levels on players’ personal judgment of the instrument quality and on the dynamics and timing of the players’ performance of a music piece excerpt is examined. In an experiment four vibration levels were presented [...] Read more.
In this study, the influence of piano key vibration levels on players’ personal judgment of the instrument quality and on the dynamics and timing of the players’ performance of a music piece excerpt is examined. In an experiment four vibration levels were presented to eleven pianists playing on a digital grand piano with grand piano-like key action. By evaluating the players’ judgment of the instrument quality, strong integration effects of auditory and tactile information were observed. Differences in the sound of the instrument were perceived by the players, when the vibration level in the keys was changed and the results indicate a sound-dependent optimum of the vibration levels. By analyzing the influence of the vibration levels on the timing and dynamics accuracy of the pianists’ musical performances, we could not observe systematic differences that depend on the vibration level. Full article
(This article belongs to the Special Issue Modelling, Simulation and Data Analysis in Acoustical Problems)
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<p>(<b>a</b>) Block diagram of the vibrotactile feedback rendering system to generate the key vibrations; the mono audio output of the N3X was filtered and attenuated with a DSP. Thereafter the signal was power amplified to drive the transducer of the built-in vibrotactile rendering system of the N3X. (<b>b</b>) Comparison of the tonal part of the vibration levels <math display="inline"><semantics> <mrow> <msub> <mi>V</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>V</mi> <mn>2</mn> </msub> <mo>,</mo> <msub> <mi>V</mi> <mn>3</mn> </msub> </mrow> </semantics></math> to vibration levels of four acoustic concert grand pianos. The comparison is based on vibrometer measurements of <span class="html-italic">forte</span> keystrokes [<a href="#B7-applsci-08-02697" class="html-bibr">7</a>]. (<math display="inline"><semantics> <msub> <mi>V</mi> <mn>0</mn> </msub> </semantics></math> is not shown because it corresponds to no vibrations.)</p>
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<p>Analysis of the preference ratings (“better” (1), “worse” (−1), or “similar” (0)) of the vibration levels across all parts and all participants. The ratings are based on direct comparisons between all pairs of levels. The ratings are relative. For example, a positive value for <math display="inline"><semantics> <msub> <mi>V</mi> <mn>0</mn> </msub> </semantics></math>:<math display="inline"><semantics> <msub> <mi>V</mi> <mn>1</mn> </msub> </semantics></math> indicates a preference of <math display="inline"><semantics> <msub> <mi>V</mi> <mn>0</mn> </msub> </semantics></math> over <math display="inline"><semantics> <msub> <mi>V</mi> <mn>1</mn> </msub> </semantics></math> and a negative value a preference of <math display="inline"><semantics> <msub> <mi>V</mi> <mn>1</mn> </msub> </semantics></math> over <math display="inline"><semantics> <msub> <mi>V</mi> <mn>0</mn> </msub> </semantics></math>. The shaded area marks the standard deviation of the ratings.</p>
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<p>“Amount of adaption” to the feedback levels <math display="inline"><semantics> <msub> <mi>V</mi> <mi>n</mi> </msub> </semantics></math> (<math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mo>{</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>3</mn> <mo>}</mo> </mrow> </semantics></math>) relative to the no-vibration condition <math display="inline"><semantics> <msub> <mi>V</mi> <mn>0</mn> </msub> </semantics></math> across all pianists <math display="inline"><semantics> <msub> <mi>P</mi> <mi>k</mi> </msub> </semantics></math> and for both performance parameters. The differences in the amount of adaption for all vibration levels are not significant for both parameters. The line in the center of the box-plot marks the median, the box extends from the first to the third quartile, and the whiskers mark the value range.</p>
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<p>Estimated repeatabilities per participant <math display="inline"><semantics> <msub> <mi>P</mi> <mi>k</mi> </msub> </semantics></math> and vibration level <math display="inline"><semantics> <msub> <mi>V</mi> <mo>ℓ</mo> </msub> </semantics></math> for both performance parameters. No consistent tendency occurred among the pianists, but the vibration levels had a significant influence (marked with *) on repeatability for a majority of the participants.</p>
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<p>Music sheet of the study. The excerpt was taken from Klage by composer Gretchaninov [<a href="#B11-applsci-08-02697" class="html-bibr">11</a>]. The excerpt was edited to cover a broad dynamic range and also to include accents.</p>
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14 pages, 6743 KiB  
Article
Impregnation of Wood with Microencapsulated Bio-Based Phase Change Materials for High Thermal Mass Engineered Wood Flooring
by Damien Mathis, Pierre Blanchet, Véronic Landry and Philippe Lagière
Appl. Sci. 2018, 8(12), 2696; https://doi.org/10.3390/app8122696 - 19 Dec 2018
Cited by 42 | Viewed by 6543
Abstract
Wood is a porous material that can be impregnated and have enhanced properties. Two species of hardwood, red oak (Quercus rubra L.) and sugar maple (Acer saccharum Marsh.), were impregnated in a reactor with a microencapsulated phase change material. The objective [...] Read more.
Wood is a porous material that can be impregnated and have enhanced properties. Two species of hardwood, red oak (Quercus rubra L.) and sugar maple (Acer saccharum Marsh.), were impregnated in a reactor with a microencapsulated phase change material. The objective was to enhance the thermal mass of wood boards used as surface layers for engineered wood flooring manufacturing. Preliminary experiments were conducted on small samples in order to define suitable impregnation parameters, based on the Bethell cycle. Thin wood boards were impregnated with a microencapsulated phase change material dispersed into distilled water. Several cycles of pressure were applied. Heating storage of the impregnated wood boards was determined using a dynamic heat flow meter apparatus method. A latent heat storage of 7.6 J/g over 3 °C was measured for impregnated red oak samples. This corresponds to a heat storage enhancement of 77.0%. Sugar maple was found to be harder to impregnate and thus his thermal enhancement was lower. Impregnated samples were observed by reflective optical microscopy. Microcapsules were found mainly in the large vessels of red oak, forming aggregates. Pull-off tests were conducted on varnished samples to assess the influence of an impregnation on varnish adhesion and no significant influence was revealed. Engineered wood flooring manufactured with impregnated boards such as characterized in this study could store solar energy and thus improve buildings energy efficiency. Although wood is a material with a low-conductivity, the thermal exchange between the PCM and the building air could be good enough as the microcapsules are positioned in the surface layer. Furthermore, flooring is an area with frequent sunrays exposure. Such high thermal mass EWF could lead to energy savings and to an enhancement of occupant’s thermal comfort. This study aimed to characterize the potential of impregnation with MPCM of two wood species in order to make high thermal mass EWF. Full article
(This article belongs to the Special Issue Advanced Applications of Phase Change Materials)
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Graphical abstract
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<p>Surface for thermal testing, constituted by three glued wood boards.</p>
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<p>Wood morphology before impregnation. (<b>a</b>) Red Oak; (<b>b</b>) Sugar maple.</p>
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<p>Observation of microcapsules by optical microscopy 100×.</p>
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<p>Distribution of Nextek29<sup>©</sup> microcapsules size.</p>
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<p>Weight enhancement for several microcapsules ratios.</p>
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<p>Weight enhancement of small samples for several pressures.</p>
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<p>Red oak impregnated with MPCM.</p>
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<p>Impregnated red oak microscopy. (<b>a</b>) Small late wood vessels are mostly empty; (<b>b</b>) Some small vessels are filled with a few microcapsules.</p>
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<p>Impregnated sugar maple microscopy. (<b>a</b>) Sugar maple vessels containing microcapsules. (<b>b</b>) Zoom on the microcapsules.</p>
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<p>Heat storage capacity of red oak, melting cycle.</p>
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<p>Comparison of impregnated red oak melting and solidification.</p>
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<p>Heat storage capacity of sugar maple, melting cycle.</p>
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<p>Average coating adhesive strengths for different samples.</p>
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19 pages, 6974 KiB  
Article
Comparative Analysis of Current Control Techniques to Support Virtual Inertia Applications
by Ujjwol Tamrakar, Dipesh Shrestha, Naresh Malla, Zhen Ni, Timothy M. Hansen, Indraman Tamrakar and Reinaldo Tonkoski
Appl. Sci. 2018, 8(12), 2695; https://doi.org/10.3390/app8122695 - 19 Dec 2018
Cited by 20 | Viewed by 3758
Abstract
The rapid transition towards an inverter-dominated power system has reduced the inertial response capability of modern power systems. As a solution, inverters are equipped with control strategies, which can emulate inertia by exchanging power with the grid based on frequency changes. This paper [...] Read more.
The rapid transition towards an inverter-dominated power system has reduced the inertial response capability of modern power systems. As a solution, inverters are equipped with control strategies, which can emulate inertia by exchanging power with the grid based on frequency changes. This paper discusses the various current control techniques for application in these systems, known as virtual inertia systems. Some classic control techniques like the proportional-integral, the proportional-resonant, and the hysteresis control are presented first, followed by the design and discussion of two more advanced control techniques based on model prediction and machine learning, respectively. MATLAB/Simulink-based simulations are performed, and results are presented to compare these control techniques in terms of harmonic performance, switching frequency, and transient response. Full article
(This article belongs to the Section Energy Science and Technology)
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<p>Operating principle of a virtual inertia emulation system.</p>
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<p>Schematic diagram of the grid-connected inverter benchmark and PI (proportional-integral) Type-2 based current controller; PLL: Phase Locked Loop; PWM: Pulse Width Modulation; “*” represents the reference values of the signals.</p>
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<p>Schematic diagram of a PR-based current controller; PR: proportional-resonant; PWM: pulse width modulation; “*” represents the reference values of the signals.</p>
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<p>Hysteresis current controller. (<b>a</b>) Schematic diagram. (<b>b</b>) gate signal generation.</p>
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<p>Model predictive controller. (<b>a</b>) Schematic diagram. (<b>b</b>) detailed structure.</p>
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<p>Supplementary ADP (adaptive dynamic programming) controller. (<b>a</b>) Schematic diagram. (<b>b</b>) detailed structure showing the action and critic network.</p>
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<p>Frequency response of the plant, the designed controller, and the loop transfer function (LTF) of the overall system. (<b>a</b>) PI Type-2 controller. (<b>b</b>) PR controller.</p>
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<p>Simulation results with the PI controller. (<b>a</b>) Three-phase output current of the inverter. (<b>b</b>) Active and reactive power output.</p>
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<p>Simulation results with PR controller. (<b>a</b>) Three-phase output current of inverter. (<b>b</b>) Active and reactive power output.</p>
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<p>Simulation results with hysteresis controller. (<b>a</b>) Three-phase output current of inverter. (<b>b</b>) Active and reactive power output.</p>
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<p>Simulation results with MPC. (<b>a</b>) Three-phase output current of inverter. (<b>b</b>) Active and reactive power output.</p>
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<p>Simulation results with the ADP controller. (<b>a</b>) Three-phase output current of inverter. (<b>b</b>) Active and reactive power output.</p>
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<p>Comparison of <span class="html-italic">d</span>-axis and <span class="html-italic">q</span>-axis current responses. (<b>a</b>) PI controller. (<b>b</b>) PR controller. (<b>c</b>) Hysteresis controller. (<b>d</b>) MPC. (<b>e</b>) Supplementary ADP controller; O.S.: Overshoot.</p>
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<p>Comparison of the harmonic spectrum of the output current. (<b>a</b>) PI controller. (<b>b</b>) PR controller. (<b>c</b>) Hysteresis controller. (<b>d</b>) MPC. (<b>e</b>) Supplementary ADP controller. THD: Total Harmonic Distortion.</p>
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15 pages, 5107 KiB  
Article
Management of Waste Streams from Dairy Manufacturing Operations Using Membrane Filtration and Dissolved Air Flotation
by Subbiah Nagappan, David M. Phinney and Dennis R. Heldman
Appl. Sci. 2018, 8(12), 2694; https://doi.org/10.3390/app8122694 - 19 Dec 2018
Cited by 12 | Viewed by 4379
Abstract
Membrane filtration can provide a significant role in the management of waste streams from food manufacturing operations. The objective of this research was to evaluate the reductions in the organic content of waste streams accomplished when using membrane filtration. Reductions in Chemical Oxygen [...] Read more.
Membrane filtration can provide a significant role in the management of waste streams from food manufacturing operations. The objective of this research was to evaluate the reductions in the organic content of waste streams accomplished when using membrane filtration. Reductions in Chemical Oxygen Demand (COD) by membrane filtration were compared to a Dissolved Air Floatation (DAF) system. Membranes with six different pore sizes (200, 20, 8, 4, 0.083, and 0.058 kDa) were evaluated. In addition, the various membrane treatments were applied after the DAF as an additional level of comparison. The DAF treatment provided 75.15 ± 3.95% reduction in COD, and the reduction in COD improved from 85% to 99% as the membrane pore size decreased. When all membranes were used after a DAF pre-treatment, a reduction in COD to less than 1200 ppm in the permeate stream was achieved. These reductions were independent of the COD in the feed stream. The membrane fouling rates were evaluated for the membranes with the four largest pore-sizes membranes. The membranes with 20 kDa pore-size had the lowest fouling rates during extended fouling-rate studies. Full article
(This article belongs to the Special Issue Membrane Separation Techniques – Optimization and Application)
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<p>Process flow of current wastewater treatment method.</p>
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<p>Membrane housing with six slots for 1 foot, ½ inch diameter tubular membranes.</p>
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<p>Process flow for membrane filtration experiment. a—Baffler; b—Temperature sensor; c—Pressure sensor.</p>
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<p>Process flow for post-DAF membrane filtration experiment. a—Baffler; b—Temperature sensor; c—Pressure sensor.</p>
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<p>Chemical Oxygen Demand (COD) of the feed and permeate stream of different membranes in (<b>A</b>) membrane filtration and (<b>B</b>) post-DAF membrane filtration experiment.</p>
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<p>Percent reduction in COD across various treatment methods.</p>
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<p>Permeate fluxes (L/(m<sup>2</sup>h)) of different membranes in both experiments—membrane filtration and post-DAF membrane filtration.</p>
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<p>Exponential fouling model on 200 kDa and 20 kDa in membrane filtration experiment at 6.89 bar.</p>
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<p>Exponential fouling model on 8 kDa and 4 kDa in membrane filtration experiment at 24.13 bar.</p>
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<p>Total solids in the feed and the output/permeate of different treatments: A—DAF; B—membrane filtration; C—post-DAF membrane filtration.</p>
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<p>Protein concentration in the feed and the output/permeate of different treatments: A—DAF; B—membrane filtration; C—post-DAF membrane filtration.</p>
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19 pages, 5503 KiB  
Article
Estimating Underwater Light Regime under Spatially Heterogeneous Sea Ice in the Arctic
by Philippe Massicotte, Guislain Bécu, Simon Lambert-Girard, Edouard Leymarie and Marcel Babin
Appl. Sci. 2018, 8(12), 2693; https://doi.org/10.3390/app8122693 - 19 Dec 2018
Cited by 16 | Viewed by 4053
Abstract
The vertical diffuse attenuation coefficient for downward plane irradiance ( K d ) is an apparent optical property commonly used in primary production models to propagate incident solar radiation in the water column. In open water, estimating K d is relatively straightforward when [...] Read more.
The vertical diffuse attenuation coefficient for downward plane irradiance ( K d ) is an apparent optical property commonly used in primary production models to propagate incident solar radiation in the water column. In open water, estimating K d is relatively straightforward when a vertical profile of measurements of downward irradiance, E d , is available. In the Arctic, the ice pack is characterized by a complex mosaic composed of sea ice with snow, ridges, melt ponds, and leads. Due to the resulting spatially heterogeneous light field in the top meters of the water column, it is difficult to measure at single-point locations meaningful K d values that allow predicting average irradiance at any depth. The main objective of this work is to propose a new method to estimate average irradiance over large spatially heterogeneous area as it would be seen by drifting phytoplankton. Using both in situ data and 3D Monte Carlo numerical simulations of radiative transfer, we show that (1) the large-area average vertical profile of downward irradiance, E d ¯ ( z ) , under heterogeneous sea ice cover can be represented by a single-term exponential function and (2) the vertical attenuation coefficient for upward radiance ( K L u ), which is up to two times less influenced by a heterogeneous incident light field than K d in the vicinity of a melt pond, can be used as a proxy to estimate E d ¯ ( z ) in the water column. Full article
(This article belongs to the Special Issue Outstanding Topics in Ocean Optics)
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Figure 1
<p>Spatial configuration used for the 3D Monte Carlo numerical simulations. (<b>A</b>) Surface view showing the percentage of the total area covered by the melt pond over the areas described by the black lines. For each of these areas, light profiles were averaged (see Figure 7). For visualization purpose, lines of the horizontal sampling distances from the centre of the melt pond have been plotted only at 5 m intervals. (<b>B</b>) 2D side view showing the 3D volume for which simulated data were extracted and how photon detectors were placed in the water column. Orange arrows indicate incident light sources.</p>
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<p>Comparison of the under-ice measured downward radiance distribution (the average cosine is ≈0.61, [<a href="#B18-applsci-08-02693" class="html-bibr">18</a>]) and the angular distribution of light-emitting source used in the paper.</p>
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<p>Examples of in situ downward irradiance (<math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mi>d</mi> </msub> <mrow> <mo>(</mo> <mi>z</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>) and upward radiance (<math display="inline"><semantics> <mrow> <msub> <mi>L</mi> <mi>u</mi> </msub> <mrow> <mo>(</mo> <mi>z</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>) profiles measured under-ice on 20 June 2016. Note the presence of subsurface maxima in the downward irradiance profiles and the absence of subsurface maxima in the upward radiance profiles.</p>
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<p>Comparison of downward irradiance (<math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mi>d</mi> </msub> <mrow> <mo>(</mo> <mi>z</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>) and upward radiance (<math display="inline"><semantics> <mrow> <msub> <mi>L</mi> <mi>u</mi> </msub> <mrow> <mo>(</mo> <mi>z</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>) for one example light profile measured under-ice. Profiles were normalized to the measured radiometric value at 10 m depth (under the subsurface light maximum) in order to emphasize the similar shape between <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mi>d</mi> </msub> <mrow> <mo>(</mo> <mi>z</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>L</mi> <mi>u</mi> </msub> <mrow> <mo>(</mo> <mi>z</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>.</p>
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<p>Scatter plots showing the relationships between the measured <math display="inline"><semantics> <msub> <mi>K</mi> <mi>d</mi> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>K</mi> <mrow> <mi>L</mi> <mi>u</mi> </mrow> </msub> </semantics></math> in the spectral range between 400 and 580 nm at different depths (numbers in gray boxes). Red lines represent the regression lines of the fitted linear models. Regression equations and determination coefficients (<math display="inline"><semantics> <msup> <mi>R</mi> <mn>2</mn> </msup> </semantics></math>) are also provided in each plot. Dashed lines are the 1:1 lines.</p>
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<p>Cross-sections of simulated downward irradiance and upward radiance fields under a melt pond with a 5 m radius. The logarithm of the normalized number of photons has been used to create the scale for visualization. The normalization has been done using the values modelled at a 0.5 m depth and at a horizontal distance of 50 m from the centre of the melt pond.</p>
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<p>Simulated reference downward irradiance and upward radiance profiles (<math display="inline"><semantics> <mrow> <mover> <msub> <mi>E</mi> <mi>d</mi> </msub> <mo>¯</mo> </mover> <mrow> <mo>(</mo> <mi>z</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mover> <msub> <mi>L</mi> <mi>u</mi> </msub> <mo>¯</mo> </mover> <mrow> <mo>(</mo> <mi>z</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> in relative units) for six different areas with varying proportions of the surface occupied by the melt pond (see <a href="#applsci-08-02693-f001" class="html-fig">Figure 1</a>). Note that none of the averaged irradiance profiles show the same subsurface light maxima as observed with in situ data (see <a href="#applsci-08-02693-f003" class="html-fig">Figure 3</a>).</p>
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<p>Simulated local downward irradiance and upward radiance profiles (expressed in relative units) at different horizontal distances from the centre of the melt pond (see <a href="#applsci-08-02693-f001" class="html-fig">Figure 1</a>) used to compute <math display="inline"><semantics> <msub> <mi>K</mi> <mi>d</mi> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>K</mi> <mrow> <mi>L</mi> <mi>u</mi> </mrow> </msub> </semantics></math>. These attenuation coefficients were used to propagate surface reference downward irradiance (<math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mi>d</mi> </msub> <mrow> <mo>(</mo> <msup> <mn>0</mn> <mo>−</mo> </msup> <mo>)</mo> </mrow> </mrow> </semantics></math>, the surface values of the lines in <a href="#applsci-08-02693-f007" class="html-fig">Figure 7</a>) through the water column.</p>
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<p>Diffuse attenuation coefficients calculated from local downward irradiance and upward radiance profiles simulated at different distances from the centre of the melt pond (see <a href="#applsci-08-02693-f008" class="html-fig">Figure 8</a>).</p>
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<p>Reference downward irradiance profiles (thick black lines, in relative units) and propagated irradiance through the water column (coloured lines, in relative units) using local values of <math display="inline"><semantics> <msub> <mi>K</mi> <mi>d</mi> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>K</mi> <mrow> <mi>L</mi> <mi>u</mi> </mrow> </msub> </semantics></math> (see <a href="#applsci-08-02693-f008" class="html-fig">Figure 8</a>). Light was propagated using the surface reference downward irradiance.</p>
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<p>Relative errors of the predictions calculated as the relative differences between the depth integral of the reference and predicted irradiance profiles.</p>
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<p>The field campaign was part of the GreenEdge project (<a href="http://www.greenedgeproject.info" target="_blank">www.greenedgeproject.info</a>) which was conducted on landfast ice southeast of the Qikiqtarjuaq Island in the Baffin Bay (67.4797 N, 63.7895 W).</p>
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<p>Examples showing the number of downward irradiance (<b>A</b>) and upward radiance (<b>B</b>) photons captured by the detectors of the Monte Carlo simulation at different depth ranges (numbers in gray boxes) as a function of the horizontal distance from the melt pond. The red lines represent the fitted Gaussian curves.</p>
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<p>Scatter plots showing the relationships between downward irradiance (<math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mi>d</mi> </msub> <mrow> <mo>(</mo> <mi>z</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>) and upward radiance (<math display="inline"><semantics> <mrow> <msub> <mi>L</mi> <mi>u</mi> </msub> <mrow> <mo>(</mo> <mi>z</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>) between 400 and 700 nm at different depths (numbers in gray boxes). Red lines represent the regression lines of the fitted linear models. Dashed lines are the 1:1 lines. Note the large deviations between the data points and the 1:1 line occurring in the orange and red regions (≥600 nm).</p>
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<p>Average determination coefficient <math display="inline"><semantics> <msup> <mi>R</mi> <mn>2</mn> </msup> </semantics></math> and standard deviation (shaded area) of the regressions between normalized (at 10 m depth) <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mi>d</mi> </msub> <mrow> <mo>(</mo> <mi>z</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>L</mi> <mi>u</mi> </msub> <mrow> <mo>(</mo> <mi>z</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> profiles between 400 and 700 nm. At each wavelength, average values were computed from the 83 COPS measurements. A sharp decrease of <math display="inline"><semantics> <msup> <mi>R</mi> <mn>2</mn> </msup> </semantics></math> occurred at wavelength longer than approximately 575 nm, suggesting a gradual decoupling between <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mi>d</mi> </msub> <mrow> <mo>(</mo> <mi>z</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>L</mi> <mi>u</mi> </msub> <mrow> <mo>(</mo> <mi>z</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> profiles at longer wavelengths, possibly due to the effect of inelastic scattering.</p>
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<p>Scatter plots showing the relationships between <math display="inline"><semantics> <msub> <mi>K</mi> <mi>d</mi> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>K</mi> <mrow> <mi>L</mi> <mi>u</mi> </mrow> </msub> </semantics></math> calculated from the downward irradiance and upward radiance profiles modelled with and without Raman scattering. The dashed lines represent the 1:1 lines.</p>
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19 pages, 12666 KiB  
Article
Advantages and Limitations to the Use of Optical Measurements to Study Sediment Properties
by Emmanuel Boss, Christopher R. Sherwood, Paul Hill and Tim Milligan
Appl. Sci. 2018, 8(12), 2692; https://doi.org/10.3390/app8122692 - 19 Dec 2018
Cited by 17 | Viewed by 7516
Abstract
Measurements of optical properties have been used for decades to study particle distributions in the ocean. They are useful for estimating suspended mass concentration as well as particle-related properties such as size, composition, packing (particle porosity or density), and settling velocity. Measurements of [...] Read more.
Measurements of optical properties have been used for decades to study particle distributions in the ocean. They are useful for estimating suspended mass concentration as well as particle-related properties such as size, composition, packing (particle porosity or density), and settling velocity. Measurements of optical properties are, however, biased, as certain particles, because of their size, composition, shape, or packing, contribute to a specific property more than others. Here, we study this issue both theoretically and practically, and we examine different optical properties collected simultaneously in a bottom boundary layer to highlight the utility of such measurements. We show that the biases we are likely to encounter using different optical properties can aid our studies of suspended sediment. In particular, we investigate inferences of settling velocity from vertical profiles of optical measurements, finding that the effects of aggregation dynamics can seldom be ignored. Full article
(This article belongs to the Special Issue Outstanding Topics in Ocean Optics)
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Figure 1
<p>Top panels: volume-specific beam attenuation (<span class="html-italic">α<sub>v</sub></span> for solid particles (left) and aggregates (right) as function of (2<span class="html-italic">πD</span>)/λ<sub>water</sub> (<span class="html-italic">n</span> − 1) where <span class="html-italic">D</span> is diameter, <span class="html-italic">λ<sub>water</sub></span> the wavelength in water and <span class="html-italic">n</span> the index of refraction (which increases between organic and inorganic particles). For aggregates, <span class="html-italic">n<sub>aggregate</sub></span> = 1 + <span class="html-italic">F</span>(<span class="html-italic">n</span> − 1), where <span class="html-italic">F</span> is the solid fraction and <span class="html-italic">n</span> the index of refraction of the particles that comprise the aggregate. In all cases, the volume used to compute <span class="html-italic">α<sub>v</sub></span> is that of the solid fraction. Bottom panels: same as on top but plotted as function of particle diameter. In all the computations <span class="html-italic">λ<sub>air</sub></span> = 660 nm and <span class="html-italic">n</span>’ = 0.0001, where <span class="html-italic">n</span>’ is the imaginary part of the index of refraction, representing absorption. For aggregates we use <span class="html-italic">n</span> = 1.15 (solid) and <span class="html-italic">n</span> = 1.05 (dashed) typical of inorganic and organic materials, respectively.</p>
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<p>Time series of conditions at the 12-m Martha’s Vineyard Coastal Observatory (MVCO) site during the Optics and Acoustics and Stress In Situ (OASIS) deployment in 2011: beam attenuation at 650 nm measured at 1 m above the bottom (gray), tidal current shear velocity sign(<span class="html-italic">u</span>)<span class="html-italic">u</span><sub>∗c</sub> (blue), and combined wave-current shear velocity <span class="html-italic">u</span><sub>∗cw</sub> (purple). Notice labels describing specific periods.</p>
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<p>Profiles of optical parameters at the MVCO site on day 261.1 when waves from Hurricane Maria were coming to shore (see <a href="#applsci-08-02692-f002" class="html-fig">Figure 2</a>). (<b>a</b>) Beam attenuation (<span class="html-italic">c<sub>p</sub></span>(650), black), particulate backscattering (<span class="html-italic">b<sub>bp</sub></span>(650), red), and LISST attenuation (gray). (<b>b</b>) Deviation from the Rouse-profile fits for the same parameters (i.e., observed minus fit). (<b>c</b>) Power-law exponent of <span class="html-italic">c<sub>p</sub></span>(650) (black) and <span class="html-italic">b<sub>bp</sub></span>(650) (red). (<b>d</b>) Sauter diameter (red) and inverse particle density (black). (<b>e</b>) Ratio of chlorophyll divided by <span class="html-italic">c<sub>p</sub></span>(650) (black) and backscattering ratio (red). (<b>f</b>) Spectra of LISST volume concentration as a function of size at nine elevations. In panels (<b>a</b>, <b>c</b>, <b>d</b>, and <b>e</b>), standard deviation about the mean values for three consecutive profiles (60 min) are shown with crosses. Dashed lines in panel (<b>a</b>) are log-log (Rouse) fits to the data. In panel (<b>f</b>), the elevations for each spectrum are indicated by black lines, and the gray vertical scale indicates 10 μL/L. Numbers in the box denote settling velocities based on Rouse fits to backscattering, AC-9 particulate attenuation at 660 nm, and LISST attenuation at 670 nm.</p>
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<p>Profiles of optical parameters at MVCO on day 268.62 during spring tides and moderate waves. Panels are as described in <a href="#applsci-08-02692-f003" class="html-fig">Figure 3</a>.</p>
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<p>Profiles of optical parameters at MVCO on day 275.20 during the passage of Hurricane Ophelia. Panels are as described in <a href="#applsci-08-02692-f003" class="html-fig">Figure 3</a>.</p>
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<p>Profiles of optical parameters at MVCO on day 281.93 during calm conditions. Panels are as described in <a href="#applsci-08-02692-f003" class="html-fig">Figure 3</a>.</p>
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19 pages, 2185 KiB  
Article
Remote Sensing of Coral Reefs: Uncertainty in the Detection of Benthic Cover, Depth, and Water Constituents Imposed by Sensor Noise
by Steven G. Ackleson, Wesley J. Moses and Marcos J. Montes
Appl. Sci. 2018, 8(12), 2691; https://doi.org/10.3390/app8122691 - 19 Dec 2018
Cited by 5 | Viewed by 3099
Abstract
Coral reefs are biologically diverse and economically important ecosystems that are on the decline worldwide in response to direct human impacts and climate change. Ocean color remote sensing has proven to be an important tool in coral reef research and monitoring. Remote sensing [...] Read more.
Coral reefs are biologically diverse and economically important ecosystems that are on the decline worldwide in response to direct human impacts and climate change. Ocean color remote sensing has proven to be an important tool in coral reef research and monitoring. Remote sensing data quality is driven by factors related to sensor design and environmental variability. This work explored the impact of sensor noise, defined as the signal to noise ratio (SNR), on the detection uncertainty of key coral reef ecological properties (bottom depth, benthic cover, and water quality) in the absence of environmental uncertainties. A radiative transfer model for a shallow reef environment was developed and Monte Carlo methods were employed to identify the range in environmental conditions that are spectrally indistinguishable from true conditions as a function of SNR. The spectrally averaged difference between remotely sensed radiance relative to sensor noise, ε, was used to quantify uncertainty in bottom depth, the fraction of benthic cover by coral, algae, and uncolonized sand, and the concentration of water constituents defined as chlorophyll, dissolved organic matter, and suspended calcite particles. Parameter uncertainty was found to increase with sensor noise (decreasing SNR) but the impact was non-linear. The rate of change in uncertainty per incremental change in SNR was greatest for SNR < 500 and increasing SNR further to 1000 resulted in only modest improvements. Parameter uncertainty was complicated by the bottom depth and benthic cover. Benthic cover uncertainty increased with bottom depth, but water constituent uncertainty changed inversely with bottom depth. Furthermore, water constituent uncertainty was impacted by the type of constituent material in relation to the type of benthic cover. Uncertainty associated with chlorophyll concentration and dissolved organic matter increased when the benthic cover was coral and/or benthic algae while uncertainty in the concentration of suspended calcite increased when the benthic cover was uncolonized sand. While the definition of an optimal SNR is subject to user needs, we propose that SNR of approximately 500 (relative to 5% Earth surface reflectance and a clear maritime atmosphere) is a reasonable engineering goal for a future satellite sensor to support research and management activities directed at coral reef ecology and, more generally, shallow aquatic ecosystems. Full article
(This article belongs to the Special Issue Outstanding Topics in Ocean Optics)
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Figure 1
<p>Computation flow chart. SNR, signal to noise ratio.</p>
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<p>Benthic reflectance spectra used in model computations.</p>
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<p>Average spectral difference parameter, <span class="html-italic">ε</span>, as a function of depth, where <span class="html-italic">L</span>′<span class="html-italic"><sub>sat</sub></span> representing shallow reef conditions is compared with optically deep water. The water column is relatively clear (<span class="html-italic">C</span>′<span class="html-italic"><sub>chl</sub></span> = 0.1 mg m<sup>−3</sup>, <span class="html-italic">a</span>′<span class="html-italic"><sub>g,450</sub></span>= 0.2 m<sup>−1</sup>, and <span class="html-italic">C</span>′<span class="html-italic"><sub>cal</sub></span> = 0.3 g m<sup>−3</sup>) and the threshold of spectral separation is <span class="html-italic">ε</span> = 1.0 (gray line). Impacts of cover type (<b>A</b>) are shown for <span class="html-italic">B</span>′<span class="html-italic"><sub>c</sub></span> (solid), <span class="html-italic">B</span>′<span class="html-italic"><sub>a</sub></span> (dot-dash), and <span class="html-italic">B</span>′<span class="html-italic"><sub>s</sub></span> (dash) and the response to <span class="html-italic">SNR</span> (B) is shown for <span class="html-italic">B'<sub>c</sub></span>, where <span class="html-italic">SNR</span> = 100 (dot-dash), 500 (solid), and 1000 (dash).</p>
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<p>Uncertainty in water depth (Δ<span class="html-italic">D</span>), expressed as the difference between the upper and lower bounds of the test conditions relative to the reference condition, plotted against optical depth (<span class="html-italic">D<sub>o</sub></span>) for variable water constituent concentration (<span class="html-italic">S1</span>, upper figure) and constant constituent concentration (<span class="html-italic">S2</span>, lower figure).</p>
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<p>Uncertainty in the detection of endmember benthic cover relative to the reference condition, indicated as zero on the ordinate in each graph. <span class="html-italic">S1</span> (top row of graphs) represent variable water constituent concentration and depth across the allowable ranges. <span class="html-italic">S2</span> (middle row of graphs) represent variable depth while setting water constituent concentration constant and equal to the reference condition. <span class="html-italic">S3</span> (bottom row of graphs) represent depth and water constituent concentration constant and equal to the reference condition.</p>
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<p>Detection uncertainty in the fractional cover of Porites (<math display="inline"><semantics> <mrow> <msubsup> <mi>B</mi> <mrow> <mi>c</mi> <mo>,</mo> <mi>p</mi> </mrow> <mo>′</mo> </msubsup> </mrow> </semantics></math>) and benthic algae (<math display="inline"><semantics> <mrow> <msubsup> <mi>B</mi> <mi>a</mi> <mo>′</mo> </msubsup> </mrow> </semantics></math>) relative to the reference condition (scenario <span class="html-italic">S4</span>, <math display="inline"><semantics> <mrow> <msubsup> <mi>B</mi> <mi>c</mi> <mo>′</mo> </msubsup> </mrow> </semantics></math> = 1). The uncertainty in the fractional cover is expressed as the difference between the upper bound of the test condition and the reference condition (solid gray line). Note that uncertainty in this scenario can only be positive (overestimated), since in this scenario <math display="inline"><semantics> <mrow> <msubsup> <mi>B</mi> <mrow> <mi>c</mi> <mo>,</mo> <mi>p</mi> </mrow> <mo>′</mo> </msubsup> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msubsup> <mi>B</mi> <mi>a</mi> <mo>′</mo> </msubsup> </mrow> </semantics></math> are zero.</p>
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<p>Water constituent uncertainty (<span class="html-italic">y</span>-axis) for variable depth and benthic cover (<span class="html-italic">S1</span>) and water depth and benthic cover equal to the reference condition (<span class="html-italic">S5<sub>a</sub></span> and <span class="html-italic">S5<sub>b</sub></span>). Scenario <span class="html-italic">S5<sub>a</sub></span> represents benthic cover of 100% coral and <span class="html-italic">S5<sub>b</sub></span> represents 100% uncolonized sand. The ordinate scale represents the difference between the upper and lower boundaries of parameter uncertainty, within the ranges specified, and the reference condition, indicated as 0 in all graphs.</p>
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32 pages, 1189 KiB  
Article
Measurements of the Volume Scattering Function and the Degree of Linear Polarization of Light Scattered by Contrasting Natural Assemblages of Marine Particles
by Daniel Koestner, Dariusz Stramski and Rick A. Reynolds
Appl. Sci. 2018, 8(12), 2690; https://doi.org/10.3390/app8122690 - 19 Dec 2018
Cited by 26 | Viewed by 5407
Abstract
The light scattering properties of seawater play important roles in radiative transfer in the ocean and optically-based methods for characterizing marine suspended particles from in situ and remote sensing measurements. The recently commercialized LISST-VSF instrument is capable of providing in situ or laboratory [...] Read more.
The light scattering properties of seawater play important roles in radiative transfer in the ocean and optically-based methods for characterizing marine suspended particles from in situ and remote sensing measurements. The recently commercialized LISST-VSF instrument is capable of providing in situ or laboratory measurements of the volume scattering function, β p ( ψ ) , and the degree of linear polarization, DoLP p ( ψ ) , associated with particle scattering. These optical quantities of natural particle assemblages have not been measured routinely in past studies. To fully realize the potential of LISST-VSF measurements, we evaluated instrument performance, and developed calibration correction functions from laboratory measurements and Mie scattering calculations for standard polystyrene beads suspended in water. The correction functions were validated with independent measurements. The improved LISST-VSF protocol was applied to measurements of β p ( ψ ) and DoLP p ( ψ ) taken on 17 natural seawater samples from coastal and offshore marine environments characterized by contrasting assemblages of suspended particles. Both β p ( ψ ) and DoLP p ( ψ ) exhibited significant variations related to a broad range of composition and size distribution of particulate assemblages. For example, negative relational trends were observed between the particulate backscattering ratio derived from β p ( ψ ) and increasing proportions of organic particles or phytoplankton in the particulate assemblage. Our results also suggest a potential trend between the maximum values of DoLP p ( ψ ) and particle size metrics, such that a decrease in the maximum DoLP p ( ψ ) tends to be associated with particulate assemblages exhibiting a higher proportion of large-sized particles. Such results have the potential to advance optically-based applications that rely on an understanding of relationships between light scattering and particle properties of natural particulate assemblages. Full article
(This article belongs to the Special Issue Outstanding Topics in Ocean Optics)
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Figure 1
<p>Measurements of the particulate volume scattering function, <math display="inline"><semantics> <mrow> <msub> <mi>β</mi> <mi>p</mi> </msub> <mrow> <mo stretchy="false">(</mo> <mi>ψ</mi> <mo stretchy="false">)</mo> </mrow> </mrow> </semantics></math>, at light wavelength of 532 nm for 200 nm (<b>a</b>,<b>b</b>) and 2000 nm (<b>c</b>,<b>d</b>) diameter polystyrene beads suspended in water. The left panels depict the angular range of 1–50° with logarithmic scaling, and the right panels depict the range 50–160° with linear scaling. The expected reference value, <math display="inline"><semantics> <mrow> <msubsup> <mi>β</mi> <mi>p</mi> <mrow> <mi mathvariant="italic">REF</mi> </mrow> </msubsup> <mrow> <mo stretchy="false">(</mo> <mi>ψ</mi> <mo stretchy="false">)</mo> </mrow> </mrow> </semantics></math>, obtained from Mie scattering calculations is indicated as a dashed line. Quality-controlled but uncorrected measurements obtained with the LISST-VSF (gray lines, number of measurements <span class="html-italic">N</span> = 128) and the median value (solid black line) are shown.</p>
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<p>Comparison of measurements of the particulate beam attenuation coefficient, <math display="inline"><semantics> <mrow> <msub> <mi>c</mi> <mi>p</mi> </msub> </mrow> </semantics></math>, at 532 nm obtained with a spectrophotometer with measurements from the LISST-VSF. The comparison is depicted for suspensions of polystyrene beads of six different diameters as indicated in the legend, and the 1:1 line is plotted for reference (dotted black line). Appropriate dilution factors have been applied to account for the different particle concentrations used in measurements with each instrument. The presented values correspond to samples measured with the LISST-VSF.</p>
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<p>Measured values of the particulate volume scattering function <math display="inline"><semantics> <mrow> <msub> <mi>β</mi> <mi>p</mi> </msub> <mrow> <mo stretchy="false">(</mo> <mi>ψ</mi> <mo stretchy="false">)</mo> </mrow> </mrow> </semantics></math> obtained with the LISST-VSF after correction (circles) for scattering angles 90–150° and illustration of the results of two model relationships (Beardsley and Zaneveld [<a href="#B75-applsci-08-02690" class="html-bibr">75</a>], Zhang et al. [<a href="#B76-applsci-08-02690" class="html-bibr">76</a>]) fitted to the data. The illustrated example measurement was made on a natural sample collected from the San Diego River estuary.</p>
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<p>Correction functions, <span class="html-italic">CF</span><math display="inline"><semantics> <mrow> <mrow> <mo stretchy="false">(</mo> <mi>ψ</mi> <mo stretchy="false">)</mo> </mrow> </mrow> </semantics></math>, for the LISST-VSF measurements of particulate volume scattering function <math display="inline"><semantics> <mrow> <msubsup> <mi>β</mi> <mi>p</mi> <mrow> <mrow> <mi mathvariant="italic">LISST</mi> <mo>∗</mo> </mrow> </mrow> </msubsup> <mrow> <mo stretchy="false">(</mo> <mi>ψ</mi> <mo stretchy="false">)</mo> </mrow> </mrow> </semantics></math> over the angular range 4.96–150° determined for 100, 200, and 400 nm polystyrene bead suspensions. For each individual bead size, dashed lines represent the median values and the dotted lines indicate the 25th and 75th percentiles determined from the series of measurements. The final computed correction function <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="italic">CF</mi> </mrow> <mi>f</mi> </msub> <mrow> <mo stretchy="false">(</mo> <mi>ψ</mi> <mo stretchy="false">)</mo> </mrow> </mrow> </semantics></math> is shown in black, and includes the constant value used for the near-forward angular range from 0.09° to 4.96°.</p>
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<p>Correction functions, <math display="inline"><semantics> <mrow> <mrow> <mi mathvariant="italic">BF</mi> <mo stretchy="false">(</mo> <mi>ψ</mi> <mo stretchy="false">)</mo> </mrow> </mrow> </semantics></math>, for LISST-VSF measurements of the degree of linear polarization of light scattered by particles, <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi mathvariant="italic">DoLP</mi> </mrow> <mi>p</mi> <mrow> <mrow> <mi mathvariant="italic">LISST</mi> <mo>∗</mo> </mrow> </mrow> </msubsup> <mrow> <mo stretchy="false">(</mo> <mi>ψ</mi> <mo stretchy="false">)</mo> </mrow> </mrow> </semantics></math> over the angular range 16–150° determined for 100, 200, and 400 nm polystyrene bead suspensions. For each individual bead size, dashed lines represent the median values and the dotted lines indicate the 25th and 75th percentiles determined from the series of measurements. The final computed correction function <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="italic">BF</mi> </mrow> <mi>f</mi> </msub> <mrow> <mo stretchy="false">(</mo> <mi>ψ</mi> <mo stretchy="false">)</mo> </mrow> </mrow> </semantics></math> is shown in black.</p>
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<p>Comparison of <math display="inline"><semantics> <mrow> <msub> <mi>β</mi> <mi>p</mi> </msub> <mrow> <mo stretchy="false">(</mo> <mi>ψ</mi> <mo stretchy="false">)</mo> </mrow> </mrow> </semantics></math> measurements on suspensions of polystyrene beads of varying diameter with reference values, <math display="inline"><semantics> <mrow> <msubsup> <mi>β</mi> <mi>p</mi> <mrow> <mi mathvariant="italic">REF</mi> </mrow> </msubsup> <mrow> <mo stretchy="false">(</mo> <mi>ψ</mi> <mo stretchy="false">)</mo> </mrow> </mrow> </semantics></math>. The <math display="inline"><semantics> <mrow> <msubsup> <mi>β</mi> <mi>p</mi> <mrow> <mi mathvariant="italic">LISST</mi> </mrow> </msubsup> <mrow> <mo stretchy="false">(</mo> <mi>ψ</mi> <mo stretchy="false">)</mo> </mrow> </mrow> </semantics></math> data represent <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="italic">CF</mi> </mrow> <mi>f</mi> </msub> </mrow> </semantics></math>-corrected median values obtained from a series of measurements with the LISST-VSF. Independent measurements of <math display="inline"><semantics> <mrow> <msub> <mi>β</mi> <mi>p</mi> </msub> <mrow> <mo stretchy="false">(</mo> <mi>ψ</mi> <mo stretchy="false">)</mo> </mrow> </mrow> </semantics></math> obtained with the DAWN-EOS instrument are also shown as diamonds in panels a, b, c and e. The bead diameters are indicated in the legend.</p>
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<p>(<b>a</b>) Scatter plot of <math display="inline"><semantics> <mrow> <msubsup> <mi>β</mi> <mi>p</mi> <mrow> <mi mathvariant="italic">LISST</mi> </mrow> </msubsup> </mrow> </semantics></math> vs. <math display="inline"><semantics> <mrow> <msubsup> <mi>β</mi> <mi>p</mi> <mrow> <mi mathvariant="italic">REF</mi> </mrow> </msubsup> </mrow> </semantics></math> for polystyrene beads of varying diameters as indicated. Data obtained with the ring detectors and Roving Eyeball sensor are plotted separately, and the 1:1 line is plotted for reference (dotted black line). (<b>b</b>) Residuals expressed as percentages between <math display="inline"><semantics> <mrow> <msubsup> <mi>β</mi> <mi>p</mi> <mrow> <mi mathvariant="italic">LISST</mi> </mrow> </msubsup> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msubsup> <mi>β</mi> <mi>p</mi> <mrow> <mi mathvariant="italic">REF</mi> </mrow> </msubsup> </mrow> </semantics></math> for each bead size as a function of scattering angle.</p>
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<p>(<b>a</b>) Scatter plot comparing reference values of the particulate scattering coefficient computed over the angular range 0.09–150°, <math display="inline"><semantics> <mrow> <msubsup> <mi>b</mi> <mrow> <mrow> <mi>p</mi> <mo>,</mo> <mn>150</mn> </mrow> </mrow> <mrow> <mi mathvariant="italic">REF</mi> </mrow> </msubsup> </mrow> </semantics></math>, with values determined from the LISST-VSF, <math display="inline"><semantics> <mrow> <msubsup> <mi>b</mi> <mrow> <mrow> <mi>p</mi> <mo>,</mo> <mn>150</mn> </mrow> </mrow> <mrow> <mi mathvariant="italic">LISST</mi> </mrow> </msubsup> </mrow> </semantics></math>, before (asterisks) and after (circles) correction with <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="italic">CF</mi> </mrow> <mi>f</mi> </msub> </mrow> </semantics></math>. A type II linear regression model fit to the data is indicated by the dotted lines. (<b>b</b>) Similar to (<b>a</b>), but for the particulate backscattering coefficient computed over the range 90–150°.</p>
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<p>Similar to <a href="#applsci-08-02690-f006" class="html-fig">Figure 6</a>, but for measured and reference values of particulate degree of linear polarization <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="italic">DoLP</mi> </mrow> <mi>p</mi> </msub> </mrow> </semantics></math>. Measurements obtained with the LISST-VSF were corrected with <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="italic">BF</mi> </mrow> <mi>f</mi> </msub> </mrow> </semantics></math>.</p>
Full article ">Figure 10
<p>Similar to <a href="#applsci-08-02690-f007" class="html-fig">Figure 7</a>, but for <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="italic">DoLP</mi> </mrow> <mi>p</mi> </msub> </mrow> </semantics></math>. All data are obtained with the Roving Eyeball sensor, and the residuals between <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi mathvariant="italic">DoLP</mi> </mrow> <mi>p</mi> <mrow> <mi mathvariant="italic">LISST</mi> </mrow> </msubsup> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi mathvariant="italic">DoLP</mi> </mrow> <mi>p</mi> <mrow> <mi mathvariant="italic">REF</mi> </mrow> </msubsup> </mrow> </semantics></math> in (<b>b</b>) are expressed as absolute differences.</p>
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<p>Measurements of <math display="inline"><semantics> <mrow> <msubsup> <mi>β</mi> <mi>p</mi> <mrow> <mi mathvariant="italic">LISST</mi> </mrow> </msubsup> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi mathvariant="italic">DoLP</mi> </mrow> <mi>p</mi> <mrow> <mi mathvariant="italic">LISST</mi> </mrow> </msubsup> </mrow> </semantics></math> obtained with the LISST-VSF on natural seawater samples from the San Diego region representing (<b>a</b>,<b>b</b>) subsurface offshore waters, (<b>c</b>,<b>d</b>) SIO Pier, and (<b>e</b>,<b>f</b>) San Diego River Estuary. Solid lines represent median values while dotted lines indicate the 10<sup>th</sup> and 90<sup>th</sup> percentiles obtained from a series of measurements on each sample. Insets in (<b>a</b>,<b>c</b>,<b>d</b>) display greater detail on the near-forward scattering range.</p>
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<p>LISST-VSF measurements of (<b>a</b>,<b>b</b>) the particulate backscattering ratio, <math display="inline"><semantics> <mrow> <msubsup> <mover> <mi>b</mi> <mo>~</mo> </mover> <mi mathvariant="italic">bp</mi> <mi mathvariant="italic">LISST</mi> </msubsup> </mrow> </semantics></math>, and (<b>c</b>,<b>d</b>) the maximum value of the degree of linear polarization of scattered light, <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi mathvariant="italic">DoLP</mi> </mrow> <mrow> <mrow> <mi>p</mi> <mo>,</mo> <mi mathvariant="italic">max</mi> </mrow> </mrow> <mrow> <mi mathvariant="italic">LISST</mi> </mrow> </msubsup> </mrow> </semantics></math>, as a function of the POC/SPM or Chla/SPM ratio. The data are divided into three groups defined by the range of POC/SPM as indicated in the legend.</p>
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<p>Similar to <a href="#applsci-08-02690-f012" class="html-fig">Figure 12</a>, but with optical quantities shown as a function of the particle size metrics (<b>a</b>,<b>c</b>) <math display="inline"><semantics> <mrow> <msubsup> <mi>D</mi> <mi>V</mi> <mrow> <mn>90</mn> </mrow> </msubsup> </mrow> </semantics></math>, representing the diameter corresponding to the 90<sup>th</sup> percentile of the particle volume distribution, and (<b>b</b>,<b>d</b>) <math display="inline"><semantics> <mi>ζ</mi> </semantics></math>, the power law slope of the particle number distribution.</p>
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20 pages, 630 KiB  
Article
Assessing the Impact of a Two-Layered Spherical Geometry of Phytoplankton Cells on the Bulk Backscattering Ratio of Marine Particulate Matter
by Lucile Duforêt-Gaurier, David Dessailly, William Moutier and Hubert Loisel
Appl. Sci. 2018, 8(12), 2689; https://doi.org/10.3390/app8122689 - 19 Dec 2018
Cited by 7 | Viewed by 3696
Abstract
The bulk backscattering ratio ( b b p ˜ ) is commonly used as a descriptor of the bulk real refractive index of the particulate assemblage in natural waters. Based on numerical simulations, we analyze the impact of modeled structural heterogeneity of phytoplankton [...] Read more.
The bulk backscattering ratio ( b b p ˜ ) is commonly used as a descriptor of the bulk real refractive index of the particulate assemblage in natural waters. Based on numerical simulations, we analyze the impact of modeled structural heterogeneity of phytoplankton cells on b b p ˜ . b b p ˜ is modeled considering viruses, heterotrophic bacteria, phytoplankton, organic detritus, and minerals. Three case studies are defined according to the relative abundance of the components. Two case studies represent typical situations in open ocean, oligotrophic waters, and phytoplankton bloom. The third case study is typical of coastal waters with the presence of minerals. Phytoplankton cells are modeled by a two-layered spherical geometry representing a chloroplast surrounding the cytoplasm. The b b p ˜ values are higher when structural heterogeneity is considered because the contribution of coated spheres to light backscattering is higher than homogeneous spheres. The impact of heterogeneity is; however, strongly conditioned by the hyperbolic slope ξ of the particle size distribution. Even if the relative abundance of phytoplankton is small (<1%), b b p ˜ increases by about 58% (for ξ = 4 and for oligotrophic waters), when the heterogeneity is taken into account, in comparison with a particulate population composed only of homogeneous spheres. As expected, heterogeneity has a much smaller impact (about 12% for ξ = 4 ) on b b p ˜ in the presence of suspended minerals, whose increased light scattering overwhelms that of phytoplankton. Full article
(This article belongs to the Special Issue Outstanding Topics in Ocean Optics)
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Figure 1

Figure 1
<p>Flow chart of the integration procedure applied to the MIE and ScattnLay outputs.</p>
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<p>Composite PSD as derived from individual PSDs of the five considered particle groups for (<b>a</b>) the oligotrophic-like water body and (<b>b</b>) the phytoplankton bloom water body. N<math display="inline"><semantics> <msub> <mrow/> <mrow> <mi>T</mi> <mi>O</mi> <mi>T</mi> </mrow> </msub> </semantics></math> = 1.1262 × 10<math display="inline"><semantics> <msup> <mrow/> <mn>14</mn> </msup> </semantics></math> particles per m<math display="inline"><semantics> <msup> <mrow/> <mn>3</mn> </msup> </semantics></math> and <math display="inline"><semantics> <mi>ξ</mi> </semantics></math> = 4.</p>
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<p>Interference and resonance features observed for the scattering phase function of monodisperse particles (light green). The major low-frequency maxima and minima are called the “interference structure”. The high-frequency ripples are resonance features. The interference and resonance feature are washed out for a polydisperse assemblage of particles (dark green).</p>
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<p>Results of Lorentz-Mie calculations (DS1) of the particulate backscattering ratio <math display="inline"><semantics> <mover accent="true"> <msubsup> <mi>b</mi> <mrow> <mi>b</mi> <mi>p</mi> </mrow> <msub> <mi>θ</mi> <mi>a</mi> </msub> </msubsup> <mo>˜</mo> </mover> </semantics></math> as a function of the hyperbolic slope, <math display="inline"><semantics> <mi>ξ</mi> </semantics></math>, and different values of <math display="inline"><semantics> <msub> <mi>n</mi> <mi>r</mi> </msub> </semantics></math> and N<math display="inline"><semantics> <msub> <mrow/> <mi>θ</mi> </msub> </semantics></math>. The imaginary part of the refractive index = 0.005 as in Twardowski et al. [<a href="#B5-applsci-08-02689" class="html-bibr">5</a>]. This figure can be compared to <a href="#applsci-08-02689-f001" class="html-fig">Figure 1</a> in Twardowski et al. [<a href="#B5-applsci-08-02689" class="html-bibr">5</a>].</p>
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<p>(<b>a</b>) Particulate backscattering ratio <math display="inline"><semantics> <mover accent="true"> <msubsup> <mi>b</mi> <mrow> <mi>b</mi> <mi>p</mi> </mrow> <msub> <mi>θ</mi> <mi>a</mi> </msub> </msubsup> <mo>˜</mo> </mover> </semantics></math> as a function of the hyperbolic slope for the oligotrophic-like (red dashed line), phytoplankton bloom (green dashed line), and coastal-like (brown dashed line) water bodies as described in <a href="#sec4-applsci-08-02689" class="html-sec">Section 4</a>. Black and gray lines are for homogeneous reference cases. The gray solid line corresponds to <math display="inline"><semantics> <msub> <mi>n</mi> <mi>r</mi> </msub> </semantics></math> = 1.045, <math display="inline"><semantics> <msub> <mi>n</mi> <mi>i</mi> </msub> </semantics></math> = 9.93 × 10<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>4</mn> </mrow> </msup> </semantics></math>, the black dashed line to <math display="inline"><semantics> <msub> <mi>n</mi> <mi>r</mi> </msub> </semantics></math> = 1.1043, <math display="inline"><semantics> <msub> <mi>n</mi> <mi>i</mi> </msub> </semantics></math> = 1.36 × 10<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </semantics></math>, and the black solid line to <math display="inline"><semantics> <msub> <mi>n</mi> <mi>r</mi> </msub> </semantics></math> = 1.131, <math display="inline"><semantics> <msub> <mi>n</mi> <mi>i</mi> </msub> </semantics></math> = 1.37 × 10<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>4</mn> </mrow> </msup> </semantics></math>, respectively. Phytoplankton cells are modeled as two-layered spheres with a relative volume of the cytoplasm of 20% (%cyt-%chl = 80–20). (<b>b</b>) as in panel (<b>a</b>) but for the real refractive index. (<b>c</b>) as in panel (<b>a</b>) but for the imaginary part of the refractive index.</p>
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<p>Particulate backscattering ratio as a function of the hyperbolic slope for oligotrophic-like and phytoplankton bloom water bodies. Phytoplankton cells are modeled as two-layered spheres with a relative volume of the chloroplast of 20 % and 30 %, as indicated.</p>
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<p>Particulate backscattering ratio as a function of the hyperbolic slope for oligotrophic-like and phytoplankton bloom water bodies. Phytoplankton cells are modeled as two-layered spheres (80%–20%) or three-layered spheres (80%–18.5%–1.5%), as indicated.</p>
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<p>Contribution of the different particle groups the total bulk backscattering ratio for (<b>a</b>) oligotrophic-like, (<b>b</b>) phytoplankton bloom, and (<b>c</b>) coastal-like water bodies. The phytoplankton cells are modeled as a two-layered sphere (80%–20%).</p>
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<p>Backscattering cross sections, <math display="inline"><semantics> <msubsup> <mi>C</mi> <mrow> <mi>s</mi> <mi>c</mi> <mi>a</mi> </mrow> <mrow> <mi>b</mi> <mi>b</mi> </mrow> </msubsup> </semantics></math>, of the different particle groups. The phytoplankton cells are modeled as a two-layered sphere (80%–20%).</p>
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20 pages, 4615 KiB  
Article
Influence of Three-Dimensional Coral Structures on Hyperspectral Benthic Reflectance and Water-Leaving Reflectance
by John D. Hedley, Maryam Mirhakak, Adam Wentworth and Heidi M. Dierssen
Appl. Sci. 2018, 8(12), 2688; https://doi.org/10.3390/app8122688 - 19 Dec 2018
Cited by 13 | Viewed by 3203
Abstract
Shading and inter-reflections created by the three-dimensional coral canopy structure play an important role on benthic reflectance and its propagation above the water. Here, a plane parallel model was coupled with a three-dimensional radiative transfer canopy model, incorporating measured coral shapes and hyperspectral [...] Read more.
Shading and inter-reflections created by the three-dimensional coral canopy structure play an important role on benthic reflectance and its propagation above the water. Here, a plane parallel model was coupled with a three-dimensional radiative transfer canopy model, incorporating measured coral shapes and hyperspectral benthic reflectances, to investigate this question under different illumination and water column conditions. Results indicated that a Lambertian treatment of the bottom reflectance can be a reasonable assumption if a variable shading factor is included. Without flexibility in the shading treatment, nadir view bottom reflectances can vary by as much as ±20% (or ±9% in above-water remote sensing reflectance) under solar zenith angles (SZAs) up to 50°. Spectrally-independent shading factors are developed for benthic coral reflectance measurements based on the rugosity of the coral. In remote sensing applications, where the rugosity is unknown, a shading factor could be incorporated as an endmember for retrieval in the inversion scheme. In dense coral canopies in clear shallow waters, the benthos cannot always be treated as Lambertian, and for large solar-view angles the bi-directional reflectance distribution functions (BRDF) hotspot propagated to above water reflectances can create up to a 50% or more difference in water-leaving reflectances, and discrepancies of 20% even for nadir-view geometries. Full article
(This article belongs to the Special Issue Outstanding Topics in Ocean Optics)
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<p>Model setup: (<b>a</b>) Coral surface reflectances were derived from hyperspectral image, and (<b>b</b>) shape from 3D reconstructions from plaster casts; (<b>c</b>) A plane parallel water column model was coupled with (<b>d</b>) a 3D canopy model in two ways: (1) (<b>e</b>) To model bottom of water column light fields over (<b>f</b>) single structures and estimate (<b>g</b>) reflectance over the coral and (<b>h</b>) of a 50% mix with surrounding substrate; and (2) (<b>i</b>) Directional incident radiance at different angles over (<b>j</b>) assemblages of structures were used to characterize (<b>k</b>) the bi-directional reflectance distribution function (BRDF). The BRDF was then input to the water column model to give (<b>l</b>) water-leaving reflectances.</p>
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<p>Total absorption, <span class="html-italic">a</span>(λ), and attenuation, <span class="html-italic">c</span>(λ), as used in the two water column inherent optical property (IOP) treatments, forereef and lagoon (includes the contribution of pure water itself).</p>
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<p>High resolution model outputs, as a nadir-view orthogonal projected rendering above each coral, converted to RGB using the tristimulus functions [<a href="#B24-applsci-08-02688" class="html-bibr">24</a>]. Illumination conditions are solar zenith θ<sub>s</sub> = 50°, forereef IOPs, depth 1 m. These are the high-resolution models without interstitial IOPs (activity 1, <a href="#applsci-08-02688-t001" class="html-table">Table 1</a>). Note: Corals are not shown in the same relative scale, bar is 5 cm.</p>
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<p>Reflectance over coral shape area only (no substrate) at the bottom of the water column with no interstitial water scattering or attenuation included. Plots show surface reflectance (red), reflectance over coral area under 32 treatments (grey), surface reflectance scaled by shading factor (black), and % difference of each treatment from scaled surface reflectance (light blue, right hand y-axis).</p>
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<p>(<b>a</b>) High-resolution model versus; (<b>b</b>) low-resolution model of coral 35, scale bar is 5 cm.</p>
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<p>Reflectance over coral structure and surrounding substrate to give a 50% mix in areal cover. Plots show the coral surface and substrate surface reflectances (thin and thick green lines), and a 50% linear mix of those reflectances (red), reflectance over the coral and substrate area under 32 treatments (grey), surface reflectance scaled by shading factor (black), and % difference of each treatment from scaled surface reflectance (light blue, right hand y-axis).</p>
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<p>Median shading factor as a function of surface rugosity: (<b>a</b>) Over coral area only (<a href="#applsci-08-02688-f004" class="html-fig">Figure 4</a>), and; (<b>b</b>) over coral area and surrounding substrate in a 50% areal mix (<a href="#applsci-08-02688-f006" class="html-fig">Figure 6</a>) with data separated into solar zenith angles of 10° and 50°. Lines are of the form y = (1 − <span class="html-italic">A</span>) × exp[−<span class="html-italic">S</span> × (<span class="html-italic">x</span> − 1)] + <span class="html-italic">A</span> and are the least squares best fit, giving (<b>a</b>) <span class="html-italic">A</span> = 0.51, <span class="html-italic">S</span> = 0.72; (<b>b</b>) <span class="html-italic">A</span> = 0.61, <span class="html-italic">S</span> = 0.61.</p>
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<p>Examples from BRDF generation of canopy using reflectances of deep corals at 74% coral coverage (D74). Shown as nadir-view (θ<sub>e</sub> = 0°) orthogonal projected rendering above each canopy, corresponding to incident radiance from: (<b>a</b>) θ<sub>i</sub> = 10°; (<b>b</b>) θ<sub>i</sub> = 30° and; (<b>c</b>) θ<sub>i</sub> = 50°, respectively, azimuth φs = 0°. RGB images are created from hyperspectral data using the tristimulus functions and represent an area of 30 cm × 30 cm.</p>
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<p>Bidirectional reflectance functions (BRDFs) at 550 nm, of three of the composite coral canopies for shallow (S, 0–10 m) and deep (D, 10–20 m) corals with areal cover: (<b>a</b>,<b>b</b>) 46%; (<b>c</b>,<b>d</b>) 75%; and (<b>e</b>,<b>f</b>) 74%, in the incident plane (a, c, e, Δφ = 0°) and at 90° to the incident plane (<b>b</b>,<b>d</b>,<b>f</b>, Δφ = 90°). Arrows show direction of incident light, each plot shows θi of 10°, 30° and 50°. Negative view zenith angle for Δφ = 0° (<b>a</b>,<b>c</b>,<b>e</b>) means backward reflection (source and view point in the same hemisphere). Error bars are ±1 standard error on 12 values from assumed reciprocity, rotational and mirror symmetries.</p>
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<p>Modelled influence of three-dimensional coral BRDF effects (from <a href="#applsci-08-02688-f009" class="html-fig">Figure 9</a>) on water-leaving reflectance (π Lw/Ed) at 550 nm for: (<b>a</b>,<b>b</b>) Composite canopies S46 (46% shallow coral cover) and; (<b>c</b>,<b>d</b>,<b>e</b>,<b>f</b>) D74 (74% deep coral cover), in the incident plane (<b>a</b>,<b>c</b>,<b>e</b>, Δφ = 0°) and at 90° to the incident plane (<b>b</b>,<b>d</b>,<b>f</b>, Δφ = 90°). Each plot shows results of incident solar zenith angles of 10° (dotted lines) and 50° (solid lines), and depths 1, 2, 5, and 10 m. Negative view zenith angle for Δφ = 0° (<b>a</b>,<b>c</b>,<b>e</b>) means backward reflection (source and view point in the same hemisphere).</p>
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37 pages, 14721 KiB  
Article
Remote Sensing of CDOM, CDOM Spectral Slope, and Dissolved Organic Carbon in the Global Ocean
by Dirk Aurin, Antonio Mannino and David J. Lary
Appl. Sci. 2018, 8(12), 2687; https://doi.org/10.3390/app8122687 - 19 Dec 2018
Cited by 45 | Viewed by 7511
Abstract
A Global Ocean Carbon Algorithm Database (GOCAD) has been developed from over 500 oceanographic field campaigns conducted worldwide over the past 30 years including in situ reflectances and coincident satellite imagery, multi- and hyperspectral Chromophoric Dissolved Organic Matter (CDOM) absorption coefficients from 245–715 [...] Read more.
A Global Ocean Carbon Algorithm Database (GOCAD) has been developed from over 500 oceanographic field campaigns conducted worldwide over the past 30 years including in situ reflectances and coincident satellite imagery, multi- and hyperspectral Chromophoric Dissolved Organic Matter (CDOM) absorption coefficients from 245–715 nm, CDOM spectral slopes in eight visible and ultraviolet wavebands, dissolved and particulate organic carbon (DOC and POC, respectively), and inherent optical, physical, and biogeochemical properties. From field optical and radiometric data and satellite measurements, several semi-analytical, empirical, and machine learning algorithms for retrieving global DOC, CDOM, and CDOM slope were developed, optimized for global retrieval, and validated. Global climatologies of satellite-retrieved CDOM absorption coefficient and spectral slope based on the most robust of these algorithms lag seasonal patterns of phytoplankton biomass belying Case 1 assumptions, and track terrestrial runoff on ocean basin scales. Variability in satellite retrievals of CDOM absorption and spectral slope anomalies are tightly coupled to changes in atmospheric and oceanographic conditions associated with El Niño Southern Oscillation (ENSO), strongly covary with the multivariate ENSO index in a large region of the tropical Pacific, and provide insights into the potential evolution and feedbacks related to sea surface dissolved carbon in a warming climate. Further validation of the DOC algorithm developed here is warranted to better characterize its limitations, particularly in mid-ocean gyres and the southern oceans. Full article
(This article belongs to the Special Issue Outstanding Topics in Ocean Optics)
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<p>Top row: data distributions and counts (N) of relevant parameters and Chl (for context only) in Global Ocean Carbon Algorithm Database (GOCAD), NASA bio-Optical Marine Algorithm Dataset (NOMAD), and the synthetic ocean color dataset developed by the International Ocean Colour Coordinating Group (IOCCG). Bottom row: comparisons between the subset of GOCAD parameters used in optimization/tuning (Optim) and validation (Val) of algorithms (shown here with SeaWiFS match-ups, but also evaluated for MODIS Terra and Aqua with similar results). Populations of salinity and DOC share a common mean between optimization and validation datasets (ANOVA, <span class="html-italic">p</span> &gt; 0.01).</p>
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<p>Exponential slope of CDOM in NOMAD, IOCCG, and GOCAD. Median values for S<sub>412–600</sub> are highlighted in red for comparison. NOMAD and IOCCG lack UV CDOM.</p>
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<p>Global distribution of GOCAD and NOMAD field stations for CDOM (upper) and DOC (lower). The central panel shows the distributions of data within GOCAD separated into optimization (Optim) and validation (Val) dataset. Stations used in algorithm tuning are shown as red circles, the remainder of stations were available for satellite validation. The boxed subregions in the upper panel are shown in greater detail in <a href="#applsci-08-02687-f004" class="html-fig">Figure 4</a>.</p>
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<p>Examples of CDOM absorption at 412 nm (top row), and CDOM spectral slope in the UVB (middle row) and VIS (bottom row) from GOCAD show patterns which reflect the sources and age of CDOM in environments stretching from estuarine, such as the Chesapeake Bay in the eastern U.S., to stations sampled well offshore.</p>
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<p>MLR retrievals of CDOM plotted against field data for the tuning dataset (i.e., in situ <span class="html-italic">R<sub>rs</sub></span>(<span class="html-italic">λ</span>)). The solid line shows the fit through the data, and the 1:1 line is dashed.</p>
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<p>Random forest tree-bagger (RFTB), quasi-analytical algorithm (QAA), and generalized inherent optical property (GIOP) retrievals for tuning datasets.</p>
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<p>Taylor diagrams (top row) and target plots (bottom row) depicting comparative algorithm performance for retrieving CDOM absorption at 275 nm, 380 nm, and 412 nm from MODIS Aqua.</p>
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<p>Taylor diagrams (top row) and target plots (bottom row) depicting comparative algorithm performance for retrieving CDOM slope at 275–295 nm, 300–600 nm, and 412–600 nm from MODIS Aqua. Results from Shan11 and TS11 were suppressed to preserve scale.</p>
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<p>Taylor diagrams (top row) and target plots (bottom row) depicting comparative algorithm performance for retrieving DOC from MODIS Aqua and Terra, and SeaWiFS.</p>
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<p>Geographic distribution of error in MLR algorithm retrievals of CDOM absorption and slope in the VIS (top and center), and MLR2 retrievals of DOC (bottom) using validation stations and satellite imagery.</p>
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<p>Retrieved three-year mean, 9 km nominal resolution DOC from Aquarius and MODIS Aqua using the MLR2 inversion. Validation statistics are reasonably good for the MLR2 (<a href="#applsci-08-02687-f009" class="html-fig">Figure 9</a>, <a href="#applsci-08-02687-t008" class="html-table">Table 8</a>), but a larger number and wider geographic distribution of validation stations than are currently available is required to thoroughly evaluate the geographic and water-type limitations for MLR2, particularly in the mid-ocean gyres (see text <a href="#sec3dot2dot2-applsci-08-02687" class="html-sec">Section 3.2.2</a>). Overestimates of DOC (~41%) retrieved with the MLR2 were found in the southern oceans (S of 40° S)), but only for MODIS Aqua (i.e., not Terra, and no SeaWiFS stations were identified). Elsewhere (i.e., north of 40° S), retrievals tend to slightly underestimate DOC (&lt;10%). Caution is therefore advised in interpreting MLR2 retrievals in mid-ocean gyres, and in the southern oceans using Aqua.</p>
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<p>MLR retrievals of <span class="html-italic">a<sub>g</sub></span>(380) by season over the entire MODIS Aqua era (left column), and residuals between Chl and CDOM (right column). Imagery was binned from 4 km resolution monthly composites between August 2002 and January 2014.</p>
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<p>CDOM anomaly (left) and slope anomaly (right) from MLR applied to MODIS Aqua during Autumn in El Niño years (2002–2005; top panel) and La Niña years (2007, 2008, 2010, 2011; bottom panel). The Western Pacific Crescent (WPC) feature is defined here as the broad region exhibiting a notable decline in CDOM during El Niño years, and enhancement during La Niña. UV slope shows the opposite pattern, with lower slopes during La Niña, although the percentage change is roughly an order of magnitude lower. The box shows the portion of the WPC subsampled for comparison with MEI (See <a href="#applsci-08-02687-f014" class="html-fig">Figure 14</a>).</p>
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<p>Multivariate ENSO Index (MEI, in red), CDOM anomaly at 380 nm (black), and UVB slope anomaly (green, scaled by a factor of −10 for clarity) over the entire MODIS Aqua era for the region of interest highlighted in <a href="#applsci-08-02687-f013" class="html-fig">Figure 13</a>. Strong negative and positive correlations exist between MEI and CDOM and slope anomalies, respectively (see text).</p>
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14 pages, 1369 KiB  
Article
A Brief Review of Mueller Matrix Calculations Associated with Oceanic Particles
by Bingqiang Sun, George W. Kattawar, Ping Yang and Xiaodong Zhang
Appl. Sci. 2018, 8(12), 2686; https://doi.org/10.3390/app8122686 - 19 Dec 2018
Cited by 5 | Viewed by 4955
Abstract
The complete Stokes vector contains much more information than the radiance of light for the remote sensing of the ocean. Unlike the conventional radiance-only radiative transfer simulations, a full Mueller matrix-Stokes vector treatment provides a rigorous and correct approach for solving the transfer [...] Read more.
The complete Stokes vector contains much more information than the radiance of light for the remote sensing of the ocean. Unlike the conventional radiance-only radiative transfer simulations, a full Mueller matrix-Stokes vector treatment provides a rigorous and correct approach for solving the transfer of radiation in a scattering medium, such as the atmosphere-ocean system. In fact, radiative transfer simulation without considering the polarization state always gives incorrect results and the extent of the errors induced depends on a particular application being considered. However, the rigorous approach that fully takes the polarization state into account requires the knowledge of the complete single-scattering properties of oceanic particles with various sizes, morphologies, and refractive indices. For most oceanic particles, the comparisons between simulations and observations have demonstrated that the “equivalent-spherical” approximation is inadequate. We will therefore briefly summarize the advantages and disadvantages of a number of light scattering methods for non-spherical particles. Furthermore, examples for canonical cases with specifically oriented particles and randomly oriented particles will be illustrated. Full article
(This article belongs to the Special Issue Outstanding Topics in Ocean Optics)
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<p>Parameters used for light scattering by a dielectric particle. The field point <math display="inline"> <semantics> <mover accent="true"> <mi>r</mi> <mo>→</mo> </mover> </semantics> </math> is outside the scattering particle with wavenumber <span class="html-italic">k</span>, permittivity <math display="inline"> <semantics> <mi>ε</mi> </semantics> </math>, and permeability <math display="inline"> <semantics> <mrow> <msub> <mi>μ</mi> <mn>0</mn> </msub> </mrow> </semantics> </math> and the point <math display="inline"> <semantics> <mrow> <mover accent="true"> <mi>r</mi> <mo>→</mo> </mover> <mo>'</mo> </mrow> </semantics> </math> is inside the particle with wave number <span class="html-italic">k</span><sub>1</sub>, permittivity <math display="inline"> <semantics> <mrow> <msub> <mi>ε</mi> <mn>1</mn> </msub> </mrow> </semantics> </math>, and permeability <math display="inline"> <semantics> <mrow> <msub> <mi>μ</mi> <mn>0</mn> </msub> </mrow> </semantics> </math>.</p>
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<p>Discretization of a particle volume in the discrete-dipole approximation (DDA) method.</p>
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<p>Comparisons of Mueller matrix elements of a hexahedron particle calculated by the invariant-imbedding T-matrix method (IITM) and the ADDA. The volume equivalent sphere radius is 1µm and the incident wavelength is 0.658 µm. The relative refractive index is 1.12 + i0.0005.</p>
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<p>Comparisons of Mueller matrix elements of a hexahedron particle calculated by the IITM and the physical-geometric optics method (PGOM). The volume equivalent sphere radius is 8 µm and the incident wavelength is 0.658 µm. The relative refractive index is 1.12 + i0.0005.</p>
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25 pages, 44126 KiB  
Article
Assessing Fluorescent Organic Matter in Natural Waters: Towards In Situ Excitation–Emission Matrix Spectroscopy
by Oliver Zielinski, Nick Rüssmeier, Oliver D. Ferdinand, Mario L. Miranda and Jochen Wollschläger
Appl. Sci. 2018, 8(12), 2685; https://doi.org/10.3390/app8122685 - 19 Dec 2018
Cited by 12 | Viewed by 6740
Abstract
Natural organic matter (NOM) is a key parameter in aquatic biogeochemical processes. Part of the NOM pool exhibits optical properties, namely absorption and fluorescence. The latter is frequently utilized in laboratory measurements of its dissolved fraction (fluorescent dissolved organic matter, FDOM) through excitation–emission [...] Read more.
Natural organic matter (NOM) is a key parameter in aquatic biogeochemical processes. Part of the NOM pool exhibits optical properties, namely absorption and fluorescence. The latter is frequently utilized in laboratory measurements of its dissolved fraction (fluorescent dissolved organic matter, FDOM) through excitation–emission matrix spectroscopy (EEMS). We present the design and field application of a novel EEMS sensor system applicable in situ, the ‘Kallemeter’. Observations are based on a field campaign, starting in Norwegian coastal waters entering the Trondheimsfjord. Comparison against the bulk fluorescence of two commercial FDOM sensors exhibited a good correspondence of the different methods and the ability to resolve gradients and dynamics along the transect. Complementary laboratory EEM spectra measurements of surface water samples and their subsequent PARAFAC analysis revealed three dominant components while the ‘Kallemeter’ EEMS sensor system was able to produce reasonable EEM spectra in high DOM concentrated water bodies, yet high noise levels must be addressed in order to provide comparable PARAFAC components. Achievements and limitations of this proof-of-concept are discussed providing guidance towards full in situ EEMS measurements to resolve rapid changes and processes in natural waters based on the assessment of spectral properties. Their combination with multiwavelength FDOM sensors onboard autonomous platforms will enhance our capacities in observing biogeochemical processes in the marine environment in spatiotemporal and spectral dimensions. Full article
(This article belongs to the Special Issue Outstanding Topics in Ocean Optics)
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<p>(<b>a</b>) Excitation–emission matrix spectroscopy (EEMS) sensor system in profiling mode on a winch of a research vessel. (<b>b</b>) EEMS sensor system, installation for in situ underway observations in the moonpool of the research vessel, top-view. Close to the EEMS sensor system, an acoustic Doppler current profiler (ADCP) as well as the water intake pump for the FerryBox system were installed in the moonpool-frame. (<b>c</b>) View from below.</p>
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<p>System components of the submersible EEMS sensor system. (<b>a</b>) System connected to an on-board unit and remote PC-control (optional). (<b>b</b>) Schematic structure of excitation–emission matrices (EEM) fluorescence spectrometer hardware and optical components (connected by optical fibers 1, 2, 3). (<b>c</b>) Internal close-up with electronics and optical components. (<b>d</b>) Close-up of the outer components of the EEMS sensor system.</p>
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<p>Details of the EEMS sensor system sample flow-through cell. (<b>a</b>) Measuring area-section with right angle arrangement of the excitation and emission optical fibers including the concave and planar mirror, respectively. (<b>b</b>) Cut-section through the flow-through cell with media intake and quartz glass windows.</p>
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<p>(<b>a</b>) Optical fiber coupling of the Xenon flash lamp with the monochromator (labeled (1) in <a href="#applsci-08-02685-f002" class="html-fig">Figure 2</a>b). (<b>b</b>) Optical Y-fiber coupling of the monochromator with the reference spectrometer and the water sample flow-through cell (labeled (2) in <a href="#applsci-08-02685-f002" class="html-fig">Figure 2</a>b).</p>
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<p>EEM spectra for two concentrations of Suwannee River standard. Excitation wavelengths were used for the x-axis, while emission wavelengths were applied for the y-axis [<a href="#B42-applsci-08-02685" class="html-bibr">42</a>]. Color bar (z-axis) denotes relative fluorescence intensity.</p>
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<p>Overview of the study area (insert). Map shows Trondheimsfjord and its adjacent coastal sea with cruise track (solid black line) and stations (red dots) of R/V Heincke expedition HE491.</p>
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<p>Underway oceanographic data measured by the FerryBox. From top to bottom: Fluorescent dissolved organic matter (FDOM) from the Cyclops-7-U bulk fluorometer, chlorophyll a fluorescence, temperature, and salinity. The left panels display the data as time series, the corresponding right ones as a map plot. Dashed lines indicate the stations where water samples were collected. Gray lines in the top-left panel indicate availability of the in situ Kallemeter-based EEM spectra (N = 48).</p>
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<p>Comparative view on the extracted underway measurements from the MatrixFlu-UV (violet), the FerryBox FDOM Cyclops-7-U sensor (green) and the extracted bulk fluorescence signal from the EEM spectra (red stars), obtained along a transect from the Norwegian coastal sea into the Trondheimsfjord.</p>
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<p>Comparison of extracted ‘Kallemeter’ bulk fluorescence signal against FerryBox FDOM Cyclops-7-U sensor (<b>left panel</b>) as well as the corresponding channel of the MatrixFlu-UV (<b>right panel</b>). Red lines represent the results of a linear regression.</p>
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<p>Normalized EEM spectra from lab-based water sample analysis with the LS55 laboratory spectrofluorometer (<b>left</b>) and nearby (match-up of ±1 h) in situ measurements with the Kallemeter (<b>right</b>) for stations 12–15 of R/V Heincke expedition HE491 along a transect from the coastal ocean into the Trondheimsfjord. Notice that FDOM intensity in stations 14 and 15 are 5-times higher compared to stations 12 and 13.</p>
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<p>Top panel: PARAFAC-derived principal fluorescent components from lab-based EEM spectra from water samples at stations 11–15. From left to right: (C1) Terrestrial humic-like peaks A<sub>C</sub> and C; (C2) marine humic-like peaks A<sub>M</sub> and M; and (C3) protein-like Tryptophan peak T, all peaks denoted after Coble [<a href="#B29-applsci-08-02685" class="html-bibr">29</a>]. Color bar shows Raman units (RU). Bottom panel: PARAFAC derived principal fluorescent components from Kallemeter-based EEM spectra along the transect.</p>
Full article ">Figure A1
<p>Dark counts of the ‘Kallemeter’ fluorescence detection spectrometer, measured by a long-term in situ test of 50 full EEM spectra (presented as an overlay) over a period of 116 h exhibiting a constant slight intensity gradient against the ordinate (wavelength).</p>
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<p>Signal to noise measurement of tap water with the ‘Kallemeter’ fluorescence detection spectrometer (grey line, integration time 300 s), and the LS55 laboratory spectrofluorometer (black line, 2nd maxima masked), both excitation wavelengths are 260 nm.</p>
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<p>In situ underway measurements of EEM spectra, obtained from the moonpool of R/V Heincke (HE491) along transect from the coastal ocean into the Trondheimsfjord.</p>
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30 pages, 3846 KiB  
Article
Ocean Color Analytical Model Explicitly Dependent on the Volume Scattering Function
by Michael Twardowski and Alberto Tonizzo
Appl. Sci. 2018, 8(12), 2684; https://doi.org/10.3390/app8122684 - 19 Dec 2018
Cited by 29 | Viewed by 5014 | Correction
Abstract
An analytical radiative transfer (RT) model for remote sensing reflectance that includes the bidirectional reflectance distribution function (BRDF) is described. The model, called ZTT (Zaneveld-Twardowski-Tonizzo), is based on the restatement of the RT equation by Zaneveld (1995) in terms of light field shape [...] Read more.
An analytical radiative transfer (RT) model for remote sensing reflectance that includes the bidirectional reflectance distribution function (BRDF) is described. The model, called ZTT (Zaneveld-Twardowski-Tonizzo), is based on the restatement of the RT equation by Zaneveld (1995) in terms of light field shape factors. Besides remote sensing geometry considerations (solar zenith angle, viewing angle, and relative azimuth), the inputs are Inherent Optical Properties (IOPs) absorption a and backscattering bb coefficients, the shape of the particulate volume scattering function (VSF) in the backward direction, and the particulate backscattering ratio. Model performance (absolute error) is equivalent to full RT simulations for available high quality validation data sets, indicating almost all residual errors are inherent to the data sets themselves, i.e., from the measurements of IOPs and radiometry used as model input and in match up assessments, respectively. Best performance was observed when a constant backward phase function shape based on the findings of Sullivan and Twardowski (2009) was assumed in the model. Critically, using a constant phase function in the backward direction eliminates a key unknown, providing a path toward inversion to solve for a and bb. Performance degraded when using other phase function shapes. With available data sets, the model shows stronger performance than current state-of-the-art look-up table (LUT) based BRDF models used to normalize reflectance data, formulated on simpler first order RT approximations between rrs and bb/a or bb/(a + bb) (Morel et al., 2002; Lee et al., 2011). Stronger performance of ZTT relative to LUT-based models is attributed to using a more representative phase function shape, as well as the additional degrees of freedom achieved with several physically meaningful terms in the model. Since the model is fully described with analytical expressions, errors for terms can be individually assessed, and refinements can be readily made without carrying out the gamut of full RT computations required for LUT-based models. The ZTT model is invertible to solve for a and bb from remote sensing reflectance, and inversion approaches are being pursued in ongoing work. The focus here is with development and testing of the in-water forward model, but current ocean color remote sensing approaches to cope with an air-sea interface and atmospheric effects would appear to be transferable. In summary, this new analytical model shows good potential for future ocean color inversion with low bias, well-constrained uncertainties (including the VSF), and explicit terms that can be readily tuned. Emphasis is put on application to the future NASA Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) mission. Full article
(This article belongs to the Special Issue Outstanding Topics in Ocean Optics)
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<p>Refracted, in-water scattering angles made between the solar zenith and viewing angle, simulated for the upcoming NASA PACE satellite imager through a complete polar orbit (solid blue). Scattering angle distributions for SeaWiFS were similar. Data courtesy Bryan Franz (NASA GSFC). Angular weighting functions of commercial backscattering sensors WET Labs ECO-BB, ECO-NTU, and MCOMS and IMO-SC6 (see text) are overlaid after scaling by 5 × 10<sup>6</sup>.</p>
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<p>Spectral dependency of the <span class="html-italic">f<sub>L</sub></span> shape function at different <span class="html-italic">θ<sub>s</sub></span>’. Thin lines are solved <span class="html-italic">f<sub>L</sub></span> functions for each <span class="html-italic">θ<sub>s</sub></span>′ for the synthetic data set and stars are from the model approximation with constant shape described by <math display="inline"> <semantics> <mrow> <msub> <mi>f</mi> <mrow> <mi>L</mi> <mo>,</mo> <mi>a</mi> <mi>v</mi> <mi>e</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>λ</mi> <mo>)</mo> </mrow> </mrow> </semantics> </math> in Equation (30).</p>
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<p><span class="html-italic">r<sub>rs</sub></span> derived from the ZTT model with the synthetic data set compared to <span class="html-italic">r<sub>rs</sub></span> simulated using Hydrolight (HL): (<b>A</b>) <span class="html-italic">f<sub>L</sub></span> optimized spectrally (i.e., Equation (31) for the synthetic data set with Fournier-Forand phase functions individually determined for each [Chl]; (<b>B</b>) <span class="html-italic">f<sub>L</sub></span> optimized spectrally for the synthetic data set with constant <span class="html-italic">P<sub>bb,ST</sub></span>(<span class="html-italic">ψ</span>) (see text); and (<b>C</b>) <span class="html-italic">f<sub>L</sub></span> set to 1.05 with constant <span class="html-italic">P<sub>bb,ST</sub></span>(<span class="html-italic">ψ</span>). Colors represent wavelengths, from 400 to 700 nm with gray used for 350 &lt; λ &lt; 400 nm and 700 &lt; λ &lt; 800 nm.</p>
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<p><span class="html-italic">r<sub>rs</sub></span> computed from (<b>A</b>) ZTT, (<b>B</b>) ZTT with <math display="inline"> <semantics> <mrow> <msub> <mover accent="true"> <mi>b</mi> <mo>˜</mo> </mover> <mrow> <mi>b</mi> <mi>p</mi> </mrow> </msub> </mrow> </semantics> </math> fixed at 0.006, (<b>C</b>) M02, and (<b>D</b>) L11 models, compared to measured <span class="html-italic">r<sub>rs</sub></span> in the high quality validation data set from Tonizzo et al. [<a href="#B29-applsci-08-02684" class="html-bibr">29</a>]. Note L11 does not account for Raman scattering. Chlorophyll input for M02 was derived from spectral absorption using the Nardelli and Twardowski [<a href="#B78-applsci-08-02684" class="html-bibr">78</a>] line height algorithm. MAPE %<span class="html-italic">δ<sub>abs</sub></span> was 16%, 17%, 21%, and 21%, in A through D, respectively.</p>
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<p>Phase functions in the backward direction used to assess the ZTT model. “ST2009” is <span class="html-italic">P<sub>bb,ST</sub></span>(<span class="html-italic">ψ</span>) from [<a href="#B26-applsci-08-02684" class="html-bibr">26</a>] with the assumption <span class="html-italic">P<sub>bb,ST</sub></span>(<span class="html-italic">ψ</span> &gt; 170°) = <span class="html-italic">P<sub>bb</sub></span><sub>,<span class="html-italic">ST</span></sub>(170°); “Morel” is from Morel et al. [<a href="#B7-applsci-08-02684" class="html-bibr">7</a>] with endmember populations dominated by small and large particles also shown; “Measured” were directly measured; and “FF” are Fournier-Forand phase functions <span class="html-italic">P<sub>bb,FF</sub></span>(<span class="html-italic">ψ</span>,<math display="inline"> <semantics> <mrow> <msub> <mover accent="true"> <mi>b</mi> <mo>˜</mo> </mover> <mrow> <mi>b</mi> <mi>p</mi> </mrow> </msub> </mrow> </semantics> </math>) derived from measured <math display="inline"> <semantics> <mrow> <msub> <mover accent="true"> <mi>b</mi> <mo>˜</mo> </mover> <mrow> <mi>b</mi> <mi>p</mi> </mrow> </msub> </mrow> </semantics> </math> following [<a href="#B67-applsci-08-02684" class="html-bibr">67</a>].</p>
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<p>As in <a href="#applsci-08-02684-f004" class="html-fig">Figure 4</a>, but for the NASA NOMAD data set containing <span class="html-italic">a</span>, <span class="html-italic">b<sub>b</sub></span>, <span class="html-italic">r<sub>rs</sub></span>, and [Chl]. MAPE %<span class="html-italic">δ<sub>abs</sub></span> were 20%, 23%, 25%, and 26% in (<b>A</b>–<b>D</b>), respectively.</p>
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<p>Comparing measured <span class="html-italic">b<sub>b</sub></span>/<span class="html-italic">a</span> to results of inverting ZTT using least-squares minimization individually at each wavelength. Water Raman effects were removed before applying the ZTT model.</p>
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17 pages, 3541 KiB  
Article
Measuring and Modeling the Polarized Upwelling Radiance Distribution in Clear and Coastal Waters
by Arthur C. R. Gleason, Kenneth J. Voss, Howard R. Gordon, Michael S. Twardowski and Jean-François Berthon
Appl. Sci. 2018, 8(12), 2683; https://doi.org/10.3390/app8122683 - 19 Dec 2018
Cited by 5 | Viewed by 3071
Abstract
The upwelling spectral radiance distribution is polarized, and this polarization varies with the optical properties of the water body. Knowledge of the polarized, upwelling, bidirectional radiance distribution function (BRDF) is important for generating consistent, long-term data records for ocean color because the satellite [...] Read more.
The upwelling spectral radiance distribution is polarized, and this polarization varies with the optical properties of the water body. Knowledge of the polarized, upwelling, bidirectional radiance distribution function (BRDF) is important for generating consistent, long-term data records for ocean color because the satellite sensors from which the data are derived are sensitive to polarization. In addition, various studies have indicated that measurement of the polarization of the radiance leaving the ocean can used to determine particle characteristics (Tonizzo et al., 2007; Ibrahim et al., 2016; Chami et al., 2001). Models for the unpolarized BRDF (Morel et al., 2002; Lee et al., 2011) have been validated (Voss et al., 2007; Gleason et al., 2012), but variations in the polarization of the upwelling radiance due to the sun angle, viewing geometry, dissolved material, and suspended particles have not been systematically documented. In this work, we simulated the upwelling radiance distribution using a Monte Carlo-based radiative transfer code and measured it using a set of fish-eye cameras with linear polarizing filters. The results of model-data comparisons from three field experiments in clear and turbid coastal conditions showed that the degree of linear polarization (DOLP) of the upwelling light field could be determined by the model with an absolute error of ±0.05 (or 5% when the DOLP was expressed in %). This agreement was achieved even with a fixed scattering Mueller matrix, but did require in situ measurements of the other inherent optical properties, e.g., scattering coefficient, absorption coefficient, etc. This underscores the difficulty that is likely to be encountered using the particle scattering Mueller matrix (as indicated through the remote measurement of the polarized radiance) to provide a signature relating to the properties of marine particles beyond the attenuation/absorption coefficient. Full article
(This article belongs to the Special Issue Outstanding Topics in Ocean Optics)
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<p>Non-zero, normalized Muller matrix, <span class="html-italic">S</span>, elements for Rayleigh scattering (blue), V-F (red), and Mod-V-F (black).</p>
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<p>Comparison of the Stokes vector components <span class="html-italic">I</span>, <span class="html-italic">Q</span>/<span class="html-italic">I</span>, <span class="html-italic">U</span>/<span class="html-italic">I</span>, <span class="html-italic">DOLP</span>, and <math display="inline"><semantics> <mi>χ</mi> </semantics></math> for clear water. Shown are the calculation for single scattering (left column), RT model (center column), and data (right column). The data were taken on 2 December 2005, at 20:46 UTC off of Oahu, Hawaii. The conditions were: SZA = 48°, 442 nm, <span class="html-italic">Chl</span> = 0.1 mg/m<sup>3</sup>, <span class="html-italic">c<sub>t</sub></span> = 0.1 m<sup>−1</sup> (calculated from <span class="html-italic">Chl</span> as described in text), <span class="html-italic">ω</span><sub>0</sub> = 0.8, and clear skies. The intensity for the model was adjusted to match the data at nadir. Each image is a fisheye projection. Nadir is in the center of the circle, nadir angle is linearly proportional to radius from center. The angle of the Snell’s circle (48° nadir angle) is shown in as the white circle. The principal plane (plane containing the anti-solar point and the nadir direction) is a vertical line through the center of the image. The anti-solar direction is towards the bottom of the image, and the sun direction is towards the top of the image.</p>
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<p>Similar to <a href="#applsci-08-02683-f002" class="html-fig">Figure 2</a>, but for a case with more turbid water. The data were taken on 22 March 2009, at 9:40 UTC in the Ligurian Sea. The conditions were: SZA = 48°, 550 nm, <span class="html-italic">c<sub>t</sub></span> = 0.44 m<sup>−1</sup>, <span class="html-italic">ω</span><sub>0</sub> = 0.82, and clear skies. The figure geometry is the same as <a href="#applsci-08-02683-f002" class="html-fig">Figure 2</a>.</p>
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<p>Maximum <span class="html-italic">DOLP</span> in upwelling radiance distribution vs <span class="html-italic">c<sub>t</sub></span> (<b>a</b>) and <span class="html-italic">c<sub>t</sub></span>/<span class="html-italic">a<sub>t</sub></span> (<b>b</b>). The symbol legend is the same for both graphs. Filled circles represent the maximum <span class="html-italic">DOLP</span> in the total upwelling field, while the open circles represent the maximum <span class="html-italic">DOLP</span> in the portion of the upwelling light field inside the Snell’s circle (nadir angle less than 48°).</p>
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<p>Nadir <span class="html-italic">DOLP</span> as a function of solar zenith angle and <span class="html-italic">c<sub>t</sub></span>/<span class="html-italic">a<sub>t</sub></span>, computed from the RT model. Contours are lines of constant <span class="html-italic">DOLP</span>.</p>
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<p>Scatter plots of <span class="html-italic">DOLP</span> for model versus data, separated by cruise. Also shown is the best fit line (with <span class="html-italic">y</span>-intercept = 0) (black line) and 1:1 line (red line). The crosses correspond to data outside the Snell’s circle (nadir angle larger than 48°), and the dots to points inside the Snell circle. V-F was used in the model for (<b>a</b>–<b>c</b>), Mod-V-F was used for (<b>d</b>).</p>
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<p><span class="html-italic">DOLP<sub>diff</sub></span> vs solar zenith angle for each data image. The error bars are ± the standard deviation of the mean.</p>
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<p><span class="html-italic">DOLP<sub>diff</sub></span> as a function of <span class="html-italic">c<sub>t</sub></span> and <span class="html-italic">c<sub>t</sub></span>/<span class="html-italic">a<sub>t</sub></span>. Color code of data is given in <a href="#applsci-08-02683-f007" class="html-fig">Figure 7</a>. (<b>a</b>) shows <span class="html-italic">DOLP<sub>diff</sub></span> vs <span class="html-italic">c<sub>t</sub></span>, while (<b>b</b>) is <span class="html-italic">DOLP<sub>diff</sub></span> vs <span class="html-italic">c<sub>t</sub></span>/<span class="html-italic">a<sub>t</sub></span>.</p>
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<p><span class="html-italic">DOLP<sub>diff</sub></span> as a function of <span class="html-italic">b<sub>bt</sub></span>/<span class="html-italic">b<sub>t</sub></span>. There were no <span class="html-italic">b<sub>bt</sub></span> measurements during the Hawaii data collection.</p>
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<p>Scatter plots of <span class="html-italic">Q</span>/<span class="html-italic">I</span> (<b>a</b>) and <span class="html-italic">U</span>/<span class="html-italic">I</span> (<b>b</b>) for model versus data for the Ligurian Sea data set. Also shown is the best fit line between the model and data for each parameter (black line). The 1:1 line is shown in red.</p>
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52 pages, 2733 KiB  
Article
Progress in Forward-Inverse Modeling Based on Radiative Transfer Tools for Coupled Atmosphere-Snow/Ice-Ocean Systems: A Review and Description of the AccuRT Model
by Knut Stamnes, Børge Hamre, Snorre Stamnes, Nan Chen, Yongzhen Fan, Wei Li, Zhenyi Lin and Jakob Stamnes
Appl. Sci. 2018, 8(12), 2682; https://doi.org/10.3390/app8122682 - 19 Dec 2018
Cited by 29 | Viewed by 4608
Abstract
A tutorial review is provided of forward and inverse radiative transfer in coupled atmosphere-snow/ice-water systems. The coupled system is assumed to consist of two adjacent horizontal slabs separated by an interface across which the refractive index changes abruptly from its value in air [...] Read more.
A tutorial review is provided of forward and inverse radiative transfer in coupled atmosphere-snow/ice-water systems. The coupled system is assumed to consist of two adjacent horizontal slabs separated by an interface across which the refractive index changes abruptly from its value in air to that in ice/water. A comprehensive review is provided of the inherent optical properties of air and water (including snow and ice). The radiative transfer equation for unpolarized as well as polarized radiation is described and solutions are outlined. Several examples of how to formulate and solve inverse problems encountered in environmental optics involving coupled atmosphere-water systems are discussed in some detail to illustrate how the solutions to the radiative transfer equation can be used as a forward model to solve practical inverse problems. Full article
(This article belongs to the Special Issue Outstanding Topics in Ocean Optics)
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<p>Coordinate system for scattering by a volume element at <math display="inline"><semantics> <mi mathvariant="bold">O</mi> </semantics></math>. The points <math display="inline"><semantics> <mi mathvariant="bold">C</mi> </semantics></math>, <math display="inline"><semantics> <mi mathvariant="bold">A</mi> </semantics></math> and <math display="inline"><semantics> <mi mathvariant="bold">B</mi> </semantics></math> are located on the unit sphere. The incident light beam with Stokes vector <math display="inline"><semantics> <msubsup> <mi mathvariant="bold">I</mi> <mrow> <mi mathvariant="normal">S</mi> </mrow> <mi>inc</mi> </msubsup> </semantics></math> is in direction <math display="inline"><semantics> <mrow> <mi mathvariant="bold">OA</mi> <mo>(</mo> <msup> <mi>θ</mi> <mo>′</mo> </msup> <mo>,</mo> <msup> <mi>ϕ</mi> <mo>′</mo> </msup> <mo>)</mo> </mrow> </semantics></math> with unit vector <math display="inline"><semantics> <msup> <mover accent="true"> <mi mathvariant="sans-serif">Ω</mi> <mo stretchy="false">^</mo> </mover> <mo>′</mo> </msup> </semantics></math>, the scattered beam with Stokes vector <math display="inline"><semantics> <msubsup> <mi mathvariant="bold">I</mi> <mrow> <mi mathvariant="normal">S</mi> </mrow> <mi>sca</mi> </msubsup> </semantics></math> is in direction <math display="inline"><semantics> <mrow> <mi mathvariant="bold">OB</mi> <mo>(</mo> <mi>θ</mi> <mo>,</mo> <mi>ϕ</mi> <mo>)</mo> </mrow> </semantics></math> with unit vector <math display="inline"><semantics> <mover accent="true"> <mi mathvariant="sans-serif">Ω</mi> <mo stretchy="false">^</mo> </mover> </semantics></math> [<a href="#B5-applsci-08-02682" class="html-bibr">5</a>].</p>
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<p>Rayleigh, Fournier-Forand, and Petzold scattering phase functions used to represent scattering by water molecules, pigmented particles, and non-algal particles, respectively, in the CCRR bio-optical model. To generate the FF scattering phase function the values <math display="inline"><semantics> <mrow> <mi>ξ</mi> <mo>=</mo> <mn>3.38</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>m</mi> <mo stretchy="false">˜</mo> </mover> <mi mathvariant="normal">r</mi> </msub> <mo>=</mo> <mn>1.068</mn> </mrow> </semantics></math> were used.</p>
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<p>Comparison between model-simulated and measured reflectances. The measurements are in solid blue and the simulations in dashed red lines. The dip in the measured reflectance in the lower left panel is due to aircraft shadowing. [Reproduced from Figures 1a and 2a of [<a href="#B80-applsci-08-02682" class="html-bibr">80</a>].]</p>
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<p>Comparison between model-simulated reflectances assuming a 1D Gaussian BRDF (<b>left</b>), a 2D Gaussian BRDF (<b>middle</b>), and measurements (<b>right</b>) obtained on 10 July 2001.</p>
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<p>Simulated upward radiance in the nadir direction at the top of the atmosphere and close to the ocean surface. Solar zenith angle = 45<math display="inline"><semantics> <msup> <mrow/> <mo>∘</mo> </msup> </semantics></math>, US Standard atmosphere with aerosol optical depth = 0.23 at 500 nm. (<b>Left</b>) Clear water with chlorophyll concentration = 0.1 mg·m<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </semantics></math>, MIN = 0.003 g·m<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </semantics></math>, CDOM443 = 0.003 m<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math> (CCRR bio-optical model). (<b>Right</b>) Turbid water with chlorophyll concentration = 10 mg·m<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </semantics></math>, MIN = 0.1 g·m<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </semantics></math>, CDOM443 = 0.1 m<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math>.</p>
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<p>The ratio of the values for turbid water to those for clear water in <a href="#applsci-08-02682-f005" class="html-fig">Figure 5</a>.</p>
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<p>MODIS image comparison between OC-SMART (<b>top</b>) and standard SeaDAS (<b>bottom</b>) retrievals on 18 April 2014 over a Norwegian coastal area [<a href="#B107-applsci-08-02682" class="html-bibr">107</a>]. From left to right: <math display="inline"><semantics> <msub> <mi>τ</mi> <mn>869</mn> </msub> </semantics></math>, f, CHL, CDOM and <math display="inline"><semantics> <msub> <mi>b</mi> <mrow> <mi>b</mi> <mi>p</mi> </mrow> </msub> </semantics></math>, respectively.</p>
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34 pages, 1633 KiB  
Article
The Fundamental Contribution of Phytoplankton Spectral Scattering to Ocean Colour: Implications for Satellite Detection of Phytoplankton Community Structure
by Lisl Robertson Lain and Stewart Bernard
Appl. Sci. 2018, 8(12), 2681; https://doi.org/10.3390/app8122681 - 19 Dec 2018
Cited by 9 | Viewed by 4598
Abstract
There is increasing interdisciplinary interest in phytoplankton community dynamics as the growing environmental problems of water quality (particularly eutrophication) and climate change demand attention. This has led to a pressing need for improved biophysical and causal understanding of Phytoplankton Functional Type (PFT) optical [...] Read more.
There is increasing interdisciplinary interest in phytoplankton community dynamics as the growing environmental problems of water quality (particularly eutrophication) and climate change demand attention. This has led to a pressing need for improved biophysical and causal understanding of Phytoplankton Functional Type (PFT) optical signals, in order for satellite radiometry to be used to detect ecologically relevant phytoplankton assemblage changes. Biophysically and biogeochemically consistent phytoplankton Inherent Optical Property (IOP) models play an important role in achieving this understanding, as the optical effects of phytoplankton assemblage changes can be examined systematically in relation to the bulk optical water-leaving signal. The Equivalent Algal Populations (EAP) model is used here to investigate the source and magnitude of size- and pigment- driven PFT signals in the water-leaving reflectance, as well as the potential to detect these using satellite radiometry. This model places emphasis on the determination of biophysically consistent phytoplankton IOPs, with both absorption and scattering determined by mathematically cogent relationships to the particle complex refractive indices. All IOPs are integrated over an entire size distribution. A distinctive attribute is the model’s comprehensive handling of the spectral and angular character of phytoplankton scattering. Selected case studies and sensitivity analyses reveal that phytoplankton spectral scattering is most useful and the least ambiguous driver of the PFT signal. Key findings are that there is the most sensitivity in phytoplankton backscatter ( b b ϕ ) in the 1–6 μ m size range; the backscattering-driven signal in the 520 to 570 nm region is the critical PFT identifier at marginal biomass, and that, while PFT information does appear at blue wavelengths, absorption-driven signals are compromised by ambiguity due to biomass and non-algal absorption. Low signal in the red, due primarily to absorption by water, inhibits PFT detection here. The study highlights the need to quantitatively understand the constraints imposed by phytoplankton biomass and the IOP budget on the assemblage-related signal. A proportional phytoplankton contribution of approximately 40% to the total b b appears to a reasonable minimum threshold in terms of yielding a detectable optical change in R r s . We hope these findings will provide considerable insight into the next generation of PFT algorithms. Full article
(This article belongs to the Special Issue Outstanding Topics in Ocean Optics)
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Figure 1

Figure 1
<p>Relative contribution of phytoplankton to total <math display="inline"><semantics> <msub> <mi>R</mi> <mrow> <mi>r</mi> <mi>s</mi> </mrow> </msub> </semantics></math> (with <math display="inline"><semantics> <mrow> <msub> <mi>a</mi> <mrow> <mi>g</mi> <mi>d</mi> </mrow> </msub> <mrow> <mo>(</mo> <mn>400</mn> <mo>)</mo> </mrow> <mo>=</mo> <mn>0.07</mn> <mo>·</mo> <msup> <mrow> <mo>[</mo> <mi>C</mi> <mi>h</mi> <msub> <mi>l</mi> <mi>a</mi> </msub> <mo>]</mo> </mrow> <mrow> <mn>0.75</mn> </mrow> </msup> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mi>b</mi> <mrow> <mi>b</mi> <mi>n</mi> <mi>a</mi> <mi>p</mi> </mrow> </msub> <mrow> <mo>(</mo> <mn>550</mn> <mo>)</mo> </mrow> </mrow> </semantics></math> = 0.005 m<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math>) for increasing biomass with <math display="inline"><semantics> <msub> <mi>D</mi> <mrow> <mi>e</mi> <mi>f</mi> <mi>f</mi> </mrow> </msub> </semantics></math> = 2 and 12 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m. These populations are idealised examples and not intended to represent any observed relationship between Chl <span class="html-italic">a</span> concentration and <math display="inline"><semantics> <msub> <mi>D</mi> <mrow> <mi>e</mi> <mi>f</mi> <mi>f</mi> </mrow> </msub> </semantics></math>.</p>
Full article ">Figure 2
<p>Example proportional phytoplankton to total Inherent Optical Property (IOP) contributions for Case 1 waters, for idealised eukaryote assemblages of 2 and 12 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m.</p>
Full article ">Figure 3
<p>Modelled <math display="inline"><semantics> <msub> <mi>R</mi> <mrow> <mi>r</mi> <mi>s</mi> </mrow> </msub> </semantics></math> for stations 20, 21, 12 and 13 of SOSCEX III. The modelled bulk <math display="inline"><semantics> <msub> <mi>R</mi> <mrow> <mi>r</mi> <mi>s</mi> </mrow> </msub> </semantics></math> are calculated using Equivalent Algal Populations (EAP) generalised Chl <span class="html-italic">a</span>-carotenoid refractive indices and measured Chl <span class="html-italic">a</span> concentrations for the phytoplankton component, and include estimated <math display="inline"><semantics> <mrow> <msub> <mi>a</mi> <mrow> <mi>g</mi> <mi>d</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>λ</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>b</mi> <mrow> <mi>b</mi> <mi>n</mi> <mi>a</mi> <mi>p</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>λ</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> contributions appropriate for this region [<a href="#B75-applsci-08-02681" class="html-bibr">75</a>,<a href="#B76-applsci-08-02681" class="html-bibr">76</a>]. Stations 20 to 21 (<b>A</b>) represent a large change in both Chl <span class="html-italic">a</span> concentration and in <math display="inline"><semantics> <msub> <mi>D</mi> <mrow> <mi>e</mi> <mi>f</mi> <mi>f</mi> </mrow> </msub> </semantics></math>. Stations 12 to 13 (<b>B</b>) represent a large change in Chl <span class="html-italic">a</span> concentration only. The centre panel shows the measured <math display="inline"><semantics> <msub> <mi>D</mi> <mrow> <mi>e</mi> <mi>f</mi> <mi>f</mi> </mrow> </msub> </semantics></math> for the cruise track (starting at the ice shelf on the bottom right and continuing in an anticlockwise direction.) The effective diameter image is courtesy of SANAE 55 Report [<a href="#B74-applsci-08-02681" class="html-bibr">74</a>].</p>
Full article ">Figure 4
<p>Southern Ocean stations 20 to 21: <math display="inline"><semantics> <mrow> <mi>δ</mi> <msub> <mi>R</mi> <mrow> <mi>r</mi> <mi>s</mi> </mrow> </msub> <mi>ϕ</mi> </mrow> </semantics></math> is shown for <math display="inline"><semantics> <mrow> <mi>δ</mi> <msub> <mi>D</mi> <mrow> <mi>e</mi> <mi>f</mi> <mi>f</mi> </mrow> </msub> </mrow> </semantics></math> of 6 to 16 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m (<b>A</b>). The effect of <math display="inline"><semantics> <mrow> <msub> <mi>a</mi> <mrow> <mi>g</mi> <mi>d</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>λ</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> at 435 nm is shown in (<b>B</b>), and <math display="inline"><semantics> <mrow> <msub> <mi>b</mi> <mrow> <mi>b</mi> <mi>n</mi> <mi>a</mi> <mi>p</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>λ</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> at 570 nm in (<b>C</b>). The units of the colour bars are sr<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math>.</p>
Full article ">Figure 5
<p>A simulated transition from 6 to 16 <math display="inline"><semantics> <msub> <mi>D</mi> <mrow> <mi>e</mi> <mi>f</mi> <mi>f</mi> </mrow> </msub> </semantics></math> with biomass 1 to 11 mg/m<math display="inline"><semantics> <msup> <mrow/> <mn>3</mn> </msup> </semantics></math>. Intermediate values of <math display="inline"><semantics> <msub> <mi>D</mi> <mrow> <mi>e</mi> <mi>f</mi> <mi>f</mi> </mrow> </msub> </semantics></math> and Chl <span class="html-italic">a</span> are simply linearly interpolated. The lines highlight 435 nm and 570 nm, regions of maximum size signal, which are (at 435 nm) and are not (at 570 nm) sensitive to the effects of additional optical constituents.</p>
Full article ">Figure 6
<p>Modelled <math display="inline"><semantics> <msub> <mi>R</mi> <mrow> <mi>r</mi> <mi>s</mi> </mrow> </msub> </semantics></math> for Stations 12 and 13 (<b>A</b>), with EAP eukaryote phytoplankton IOPs, and <math display="inline"><semantics> <mrow> <msub> <mi>a</mi> <mrow> <mi>g</mi> <mi>d</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>λ</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>b</mi> <mrow> <mi>b</mi> <mi>n</mi> <mi>a</mi> <mi>p</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>λ</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> components estimated guided by observations in [<a href="#B75-applsci-08-02681" class="html-bibr">75</a>,<a href="#B76-applsci-08-02681" class="html-bibr">76</a>], respectively; (<b>B</b>) shows <math display="inline"><semantics> <mrow> <mi>δ</mi> <msub> <mi>R</mi> <mrow> <mi>r</mi> <mi>s</mi> </mrow> </msub> <mi>ϕ</mi> </mrow> </semantics></math> for this large change in Chl <span class="html-italic">a</span> concentration (1 to 7 mg/m<math display="inline"><semantics> <msup> <mrow/> <mn>3</mn> </msup> </semantics></math>) but a small <math display="inline"><semantics> <mrow> <mi>δ</mi> <msub> <mi>D</mi> <mrow> <mi>e</mi> <mi>f</mi> <mi>f</mi> </mrow> </msub> </mrow> </semantics></math> of 7–8 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m. The unit of the colour bar is sr<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math>. Note that the results are one order of magnitude less than in the previous example; (<b>C</b>) shows the negligible effect on <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>r</mi> <mi>s</mi> </mrow> </msub> <mi>ϕ</mi> </mrow> </semantics></math> of a change in <math display="inline"><semantics> <msub> <mi>D</mi> <mrow> <mi>e</mi> <mi>f</mi> <mi>f</mi> </mrow> </msub> </semantics></math> from 7 to 8 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m at the measured Chl <span class="html-italic">a</span> concentrations.</p>
Full article ">Figure 7
<p>Benguela-like pigment-based experiment: Modelled <math display="inline"><semantics> <msub> <mi>R</mi> <mrow> <mi>r</mi> <mi>s</mi> </mrow> </msub> </semantics></math> shown for Chl <span class="html-italic">a</span>-carotenoid pigmented assemblages (solid lines) and phycoerythrin containing assemblages (dotted lines) for identical Chl <span class="html-italic">a</span> concentrations, at 0.1, 0.3, 3, 10 and 30 mg·m<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </semantics></math>. There is no change in <math display="inline"><semantics> <msub> <mi>D</mi> <mrow> <mi>e</mi> <mi>f</mi> <mi>f</mi> </mrow> </msub> </semantics></math>, both are 12 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m. The non-algal optical constituents are modelled with <math display="inline"><semantics> <mrow> <msub> <mi>a</mi> <mrow> <mi>g</mi> <mi>d</mi> </mrow> </msub> <mrow> <mo>(</mo> <mn>400</mn> <mo>)</mo> </mrow> <mo>=</mo> <mn>0.07</mn> <mo>*</mo> <msup> <mrow> <mo>[</mo> <mi>C</mi> <mi>h</mi> <msub> <mi>l</mi> <mi>a</mi> </msub> <mo>]</mo> </mrow> <mrow> <mn>0.75</mn> </mrow> </msup> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mi>b</mi> <mrow> <mi>b</mi> <mi>n</mi> <mi>a</mi> <mi>p</mi> </mrow> </msub> <mrow> <mo>(</mo> <mn>550</mn> <mo>)</mo> </mrow> </mrow> </semantics></math> = 0.005 m<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math>.</p>
Full article ">Figure 8
<p><math display="inline"><semantics> <mrow> <mi>δ</mi> <msub> <mi>R</mi> <mrow> <mi>r</mi> <mi>s</mi> </mrow> </msub> </mrow> </semantics></math> shown for a change from a high biomass <span class="html-italic">Myrionecta rubra</span>-dominated assemblage, to a high biomass peridinin (carotenoid)-containing dinoflagellate-dominated assemblage. There is no change in <math display="inline"><semantics> <msub> <mi>D</mi> <mrow> <mi>e</mi> <mi>f</mi> <mi>f</mi> </mrow> </msub> </semantics></math>.</p>
Full article ">Figure 9
<p><math display="inline"><semantics> <mrow> <mi>δ</mi> <msub> <mi>R</mi> <mrow> <mi>r</mi> <mi>s</mi> </mrow> </msub> </mrow> </semantics></math> sensitivity to <math display="inline"><semantics> <msub> <mi>a</mi> <mrow> <mi>g</mi> <mi>d</mi> </mrow> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>b</mi> <mrow> <mi>b</mi> <mi>n</mi> <mi>a</mi> <mi>p</mi> </mrow> </msub> </semantics></math> at 570 nm, for a high biomass <span class="html-italic">Myrionecta rubra</span>-dominated assemblage, to a high biomass peridinin (carotenoid)-containing dinoflagellate-dominated assemblage.</p>
Full article ">Figure 10
<p>Maximum <math display="inline"><semantics> <mrow> <mi>δ</mi> <msub> <mi>R</mi> <mrow> <mi>r</mi> <mi>s</mi> </mrow> </msub> <mi>ϕ</mi> </mrow> </semantics></math> for <math display="inline"><semantics> <mrow> <mi>δ</mi> <msub> <mi>D</mi> <mrow> <mi>e</mi> <mi>f</mi> <mi>f</mi> </mrow> </msub> </mrow> </semantics></math> from a starting assemblage with <math display="inline"><semantics> <msub> <mi>D</mi> <mrow> <mi>e</mi> <mi>f</mi> <mi>f</mi> </mrow> </msub> </semantics></math> 2 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m, as Chl <span class="html-italic">a</span> varies (<b>A</b>). Note that the <math display="inline"><semantics> <mrow> <mi>δ</mi> <msub> <mi>R</mi> <mrow> <mi>r</mi> <mi>s</mi> </mrow> </msub> <mi>ϕ</mi> </mrow> </semantics></math> occurs at different wavelengths from 500 to 600 nm (<b>B</b>), and this shows the maximum signal, so there is no exact wavelength information in (<b>A</b>). Using a difference of 1 × 10<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </semantics></math> sr<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math> as a threshold for detection by satellite, it can be seen that, while the maximum size change here (2 to 8 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m) is not detectable with Chl <span class="html-italic">a</span> &lt; 1 mg/m<math display="inline"><semantics> <msup> <mrow/> <mn>3</mn> </msup> </semantics></math>, by 10 mg/m<math display="inline"><semantics> <msup> <mrow/> <mn>3</mn> </msup> </semantics></math>, even a small change in <math display="inline"><semantics> <msub> <mi>D</mi> <mrow> <mi>e</mi> <mi>f</mi> <mi>f</mi> </mrow> </msub> </semantics></math> results in a detectable change in <math display="inline"><semantics> <msub> <mi>R</mi> <mrow> <mi>r</mi> <mi>s</mi> </mrow> </msub> </semantics></math>.</p>
Full article ">Figure 11
<p><math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>r</mi> <mi>s</mi> </mrow> </msub> <mi>ϕ</mi> </mrow> </semantics></math> ratios for blue:green, green:red and red:NIR (Near Infra-Red) wavelengths as shown, for Chl <span class="html-italic">a</span> concentrations of 0.1 to 20 mg/m<math display="inline"><semantics> <msup> <mrow/> <mn>3</mn> </msup> </semantics></math> and <math display="inline"><semantics> <msub> <mi>D</mi> <mrow> <mi>e</mi> <mi>f</mi> <mi>f</mi> </mrow> </msub> </semantics></math> 1 to 40 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m. The B/G ratio shows a strong biomass dependency and a small sensitivity to size at large sizes, for 0.5 ≤ Chl <span class="html-italic">a</span>≤ 4.5 mg/m<math display="inline"><semantics> <msup> <mrow/> <mn>3</mn> </msup> </semantics></math>. The b<math display="inline"><semantics> <msub> <mrow/> <mrow> <mi>b</mi> <mi>ϕ</mi> </mrow> </msub> </semantics></math> ratios all display a strong size signal at 2–4 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m, and the G/R ratio shows a corresponding size-related feature.</p>
Full article ">Figure 12
<p><math display="inline"><semantics> <mrow> <msubsup> <mi>b</mi> <mrow> <mi>b</mi> </mrow> <mo>*</mo> </msubsup> <mi>ϕ</mi> </mrow> </semantics></math> shown for <math display="inline"><semantics> <msub> <mi>D</mi> <mrow> <mi>e</mi> <mi>f</mi> <mi>f</mi> </mrow> </msub> </semantics></math> 1 to 10 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m. The largest differences in backscatter across the spectrum occur between 1 and 4 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m, with the exception of the overlapping of <math display="inline"><semantics> <mrow> <msubsup> <mi>b</mi> <mrow> <mi>b</mi> </mrow> <mo>*</mo> </msubsup> <mi>ϕ</mi> </mrow> </semantics></math> in the red and NIR.</p>
Full article ">Figure 13
<p>Percentage contribution of phytoplankton to total backscatter (including water, and with nominal <math display="inline"><semantics> <msub> <mi>b</mi> <mrow> <mi>b</mi> <mi>n</mi> <mi>a</mi> <mi>p</mi> </mrow> </msub> </semantics></math>(550) = 0.005), shown for <math display="inline"><semantics> <msub> <mi>D</mi> <mrow> <mi>e</mi> <mi>f</mi> <mi>f</mi> </mrow> </msub> </semantics></math> 1 to 40 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m and Chl <span class="html-italic">a</span> from 0.1 to 20 mg/m<math display="inline"><semantics> <msup> <mrow/> <mn>3</mn> </msup> </semantics></math>, at 440, 560 and 665 nm.</p>
Full article ">Figure A1
<p>Pigment absorption spectra from Bricaud et al. [<a href="#B87-applsci-08-02681" class="html-bibr">87</a>], reprinted with permission from the American Geophysical Union. The broad featureless absorption spectra of fucoxanthin and peridinin peaking at around 500 nm are shown by the thin and thick brown lines, respectively.</p>
Full article ">Figure A2
<p>EAP Eukaryote Chl <span class="html-italic">a</span>-carotenoid-dominated IOPs for a range of assemblage <math display="inline"><semantics> <msub> <mi>D</mi> <mrow> <mi>e</mi> <mi>f</mi> <mi>f</mi> </mrow> </msub> </semantics></math>.</p>
Full article ">Figure A3
<p>EAP Phycoerythrin-containing IOPs (based on <span class="html-italic">Myrionecta Rubra</span>), used for cryptophyte-dominated assemblages in the Benguela, and <span class="html-italic">Synechococcus</span> sp. in the Southern Ocean.</p>
Full article ">Figure A4
<p>Bulk backscatter ratio shown for <math display="inline"><semantics> <msub> <mi>D</mi> <mrow> <mi>e</mi> <mi>f</mi> <mi>f</mi> </mrow> </msub> </semantics></math> 2 and 12 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m, with nominal <math display="inline"><semantics> <msub> <mi>b</mi> <mrow> <mi>b</mi> <mi>n</mi> <mi>a</mi> <mi>p</mi> </mrow> </msub> </semantics></math>(550) = 0.01 m<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math> and <math display="inline"><semantics> <msub> <mi>b</mi> <mrow> <mi>n</mi> <mi>a</mi> <mi>p</mi> </mrow> </msub> </semantics></math> as 50 times the <math display="inline"><semantics> <msub> <mi>b</mi> <mrow> <mi>b</mi> <mi>n</mi> <mi>a</mi> <mi>p</mi> </mrow> </msub> </semantics></math>, as for a coastal environment, shown for Chl <span class="html-italic">a</span> of 0.5, 1.0 and 2.0 mg/m<math display="inline"><semantics> <msup> <mrow/> <mn>3</mn> </msup> </semantics></math>. The elevated backscatter ratio of coastal environments with respect to the Southern Ocean (where <math display="inline"><semantics> <msub> <mi>b</mi> <mrow> <mi>n</mi> <mi>a</mi> <mi>p</mi> </mrow> </msub> </semantics></math> is modelled as 100 times the <math display="inline"><semantics> <msub> <mi>b</mi> <mrow> <mi>b</mi> <mi>n</mi> <mi>a</mi> <mi>p</mi> </mrow> </msub> </semantics></math>) is attributed to the contribution of terrestrial mineral particles with a high refractive index [<a href="#B66-applsci-08-02681" class="html-bibr">66</a>,<a href="#B99-applsci-08-02681" class="html-bibr">99</a>].</p>
Full article ">Figure A5
<p>Spectral position of maximum <math display="inline"><semantics> <mrow> <mi>δ</mi> <msub> <mi>R</mi> <mrow> <mi>r</mi> <mi>s</mi> </mrow> </msub> <mi>ϕ</mi> </mrow> </semantics></math> for assemblage changes from 8 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m and 14 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m, respectively.</p>
Full article ">Figure A6
<p>Total <math display="inline"><semantics> <msub> <mi>R</mi> <mrow> <mi>r</mi> <mi>s</mi> </mrow> </msub> </semantics></math> with satellite measurement uncertainties in the blue and red bands from [<a href="#B16-applsci-08-02681" class="html-bibr">16</a>] and linearly interpolated between them. An indication of model uncertainty on the <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>r</mi> <mi>s</mi> </mrow> </msub> <mi>ϕ</mi> </mrow> </semantics></math> is calculated by the spectral differences resulting from the use of a combined <math display="inline"><semantics> <mrow> <msub> <mi>b</mi> <mrow> <mi>b</mi> <mi>p</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>λ</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>-specific Fournier Forand phase function independent of wavelength, vs. wavelength- and <math display="inline"><semantics> <mrow> <msub> <mi>b</mi> <mrow> <mi>b</mi> <mi>ϕ</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>λ</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>-dependent EAP phase functions.</p>
Full article ">
23 pages, 4427 KiB  
Article
Modeling Sea Bottom Hyperspectral Reflectance
by Georges Fournier, Jean-Pierre Ardouin and Martin Levesque
Appl. Sci. 2018, 8(12), 2680; https://doi.org/10.3390/app8122680 - 19 Dec 2018
Cited by 2 | Viewed by 3292
Abstract
Over the near-ultraviolet (UV) and visible spectrum the reflectance from mineral compounds and vegetation is predominantly due to absorption and scattering in the bulk material. Except for a factor of scale, the radiative transfer mechanism is similar to that seen in murky optically [...] Read more.
Over the near-ultraviolet (UV) and visible spectrum the reflectance from mineral compounds and vegetation is predominantly due to absorption and scattering in the bulk material. Except for a factor of scale, the radiative transfer mechanism is similar to that seen in murky optically complex waters. We therefore adapted a semi-empirical algebraic irradiance model developed by Albert and Mobley to calculate the irradiance reflectance from both mineral compounds and vegetation commonly found on the sea bottom. This approach can be used to accurately predict the immersed reflectance spectra given the reflectance measured in air. When applied to mineral-based compounds or various types of marine vegetation, we obtain a simple two-parameter fit that accurately describes the key features of the reflectance spectra. The non-linear spectral combination effect as a function of the thickness of vegetation growing on a mineral substrate is then accounted for by a third parameter. Full article
(This article belongs to the Special Issue Outstanding Topics in Ocean Optics)
Show Figures

Figure 1

Figure 1
<p>This figure is a schematic of the microstructure elements relevant to scattering and absorption for both minerals (grains) and vegetation (cells). The incident light rays (1) are reflected (2) and transmitted at the first surface (3). The rays transmitted through the first surface are subsequently both reflected back from the inner surfaces of the grains (4) and absorbed. The rays that penetrate deeper (5) are multiply scattered before coming back to the surface and have a near Lambertian (uniform) scattering distribution.</p>
Full article ">Figure 2
<p>Wet to dry angularly averaged Fresnel reflectivity factors as a function of wavelength for important components of materials and vegetation: crystalline quartz, calcite and cellulose.</p>
Full article ">Figure 3
<p>Comparison of the dimensionless parameter grain size times absorption coefficient estimated using Formula (23) for various mineral compounds (solid lines) with the fit (dashed lines) obtained using Equation (25). The fit parameters are given in the corresponding entries of <a href="#applsci-08-02680-t004" class="html-table">Table 4</a>. The spectral features seen in the experimental reflectance of the Trenton limestone sample are due to an interstitial chlorophyll-a residue lying on top of the limestone.</p>
Full article ">Figure 4
<p>Graph of the ratio of the specific absorption gain to the unsaturated absorption gain. The dotted lines are from the empirical formula of Bricaud et al. [<a href="#B9-applsci-08-02680" class="html-bibr">9</a>] for chlorophyll-a. The solid lines are from Equation (21) with the parameters noted.</p>
Full article ">Figure 5
<p>Fit of the mean backscatter distance derived from the irradiance reflectance of wet <span class="html-italic">Fucus</span> sp. and a drying mixture of <span class="html-italic">Fucus</span> sp. and <span class="html-italic">F. serratus</span> from Janvrin Island in Nova Scotia. In the zone below 0.90 microns the absorption of the chlorophyll-a and various pigments starts to dominate while in the zone above 1.35 microns the absorption of water becomes large enough that the resulting irradiance reflectance signal is dominated by noise. The full parameters of the fit are given in <a href="#applsci-08-02680-t005" class="html-table">Table 5</a>.</p>
Full article ">Figure 6
<p>Fit of the absorption spectrum of wet <span class="html-italic">Fucus</span> sp. and a drying mixture of <span class="html-italic">Fucus</span> sp. and <span class="html-italic">F. serratus</span> with the extended Bricaud model. Note the noise due to the instrumental signal to noise degradation in the blue wavelength range. This effect was compensated by weighing the fit function inversely proportional to the S/N. The blue and yellow curves are the derived spectrum from the reflectance measurements and Equation (29). The green and red curves are the fit using Equation (37).</p>
Full article ">Figure 7
<p>Comparison of modeled irradiance reflectance spectrum of wet <span class="html-italic">Fucus</span> sp. and a drying mixture of <span class="html-italic">Fucus</span> sp. and <span class="html-italic">F. serratus</span> (yellow curves) with the experimental measurements (blue curves). Note the noise degradation of the instrumental signal in the blue wavelength range. This reduced sensitivity may explain part of the incipient discrepancy in that spectral region.</p>
Full article ">Figure 8
<p>Computed variation of the spectral signature of translucent fucus vegetation over Trenton limestone as a function of the thickness of the layer. The mean spacing between scattering surfaces <math display="inline"><semantics> <mrow> <mo>〈</mo> <mi>d</mi> <mo>〉</mo> </mrow> </semantics></math> is 3.3 microns.</p>
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<p>Computed irradiance reflectance for wet limestone and beach sand. The spectral signature for dry limestone comes from the shore of Lake Ontario. The signature of dry sand comes from a beach in Santa Barbara.</p>
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6 pages, 977 KiB  
Article
Anomalous Light Scattering by Pure Seawater
by Xiaodong Zhang and Lianbo Hu
Appl. Sci. 2018, 8(12), 2679; https://doi.org/10.3390/app8122679 - 19 Dec 2018
Cited by 9 | Viewed by 3201
Abstract
The latest model for light scattering by pure seawater was used to investigate the anomalous behavior of pure water. The results showed that water exhibits a minimum scattering at 24.6 °C, as compared to the previously reported values of minimum scattering at 22 [...] Read more.
The latest model for light scattering by pure seawater was used to investigate the anomalous behavior of pure water. The results showed that water exhibits a minimum scattering at 24.6 °C, as compared to the previously reported values of minimum scattering at 22 °C or maximum scattering at 15 °C. The temperature corresponding to the minimum scattering also increases with the salinity, reaching 27.5 °C for S = 40 psu. Full article
(This article belongs to the Special Issue Outstanding Topics in Ocean Optics)
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Figure 1

Figure 1
<p>The temperature variations of light scattering by pure water, calculated using the Zhang and Hu [<a href="#B9-applsci-08-02679" class="html-bibr">9</a>] model (i.e., Equation (2)) at 436 and 546 nm and normalized by their respective values at 25 °C, are compared with the estimates using the Buiteveld, et al. [<a href="#B7-applsci-08-02679" class="html-bibr">7</a>] model and with the measurements by Cohen and Eisenberg [<a href="#B6-applsci-08-02679" class="html-bibr">6</a>]. Note that the normalized variations estimated by the Zhang and Hu model overlap with each other at the two wavelengths.</p>
Full article ">Figure 2
<p>Light scattering by pure seawater at 546 nm as a function of temperature and salinity. (<b>a</b>) <span class="html-italic">b<sub>d</sub></span>, the scattering due to density fluctuation; and (<b>b</b>) <span class="html-italic">b<sub>c</sub></span>, the scattering due to concentration fluctuation. Lines of progressive colors from blue to red correspond to different salinities from 0 to 40 psu, at 5 psu increments. The dotted line in each plot connects <span class="html-italic">T<sub>min</sub></span> at different salinities.</p>
Full article ">Figure 3
<p>Total scattering coefficient by pure seawater at 546 nm as a function of temperature and salinity. Lines of progressive colors from blue to red correspond to different salinities from 0 to 40 psu at 5 psu increments. The dotted line connects <span class="html-italic">T<sub>min</sub></span> at different salinities.</p>
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20 pages, 4404 KiB  
Article
Concentrations of Multiple Phytoplankton Pigments in the Global Oceans Obtained from Satellite Ocean Color Measurements with MERIS
by Guoqing Wang, Zhongping Lee and Colleen B. Mouw
Appl. Sci. 2018, 8(12), 2678; https://doi.org/10.3390/app8122678 - 19 Dec 2018
Cited by 15 | Viewed by 4161
Abstract
The remote sensing of chlorophyll a concentration from ocean color satellites has been an essential variable quantifying phytoplankton in the past decades, yet estimation of accessory pigments from ocean color remote sensing data has remained largely elusive. In this study, we validated the [...] Read more.
The remote sensing of chlorophyll a concentration from ocean color satellites has been an essential variable quantifying phytoplankton in the past decades, yet estimation of accessory pigments from ocean color remote sensing data has remained largely elusive. In this study, we validated the concentrations of multiple pigments (Cpigs) retrieved from in situ and MEdium Resolution Imaging Spectrometer (MERIS) measured remote sensing reflectance (Rrs(λ)) in the global oceans. A multi-pigment inversion model (MuPI) was used to semi-analytically retrieve Cpigs from Rrs(λ). With a set of globally optimized parameters, the accuracy of the retrievals obtained with MuPI is quite promising. Compared with High-Performance Liquid Chromatography (HPLC) measurements near Bermuda, the concentrations of chlorophyll a, b, c ([Chl-a], [Chl-b], [Chl-c]), photoprotective carotenoids ([PPC]), and photosynthetic carotenoids ([PSC]) can be retrieved from MERIS data with a mean unbiased absolute percentage difference of 38%, 78%, 65%, 36%, and 47%, respectively. The advantage of the MuPI approach is the simultaneous retrievals of [Chl-a] and the accessory pigments [Chl-b], [Chl-c], [PPC], [PSC] from MERIS Rrs(λ) based on a closure between the input and output Rrs(λ) spectra. These results can greatly expand scientific studies of ocean biology and biogeochemistry of the global oceans that are not possible when the only available information is [Chl-a]. Full article
(This article belongs to the Special Issue Outstanding Topics in Ocean Optics)
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Figure 1
<p>In situ data distribution, the (<b><span style="color:#0432FF">o</span></b>) are the stations for quantitative filter technique (QFT) <span class="html-italic">a</span><sub>ph</sub>(λ) from SeaBASS, (<b><span style="color:#FF2600">o</span></b>) and (<b><span style="color:#FF2600">+</span></b>) are the subset_1 and subset_2 stations of matchups of <span class="html-italic">a</span><sub>ph</sub>(λ) and HPLC from SeaBASS, (<b><span style="color:#00FA00">o</span></b>) is the HPLC location for BATS (Bermuda Atlantic Time-Series Study), (<b><span style="color:#FFFC00">o</span></b>) are the BIOSOPE <span class="html-italic">R</span><sub>rs</sub>(λ), <span class="html-italic">a</span><sub>ph</sub>(λ) and HPLC locations, (<b><span style="color:#00FDFF">o</span></b>) are locations of the <span class="html-italic">R</span><sub>rs</sub>(λ), <span class="html-italic">a</span><sub>ph</sub>(λ) and HPLC from VIIRS val/cal cruises in 2014 and 2015, and (<b><span style="color:#FF40FF">o</span></b>) are the locations of <span class="html-italic">R</span><sub>rs</sub>(λ), <span class="html-italic">a</span><sub>ph</sub>(λ) and HPLC from Tara Oceans expedition.</p>
Full article ">Figure 2
<p><span class="html-italic">a</span><sub>Gau</sub>(λ) estimated pigment concentrations versus the measured concentrations from HPLC using the <span class="html-italic">a</span><sub>ph</sub>(λ) and HPLC from SeaBASS: <b>A</b>: chlorophyll <span class="html-italic">a</span> (Chl-a), <b>B</b>: chlorophyll b (Chl-b), <b>C</b>: chlorophyll c (Chl-c), <b>D</b>: photoprotective carotenoids (PPC), and <b>E</b>: photosynthetic carotenoids (PSC).</p>
Full article ">Figure 3
<p><span class="html-italic">a</span><sub>Gau</sub>(λ) heights retrieved from <span class="html-italic">R</span><sub>rs</sub>(λ) at MEdium Resolution Imaging Spectrometer (MERIS) bands versus measured Gaussian peak (decomposed from <span class="html-italic">a</span><sub>ph</sub>(λ)) for the data from different datasets (shown in different colors).</p>
Full article ">Figure 4
<p>MuPI retrieved <span class="html-italic">a</span><sub>dg</sub>(440) and <span class="html-italic">b</span><sub>bp</sub>(440) versus those from the International Ocean-Colour Coordinating Group (IOCCG) dataset.</p>
Full article ">Figure 5
<p>Time series of pigment concentrations from BATS HPLC and MERIS <span class="html-italic">R</span><sub>rs</sub>(λ), and the determination coefficients (R<sup>2</sup>). <b>A</b>: Chl-a: chlorophyll <span class="html-italic">a</span>, <b>B</b>: Chl-b: chlorophyll <span class="html-italic">b</span>, <b>C</b>: Chl-c: chlorophyll <span class="html-italic">c</span>, <b>D</b>: PPC: photoprotective carotenoids, <b>E</b>: PSC: photosynthetic carotenoids, <b>F</b>: the scatterplot of estimated versus in situ pigment concentrations.</p>
Full article ">Figure 6
<p><b>A</b>: Time series of phytoplankton pigment to chlorophyll <span class="html-italic">a</span> (Chl-a) ratios at BATS from HPLC measurements. <b>B</b>: Chl-b, Chl-c, PPC and PSC to Chl-a ratios from HPLC versus from MERIS measured <span class="html-italic">R</span><sub>rs</sub>(λ) using MuPI.</p>
Full article ">Figure 7
<p>Global distributions of chlorophyll <span class="html-italic">a</span> concentration estimated from 2007 MERIS L3 <span class="html-italic">R</span><sub>rs</sub>(λ) imagery using NASA standard algorithm OC4E (<b>A</b>) and MuPI model (<b>B</b>). The locations (<b>o</b>) of in situ Chl-a and MERIS <span class="html-italic">R</span><sub>rs</sub>(λ) matchups for further comparison of OC4E and MuPI were plotted on the OC4E Chl-a map. <b>C</b>: Chlorophyll <span class="html-italic">a</span> concentration (Chl-a) from in situ measurements and from those estimated from matchups of MERIS <span class="html-italic">R</span><sub>rs</sub>(λ) using OC4E and MuPI algorithms with mean UAPD of 48.8% and RMSE of 4.51 mg∙m<sup>−3</sup> for OC4E and mean UAPD of 49.3% and RMSE of 4.05 mg∙m<sup>−3</sup> for MuPI.</p>
Full article ">Figure 8
<p>Global distributions of chlorophyll <span class="html-italic">b</span> (Chl-a), chlorophyll <span class="html-italic">c</span> (Chl-c), photoprotective carotenoids (PPC) and photosynthetic carotenoids (PSC) from 2007 L3 annual MERIS <span class="html-italic">R</span><sub>rs</sub>(λ) imagery.</p>
Full article ">Figure 9
<p>Global distributions of the accessory pigment to chlorophyll <span class="html-italic">a</span> ratios: ratio of concentrations of chlorophyll <span class="html-italic">b</span> (Chl-b/Chl-a), chlorophyll <span class="html-italic">c</span> (Chl-c/Chl-a), photoprotective carotenoids (PPC/Chl-a), and photosynthetic carotenoids to chlorophyll <span class="html-italic">a</span> (PSC/Chl-a).</p>
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33 pages, 1064 KiB  
Article
Characterization of the Light Field and Apparent Optical Properties in the Ocean Euphotic Layer Based on Hyperspectral Measurements of Irradiance Quartet
by Linhai Li, Dariusz Stramski and Mirosław Darecki
Appl. Sci. 2018, 8(12), 2677; https://doi.org/10.3390/app8122677 - 19 Dec 2018
Cited by 11 | Viewed by 3866
Abstract
Although the light fields and apparent optical properties (AOPs) within the ocean euphotic layer have been studied for many decades through extensive measurements and theoretical modeling, there is virtually a lack of simultaneous high spectral resolution measurements of plane and scalar downwelling and [...] Read more.
Although the light fields and apparent optical properties (AOPs) within the ocean euphotic layer have been studied for many decades through extensive measurements and theoretical modeling, there is virtually a lack of simultaneous high spectral resolution measurements of plane and scalar downwelling and upwelling irradiances (the so-called irradiance quartet). We describe a unique dataset of hyperspectral irradiance quartet, which was acquired under a broad range of environmental conditions within the water column from the near-surface depths to about 80 m in the Gulf of California. This dataset enabled the characterization of a comprehensive suite of AOPs for realistic non-uniform vertical distributions of seawater inherent optical properties (IOPs) and chlorophyll-a concentration (Chl) in the common presence of inelastic radiative processes within the water column, in particular Raman scattering by water molecules and chlorophyll-a fluorescence. In the blue and green spectral regions, the vertical patterns of AOPs are driven primarily by IOPs of seawater with weak or no discernible effects of inelastic processes. In the red, the light field and AOPs are strongly affected or totally dominated by inelastic processes of Raman scattering by water molecules, and additionally by chlorophyll-a fluorescence within the fluorescence emission band. The strongest effects occur in the chlorophyll-a fluorescence band within the chlorophyll-a maximum layer, where the average cosines of the light field approach the values of uniform light field, irradiance reflectance is exceptionally high approaching 1, and the diffuse attenuation coefficients for various irradiances are exceptionally low, including the negative values for the attenuation of upwelling plane and scalar irradiances. We established the empirical relationships describing the vertical patterns of some AOPs in the red spectral region as well as the relationships between some AOPs which can be useful in common experimental situations when only the downwelling plane irradiance measurements are available. We also demonstrated the applicability of irradiance quartet data in conjunction with Gershun’s equation for estimating the absorption coefficient of seawater in the blue-green spectral region, in which the effects of inelastic processes are weak or negligible. Full article
(This article belongs to the Special Issue Outstanding Topics in Ocean Optics)
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Figure 1
<p>Location of stations in the Gulf of California where underwater radiometric and ancillary measurements were made. Circles indicate the station sites in 2010 and squares in 2011. Optical measurements were made at four, three, and four stations in the Guaymas, Carmen, and Farallon Basins, respectively. Stars indicate two stations where the absorption and beam attenuation measurements with an ac-9 instrument were made in 2011.</p>
Full article ">Figure 2
<p>Vertical profiles of water temperature <span class="html-italic">T</span>, density anomaly <span class="html-italic">σ</span>, chlorophyll-<span class="html-italic">a</span> concentration <span class="html-italic">Chl</span>, and beam attenuation coefficient of particles at 660 nm <span class="html-italic">c<sub>p</sub></span>(660) measured at two contrasting stations. (<b>a</b>) Station in the Guaymas Basin (27.54° N, 111.64° W) with lower <span class="html-italic">Chl</span> and <span class="html-italic">c<sub>p</sub></span> and smaller solar zenith angle of ~5° and (<b>b</b>) station in the Farallon Basin (25.28° N, 109.58° W) with higher <span class="html-italic">Chl</span> and <span class="html-italic">c<sub>p</sub></span> and larger solar zenith angle of ~57°.</p>
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<p>The instrument package used to measure the irradiance quartet and upwelling radiance. As shown, the five radiometric sensors are aligned to the same depth level.</p>
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<p>Spectra and vertical profiles of the irradiance quartet, <span class="html-italic">E<sub>d</sub></span> (<b>a</b>,<b>e</b>), <span class="html-italic">E<sub>od</sub></span> (<b>b</b>,<b>f</b>), <span class="html-italic">E<sub>u</sub></span> (<b>c</b>,<b>g</b>), and <span class="html-italic">E<sub>ou</sub></span> (<b>d</b>,<b>h</b>), measured at the station in the Guaymas Basin shown in <a href="#applsci-08-02677-f002" class="html-fig">Figure 2</a>a. In panels (<b>a</b>–<b>d</b>) different colors represent the measurement depths as indicated in (<b>a</b>). In (<b>e</b>–<b>h</b>) different colors and symbols represent selected light wavelengths as indicated in (<b>e</b>).</p>
Full article ">Figure 5
<p>Spectra and vertical profiles of average cosines of underwater light field, <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>μ</mi> <mo>¯</mo> </mover> <mi>d</mi> </msub> </mrow> </semantics></math> (<b>a</b>,<b>e</b>), <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>μ</mi> <mo>¯</mo> </mover> <mi>u</mi> </msub> </mrow> </semantics></math> (<b>b</b>,<b>f</b>) and <math display="inline"><semantics> <mover accent="true"> <mi>μ</mi> <mo>¯</mo> </mover> </semantics></math> (<b>c</b>,<b>g</b>) as well as irradiance reflectance, <span class="html-italic">R</span> (<b>d</b>,<b>h</b>), derived from the irradiance quartet for Guaymas Basin shown in <a href="#applsci-08-02677-f004" class="html-fig">Figure 4</a>. In panels (<b>a</b>–<b>d</b>) different colors represent depths as indicated in (<b>a</b>). The black arrow in (<b>d</b>) indicates a spectral feature caused by Raman scattering of water molecules. In (<b>e</b>–<b>h</b>) different colors and symbols represent light wavelengths as indicated in (<b>e</b>).</p>
Full article ">Figure 6
<p>Spectra and vertical profiles of diffuse attenuation coefficients, <span class="html-italic">K<sub>d</sub></span> (<b>a</b>,<b>e</b>), <span class="html-italic">K<sub>od</sub></span> (<b>b</b>,<b>f</b>), <span class="html-italic">K<sub>u</sub></span> (<b>c</b>,<b>g</b>), and <span class="html-italic">K<sub>ou</sub></span> (<b>d</b>,<b>h</b>), derived from the irradiance quartet for Guaymas Basin in <a href="#applsci-08-02677-f004" class="html-fig">Figure 4</a>. In panels (<b>a</b>–<b>d</b>) different colors represent the mid-point depths of the layers within which the <span class="html-italic">K</span> coefficients were determined as indicated in panel (<b>a</b>). Dashed lines represent the spectrum of pure water absorption coefficient, which is a theoretical minimum of <span class="html-italic">K</span> for a hypothetical case when no inelastic processes and true emission sources are present. In (<b>e</b>–<b>h</b>) different colors and symbols represent the light wavelengths as indicated in panel (<b>e</b>) and dashed lines represent pure water absorption at respective wavelengths.</p>
Full article ">Figure 7
<p>Vertical profiles of average cosines, irradiance reflectance, and diffuse attenuation coefficients for the station in the Farallon Basin shown in <a href="#applsci-08-02677-f002" class="html-fig">Figure 2</a>b. Data for <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>μ</mi> <mo>¯</mo> </mover> <mi>d</mi> </msub> </mrow> </semantics></math> panel (<b>a</b>); <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>μ</mi> <mo>¯</mo> </mover> <mi>u</mi> </msub> </mrow> </semantics></math> (<b>b</b>); <math display="inline"><semantics> <mover accent="true"> <mi>μ</mi> <mo>¯</mo> </mover> </semantics></math> (<b>c</b>); <span class="html-italic">R</span> (<b>d</b>); <span class="html-italic">K<sub>d</sub></span> (<b>e</b>); and <span class="html-italic">K<sub>u</sub></span> (<b>f</b>) are shown for selected light wavelengths as indicated in panel (<b>a</b>).</p>
Full article ">Figure 8
<p>(<b>a</b>) Relationship between <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>μ</mi> <mo>¯</mo> </mover> <mi>d</mi> </msub> </mrow> </semantics></math> at selected light wavelengths as indicated and solar zenith angle <span class="html-italic">θ<sub>s</sub></span>. (<b>b</b>) Same as (<b>a</b>) but for <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>μ</mi> <mo>¯</mo> </mover> <mi>u</mi> </msub> </mrow> </semantics></math>. The presented data were collected at all stations within the top 10 m layer. For comparison, solid lines in (<b>a</b>,<b>b</b>) represent the relationship reported by Lewis et al. [<a href="#B29-applsci-08-02677" class="html-bibr">29</a>] and dashed lines by Aas and Højerslev [<a href="#B94-applsci-08-02677" class="html-bibr">94</a>]. Dotted line in (<b>a</b>) represents <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>μ</mi> <mo>¯</mo> </mover> <mi>d</mi> </msub> </mrow> </semantics></math> = cos(<span class="html-italic">θ<sub>sw</sub></span>) where <span class="html-italic">θ<sub>sw</sub></span> is the zenith angle of the refracted solar beam just beneath the ocean surface obtained from Snell’s law.</p>
Full article ">Figure 9
<p>Average cosines in the red spectral bands (650 and 683 nm) plotted as a function of depth <span class="html-italic">z</span> using data from all stations and depths. (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>μ</mi> <mo>¯</mo> </mover> <mi>d</mi> </msub> </mrow> </semantics></math>(<span class="html-italic">z</span>, 650) (crosses) and <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>μ</mi> <mo>¯</mo> </mover> <mi>d</mi> </msub> </mrow> </semantics></math> (<span class="html-italic">z</span>, 683) (open circles) vs. <span class="html-italic">z</span>. (<b>b</b>) Same as (<b>a</b>) but for <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>μ</mi> <mo>¯</mo> </mover> <mi>u</mi> </msub> </mrow> </semantics></math>. Data points surrounded by dotted lines in (<b>a</b>) are for the station in the Farallon Basin with the solar zenith angle significantly larger (~57°) than at other stations. Solid lines show the best-fit regression functions for the average cosines vs. depth.</p>
Full article ">Figure 10
<p>Relationships between average cosines based on measurements from all stations and depths. Data for (<b>a</b>) <math display="inline"><semantics> <mover accent="true"> <mi>μ</mi> <mo>¯</mo> </mover> </semantics></math> vs. <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>μ</mi> <mo>¯</mo> </mover> <mi>d</mi> </msub> </mrow> </semantics></math> and (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>μ</mi> <mo>¯</mo> </mover> <mi>u</mi> </msub> </mrow> </semantics></math> vs. <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>μ</mi> <mo>¯</mo> </mover> <mi>d</mi> </msub> </mrow> </semantics></math> are shown for selected light wavelengths as indicated in panel (<b>a</b>). In (<b>a</b>) data points for the blue and green (440 nm and 550 nm) were combined to determine the best-fit regression function (blue line) and data points for the red (650 nm and 683 nm) were combined to determine the best-fit regression function (red line).</p>
Full article ">Figure 11
<p>Irradiance reflectance <span class="html-italic">R</span> in the red spectral bands (650 and 683 nm as indicated) plotted as a function of depth <span class="html-italic">z</span> using data from all stations and depths. Solid lines represent the best-fit regression functions.</p>
Full article ">Figure 12
<p>Diffuse attenuation coefficients in the red spectral bands (650 and 683 nm) plotted as a function of depth <span class="html-italic">z</span> using data from all stations and depths. (<b>a</b>) <span class="html-italic">K<sub>d</sub></span>(<span class="html-italic">z</span>, 650) (crosses) and <span class="html-italic">K<sub>d</sub></span>(<span class="html-italic">z</span>, 683) (open circles) vs. <span class="html-italic">z</span>. (<b>b</b>) Same as (<b>a</b>) but for <span class="html-italic">K</span><sub>u</sub>. Solid lines represent the best-fit regression functions.</p>
Full article ">Figure 13
<p>Relationships between diffuse attenuation coefficients based on measurements from all stations and depths. (<b>a</b>) <span class="html-italic">K<sub>u</sub></span> vs. <span class="html-italic">K<sub>d</sub></span>; (<b>b</b>) <span class="html-italic">K<sub>E</sub></span> vs. <span class="html-italic">K<sub>d</sub></span>; (<b>c</b>) <span class="html-italic">K<sub>o</sub></span> vs. <span class="html-italic">K<sub>d</sub></span>. (<b>d</b>) <span class="html-italic">K<sub>dPAR</sub></span> vs. <span class="html-italic">K<sub>d</sub></span>; (<b>e</b>) <span class="html-italic">K<sub>oPAR</sub></span> vs. <span class="html-italic">K<sub>dPAR</sub></span>; and (<b>f</b>) <span class="html-italic">K<sub>u</sub></span> vs. <span class="html-italic">K<sub>Lu</sub></span>. In (<b>a</b>–<b>c</b>,<b>f</b>), different symbols represent data at different light wavelengths as indicated. In (<b>d</b>) symbols indicate the relationships between <span class="html-italic">K<sub>oPAR</sub></span>(<span class="html-italic">z</span>) and different <span class="html-italic">K</span>-coefficients as indicated, including the relationship between <span class="html-italic">K<sub>oPAR</sub></span>(<span class="html-italic">z</span>) and [<span class="html-italic">K<sub>d</sub></span>(<span class="html-italic">z</span>, 490) + <span class="html-italic">K<sub>d</sub></span>(<span class="html-italic">z</span>, 550)]/2. The best fit regression functions (solid lines) are shown for (<b>a</b>) data at 440 nm and 550 nm analyzed separately, (<b>b</b>,<b>c</b>) combined data at 440 nm, 550 nm, and 650 nm, (<b>d</b>) data of <span class="html-italic">K<sub>oPAR</sub></span>(<span class="html-italic">z</span>) vs. [<span class="html-italic">K<sub>d</sub></span>(<span class="html-italic">z</span>, 490) + <span class="html-italic">K<sub>d</sub></span>(<span class="html-italic">z</span>, 550)]/2, and (<b>e</b>,<b>f</b>) all data displayed in these panels. Dashed lines represent the 1:1 agreement between the variables.</p>
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<p>Comparison of the absorption coefficient of seawater measured with an ac-9 instrument (<span class="html-italic">a<sub>ac9</sub></span>) and estimated from AOPs using the irradiance quartet measurements and Gershun’s equation (<span class="html-italic">a<sub>AOP</sub></span>). (<b>a</b>) Spectra of absorption coefficient at depths for which the determinations of <span class="html-italic">K<sub>E</sub></span> were made on the basis of radiometric measurements taken at one station in the Carmen Basin where ac-9 measurements were made during the 2011 cruise. The vertical dotted line indicates the transition region to long-wavelength portion of the spectrum where the use of Gershun’s is inadequate because of the effects of inelastic processes. (<b>b</b>) Vertical profiles of absorption coefficient measured with the ac-9 instrument (solid lines) and estimated from AOPs (dashed lines) at selected light wavelengths as indicated. Data were collected at the same station as in panel (<b>a</b>). (<b>c</b>) Direct comparison between <span class="html-italic">a<sub>AOP</sub></span> and <span class="html-italic">a<sub>ac9</sub></span> at selected blue and green spectral bands as indicated. Data were collected at one station in the Guaymas Basin and one station in the Carmen Basin where ac-9 measurements were made during the 2011 cruise. (<b>d</b>) Relationship between <span class="html-italic">K<sub>E</sub></span>·<math display="inline"><semantics> <mover accent="true"> <mi>μ</mi> <mo>¯</mo> </mover> </semantics></math> and <span class="html-italic">K<sub>d</sub></span>·<math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>μ</mi> <mo>¯</mo> </mover> <mi>d</mi> </msub> </mrow> </semantics></math> for the blue-green spectral regions based on data at the three selected light wavelengths as indicated. Solid lines in (<b>c</b>,<b>d</b>) represent the best-fit regression functions and dashed lines the 1:1 agreement between the variables.</p>
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19 pages, 3692 KiB  
Article
Estimation of Suspended Matter, Organic Carbon, and Chlorophyll-a Concentrations from Particle Size and Refractive Index Distributions
by Jacopo Agagliate, Rüdiger Röttgers, Kerstin Heymann and David McKee
Appl. Sci. 2018, 8(12), 2676; https://doi.org/10.3390/app8122676 - 19 Dec 2018
Cited by 4 | Viewed by 4163
Abstract
Models of particle density and of organic carbon and chlorophyll-a intraparticle concentration were applied to particle size distributions and particle real refractive index distributions determined from flow cytometry measurements of natural seawater samples from a range of UK coastal waters. The models allowed [...] Read more.
Models of particle density and of organic carbon and chlorophyll-a intraparticle concentration were applied to particle size distributions and particle real refractive index distributions determined from flow cytometry measurements of natural seawater samples from a range of UK coastal waters. The models allowed for the estimation of suspended particulate matter, organic suspended matter, inorganic suspended matter, particulate organic carbon, and chlorophyll-a concentrations. These were then compared with independent measurements of each of these parameters. Particle density models were initially applied to a simple spherical model of particle volume, but generally overestimated independently measured values, sometimes by over two orders of magnitude. However, when the same density models were applied to a fractal model of particle volume, successful agreement was reached for suspended particulate matter and both inorganic and organic suspended matter values (RMS%E: 57.4%, 148.5%, and 83.1% respectively). Non-linear organic carbon and chlorophyll-a volume scaling models were also applied to a spherical model of particle volume, and after an optimization procedure achieved successful agreement with independent measurements of particulate organic carbon and chlorophyll-a concentrations (RMS%E: 45.6% and 51.8% respectively). Refractive index-based models of carbon and chlorophyll-a intraparticle concentration were similarly tested, and were also found to require a fractal model of particle volume to achieve successful agreement with independent measurements, producing RMS%E values of 50.2% and 45.2% respectively after an optimization procedure. It is further shown that the non-linear exponents of the volume scaling models are mathematically equivalent to the fractal dimensionality coefficients that link cell volume to mass concentration, reflecting the impact of non-uniform distribution of intracellular carbon within cells. Fractal models of particle volume are thus found to be essential to successful closure between results provided by models of particle mass, intraparticle carbon and chlorophyll content, and bulk measurements of suspended mass and total particulate carbon and chlorophyll when natural mixed particle populations are concerned. The results also further confirm the value of determining both size and refractive index distributions of natural particle populations using flow cytometry. Full article
(This article belongs to the Special Issue Outstanding Topics in Ocean Optics)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Track of the He442 research cruise, which took place in April 2015 in UK coastal waters aboard R/V Heincke. Out of the 62 measurement stations visited a total of 50 complete sets of data were retrieved, matching flow cytometric data, and ancillary measurements (blue circles). Yellow circles denote stations where two samples were taken. The figure was adapted and modified with permission from <a href="#applsci-08-02676-f001" class="html-fig">Figure 1</a> of Agagliate et al. [<a href="#B2-applsci-08-02676" class="html-bibr">2</a>].</p>
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<p>Power law best fit and real refractive index approximation in a typical natural particle population sample. Independent <span class="html-italic">n<sub>r</sub></span> values obtained by averaging the <span class="html-italic">n<sub>r</sub></span> of particles at the extremes of the PSD (<math display="inline"> <semantics> <mrow> <msub> <mover accent="true"> <mi>n</mi> <mo>¯</mo> </mover> <mrow> <mi>r</mi> <mo>,</mo> <mn>1</mn> </mrow> </msub> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <msub> <mover accent="true"> <mi>n</mi> <mo>¯</mo> </mover> <mrow> <mi>r</mi> <mo>,</mo> <mn>2</mn> </mrow> </msub> </mrow> </semantics> </math>) were used to approximate the real refractive index within the respective ends of the PSD extrapolation (dotted line). The figure was adapted and modified with permission from <a href="#applsci-08-02676-f003" class="html-fig">Figure 3</a> of Agagliate et al. [<a href="#B2-applsci-08-02676" class="html-bibr">2</a>].</p>
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<p>Collective view of (<b>a</b>) all 50 UKCW PSDs and (<b>b</b>) UKCW PRIDs produced by the FC method. Note that real refractive index values above 1.15 are not precise, but still indicate high refractive indices [<a href="#B1-applsci-08-02676" class="html-bibr">1</a>]. (<b>c</b>) Total, organic, inorganic, and fluorescent PSDs for a typical sample of the UKCW dataset and (<b>d</b>) power law extension of the total, organic and inorganic PSDs. Note that the extended organic and inorganic PSDs intersect the extended total PSD; therefore the sum of the extended organic and inorganic PSDs is not exactly equal to the extended total SPM. To evaluate the error thus introduced, SPM values are modelled both from the total PSD and by summing model ISM and OSM values. Panels (<b>a</b>,<b>b</b>) of the figure were adapted and modified with permission from <a href="#applsci-08-02676-f002" class="html-fig">Figure 2</a> of Agagliate et al. [<a href="#B2-applsci-08-02676" class="html-bibr">2</a>].</p>
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<p>Comparison of modelled vs. measured (<b>a</b>) SPM, (<b>b</b>) ISM, and (<b>c</b>) OSM values for a simple spherical volume model. SPM values derived from the total PSD are represented as dark grey squares, while SPM values calculated as the sum of ISM and OSM are represented as light grey diamonds. The dashed grey lines indicate the 1:1 relationship.</p>
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<p>Comparison of modelled vs. measured (<b>a</b>) SPM, (<b>b</b>) ISM, and (<b>c</b>) OSM values for the fractal volume model. SPM values derived from the total PSD are represented as dark grey squares, while SPM values calculated as the sum of ISM and OSM are represented as light grey diamonds. The dashed grey lines indicate the 1:1 relationship.</p>
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<p>Cumulative distributions of modelled (<b>a</b>) SPM, (<b>b</b>) ISM, and (<b>c</b>) OSM values for the fractal volume model. The SPM curves refer to SPM values calculated from the total PSD. Solid, dashed, and dotted lines represent median, upper/lower quartiles, and maximum/minimum values respectively. The light grey horizontal lines mark the middle 90% of the contribution (i.e., from 5% to 95%) to the total value of each parameter.</p>
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<p>(<b>a</b>) Comparison of modelled vs. measured POC. POC values calculated using the diatom model (Menden-Deuer &amp; Lessard, 2000) are represented by dark grey squares; the RMS%E value refers to these. POC values calculated using the other three models are represented by light grey diamonds and triangles; (<b>b</b>) Cumulative distribution of modelled POC for the diatom model. Solid, dashed, and dotted lines represent median, upper/lower quartiles, and max./min. values respectively. The light grey horizontal lines mark the middle 90% of the contribution (i.e., from 5% to 95%) to the total POC value. The dashed grey line indicates the 1:1 relationship.</p>
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<p>Comparison of modelled vs. measured ChlA. ChlA values calculated using the Montagnes et al. [<a href="#B11-applsci-08-02676" class="html-bibr">11</a>] model and the Álvarez et al. [<a href="#B13-applsci-08-02676" class="html-bibr">13</a>] model are represented by dark grey squares and light grey diamonds respectively. The dashed grey line indicates the 1:1 relationship.</p>
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<p>Comparison of (<b>a</b>) modelled vs. measured POC and (<b>b</b>) modelled vs. measured ChlA when a spherical model of particle volume is employed, and comparison of (<b>c</b>) modelled vs. measured POC and (<b>d</b>) modelled vs. measured ChlA when a fractal model of particle volume is employed instead. POC and ChlA values calculated using the Stramski [<a href="#B19-applsci-08-02676" class="html-bibr">19</a>] and Durand et al. [<a href="#B20-applsci-08-02676" class="html-bibr">20</a>] models are represented by dark grey squares and light grey diamonds respectively. The dashed grey lines indicate the 1:1 relationship.</p>
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<p>Comparison of (<b>a</b>) POC and (<b>b</b>) ChlA values as determined by the optimized models of Equations (13) and (14) vs. their respective measured values. The dashed grey lines indicate the 1:1 relationship.</p>
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<p>Comparison of (<b>a</b>) POC and (<b>b</b>) ChlA values as determined by the optimized models of Equations (17) and (18) vs. their respective measured values. The dashed grey lines indicate the 1:1 relationship.</p>
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19 pages, 4420 KiB  
Article
Control Requirements for Future Gas Turbine-Powered Unmanned Drones: JetQuads
by Soheil Jafari, Seyed Alireza Miran Fashandi and Theoklis Nikolaidis
Appl. Sci. 2018, 8(12), 2675; https://doi.org/10.3390/app8122675 - 19 Dec 2018
Cited by 6 | Viewed by 5564
Abstract
The next generation of aerial robots will be utilized extensively in real-world applications for different purposes: Delivery, entertainment, inspection, health and safety, photography, search and rescue operations, fire detection, and use in hazardous and unreachable environments. Thus, dynamic modeling and control of drones [...] Read more.
The next generation of aerial robots will be utilized extensively in real-world applications for different purposes: Delivery, entertainment, inspection, health and safety, photography, search and rescue operations, fire detection, and use in hazardous and unreachable environments. Thus, dynamic modeling and control of drones will play a vital role in the growth phase of this cutting-edge technology. This paper presents a systematic approach for control mode identification of JetQuads (gas turbine-powered quads) that should be satisfied simultaneously to achieve a safe and optimal operation of the JetQuad. Using bond graphs as a powerful mechatronic tool, a modular model of a JetQuad including the gas turbine, electric starter, and the main body was developed and validated against publicly available data. Two practical scenarios for thrust variation as a function of time were defined to investigate the compatibility and robustness of the JetQuad. The simulation results of these scenarios confirmed the necessity of designing a compatibility control loop, a stability control loop, and physical limitation control loops for the safe and errorless operation of the system. A control structure with its associated control algorithm is also proposed to deal with future challenges in JetQuad control problems. Full article
(This article belongs to the Special Issue Gas Turbine Engine - towards the Future of Power)
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Figure 1

Figure 1
<p>Main structure of a JetQuad (JQ) [<a href="#B13-applsci-08-02675" class="html-bibr">13</a>].</p>
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<p>Schematic of the JetQuad jet engines and the developed bond graph model.</p>
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<p>Numerical solution procedure for the modeling of jet engines.</p>
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<p>Electric starter, circuit, and the bond graph model [<a href="#B14-applsci-08-02675" class="html-bibr">14</a>].</p>
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<p>Validation of the starter motor performance model [<a href="#B16-applsci-08-02675" class="html-bibr">16</a>].</p>
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<p>AB4 JetQuad bond graph model: (<b>a</b>) Bond graph model of jet engine; (<b>b</b>) bond graph representation of Euler equations; (<b>c</b>) combined bond graph model of the JQ.</p>
Full article ">Figure 6 Cont.
<p>AB4 JetQuad bond graph model: (<b>a</b>) Bond graph model of jet engine; (<b>b</b>) bond graph representation of Euler equations; (<b>c</b>) combined bond graph model of the JQ.</p>
Full article ">Figure 6 Cont.
<p>AB4 JetQuad bond graph model: (<b>a</b>) Bond graph model of jet engine; (<b>b</b>) bond graph representation of Euler equations; (<b>c</b>) combined bond graph model of the JQ.</p>
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<p>Prescribed variations of thrust in the compatibility scenario.</p>
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<p>Variation of displacements in the compatibility scenario.</p>
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<p>(<b>a</b>) Variations in linear velocities, (<b>b</b>) angular velocities, (<b>c</b>) linear accelerations, and (<b>d</b>) angular accelerations of the JQ in the compatibility scenario.</p>
Full article ">Figure 9 Cont.
<p>(<b>a</b>) Variations in linear velocities, (<b>b</b>) angular velocities, (<b>c</b>) linear accelerations, and (<b>d</b>) angular accelerations of the JQ in the compatibility scenario.</p>
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<p>Prescribed variations of thrust in the robustness scenario.</p>
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<p>Variation of displacements in the robustness scenario.</p>
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<p>Variations in linear velocity in the robustness scenario.</p>
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<p>Variations in linear acceleration in the robustness scenario.</p>
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<p>Variations in angular velocity in the robustness scenario.</p>
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<p>Proposed control structure for the JetQuad.</p>
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11 pages, 266 KiB  
Article
Estimation of Association between Healthcare System Efficiency and Policy Factors for Public Health
by Seunggyu Lee and Changhee Kim
Appl. Sci. 2018, 8(12), 2674; https://doi.org/10.3390/app8122674 - 19 Dec 2018
Cited by 20 | Viewed by 5468
Abstract
Objective: To assess the association between the healthcare system’s efficiency and policy factors (the types of healthcare systems and various health policy indicators). Methods: In this study, a data envelopment analysis (DEA) with bootstrapping was applied to the healthcare system’s efficiency to correct [...] Read more.
Objective: To assess the association between the healthcare system’s efficiency and policy factors (the types of healthcare systems and various health policy indicators). Methods: In this study, a data envelopment analysis (DEA) with bootstrapping was applied to the healthcare system’s efficiency to correct the bias of efficiency scores and to rank countries appropriately. We analyzed data mainly from the OECD (Organization for Economic Co-operation and Development) Health Data from 2014. After obtaining the efficiency score result, we analyzed which policy factor caused the inefficiency of the healthcare system by Tobit Regression. Results: Based on five types of healthcare system classification, the result suggested that the social health insurance (e.g., Austria, Germany, Switzerland) showed the lowest efficiency score on average when compared to other types of systems, but evidence of a statistically significant difference in healthcare efficiency among four types of healthcare systems was not found. It was shown that the pure technological efficiency of the healthcare system was negatively influenced by two main factors: user choice for basic insurance coverage and degree of decentralization to sub-national governments. Conclusions: Our findings suggest that countries with relatively low healthcare system efficiency may learn from countries that implement policies related to a low level of user choice and a high level of centralization to achieve more economical allocation of their healthcare resources. Full article
(This article belongs to the Special Issue Data Analytics in Smart Healthcare)
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