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Recent Progress in Remote Sensing of Terrestrial and Aquatic Fluorescence
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Recent Progress in Remote Sensing of Terrestrial and Aquatic Fluorescence

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (31 October 2019) | Viewed by 30493

Special Issue Editors

Prof. Dr. Alexander Damm

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Guest Editor
Department of Geography, University of Zurich, Winterthurerstrasse, 1908057 Zurich, Switzerland
Interests: fluorescence spectroscopy; remote sensing of vegetation; plant–water relations; carbon and water cycle; plant photosynthesis; ecosystem functioning and environmental change
Special Issues, Collections and Topics in MDPI journals
Prof. Uwe Rascher

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Guest Editor
IBG-2: Plant Sciences, Institute of Bio- und Geosciences, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
Interests: ecophysiology of photosynthesis; plant stress physiology; field phenotyping; optical remote sensing; understanding of sun-induced fluorescence; high-resolution imaging spectroscopy
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Roberto Colombo

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Guest Editor
Department of Environmental Sciences, University of Milano-Bicocca, 20126 Milano, Italy
Interests: remote sensing; imaging spectroscopy; sun-induced chlorophyll fluorescence; land surface modelling; environmental monitoring; calibration/validation field campaigns
Dr. Dirk Schuettemeyer

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Guest Editor
European Space Research and Technology Centre, European Space Agency, 2201 Noordwijk, The Netherlands
Interests: boundary layer meteorology; numerical weather prediction; remote sensing; fluorescence; calibration/validation field campaigns
Dr. Matthias Drusch

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Guest Editor
Mission Science Division (EOP-SME) European Space Agency, ESTEC, Directorate of Earth Observation Programmes Postbus 299, 2200 AG Noordwijk, The Netherlands
Interests: land surface hydrology; data assimilation; numerical weather forecasting; remote sensing; space system engineering; fluorescence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the Fluorescence Explorer (FLEX)—Sentinel-3 tandem mission in its implementation phase and fluorescence measurements becoming increasingly available from missions like OCO-2 and Sentinel-5p, remote sensing of fluorescence has been established as important and complementary Earth observation resource to assess complex processes in terrestrial and aquatic ecosystems.

The meeting of the International Network on Remote Sensing of Terrestrial and Aquatic Fluorescence, held in Davos, Switzerland from 5th to 8th March 2019 follows a series of events addressing the remote sensing of vegetation fluorescence and will focus on the latest developments of this topic.

As a follow-up to this event, we are calling for papers on the work presented at the meeting. In addition to this, we welcome papers from the global research community actively involved in research involving fluorescence remote sensing. As such, the Special Issue is open to anyone doing research in this field. The selection of papers for publication will depend on quality and rigor of research. Specific topics include but are not limited to the following:

  • Emerging passive and active sensors (field, airborne, and satellite);
  • Sun-induced chlorophyll fluorescence retrieval methods over land and water;
  • Retrieval algorithms of biophysical parameters (including the future FLEX and Sentinel-3 tandem mission concept);
  • Modelling chlorophyll fluorescence emission;
  • Combining models and observations across spatial and temporal scales;
  • Analyses and results from Sentinel-3 measurements (including the tandem phase of the Sentinel-3 A and B units in summer 2018);
  • Validation activities, field studies, and campaigns related to land and water fluorescence studies;
  • Applications of fluorescence in stress detection in agriculture and forestry;
  • Linking fluorescence and ecosystem processes including gross primary productivity and transpiration;
  • Fluorescence for aquatic research over coastal and inland waters.

Prof. Alexander Damm
Prof. Uwe Rascher
Prof. Roberto Colombo
Dr. Dirk Schuettemeyer
Dr. Matthias Drusch
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Published Papers (5 papers)

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Research

30 pages, 17721 KiB  
Article
The High-Performance Airborne Imaging Spectrometer HyPlant—From Raw Images to Top-of-Canopy Reflectance and Fluorescence Products: Introduction of an Automatized Processing Chain
by Bastian Siegmann, Luis Alonso, Marco Celesti, Sergio Cogliati, Roberto Colombo, Alexander Damm, Sarah Douglas, Luis Guanter, Jan Hanuš, Kari Kataja, Thorsten Kraska, Maria Matveeva, Jóse Moreno, Onno Muller, Miroslav Pikl, Francisco Pinto, Juan Quirós Vargas, Patrick Rademske, Fernando Rodriguez-Morene, Neus Sabater, Anke Schickling, Dirk Schüttemeyer, František Zemek and Uwe Rascheradd Show full author list remove Hide full author list
Remote Sens. 2019, 11(23), 2760; https://doi.org/10.3390/rs11232760 - 23 Nov 2019
Cited by 60 | Viewed by 7337
Abstract
The HyPlant imaging spectrometer is a high-performance airborne instrument consisting of two sensor modules. The DUAL module records hyperspectral data in the spectral range from 400–2500 nm, which is useful to derive biochemical and structural plant properties. In parallel, the FLUO module acquires [...] Read more.
The HyPlant imaging spectrometer is a high-performance airborne instrument consisting of two sensor modules. The DUAL module records hyperspectral data in the spectral range from 400–2500 nm, which is useful to derive biochemical and structural plant properties. In parallel, the FLUO module acquires data in the red and near infrared range (670–780 nm), with a distinctly higher spectral sampling interval and finer spectral resolution. The technical specifications of HyPlant FLUO allow for the retrieval of sun-induced chlorophyll fluorescence (SIF), a small signal emitted by plants, which is directly linked to their photosynthetic efficiency. The combined use of both HyPlant modules opens up new opportunities in plant science. The processing of HyPlant image data, however, is a rather complex procedure, and, especially for the FLUO module, a precise characterization and calibration of the sensor is of utmost importance. The presented study gives an overview of this unique high-performance imaging spectrometer, introduces an automatized processing chain, and gives an overview of the different processing steps that must be executed to generate the final products, namely top of canopy (TOC) radiance, TOC reflectance, reflectance indices and SIF maps. Full article
(This article belongs to the Special Issue Recent Progress in Remote Sensing of Terrestrial and Aquatic Fluorescence)
►▼ Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>HyPlant airborne imaging spectrometer: (<b>a</b>) installation of the sensor system in the aircraft, consisting of the broadband DUAL module (A), high-resolution FLUO module (B) and GPS/INS unit (C); (<b>b</b>) HyPlant DUAL (A) and FLUO (B) module installed in the hatch of the aircraft (image taken from below the aircraft); (<b>c</b>) HyPlant FLUO at-sensor radiance; (<b>d</b>) HyPlant DUAL at-sensor radiance; (<b>e</b>) HyPlant DUAL top-of-canopy radiance; (<b>f</b>) HyPlant DUAL top-of-canopy reflectance of selected surfaces.</p> Full article ">Figure 2
<p>Flow chart of the first cluster of the HyPlant processing chain, providing information on the data streams of both modules and associated calibration files for the subsequent processing clusters.</p> Full article ">Figure 3
<p>Flow chart of the second cluster of the HyPlant processing chain, presenting the entire workflow involved in processing HyPlant DUAL data, from raw images to final products.</p> Full article ">Figure 4
<p>Flow chart of the third cluster of the HyPlant processing chain, presenting the entire workflow involved in processing HyPlant FLUO data, from raw images to non-deconvolved/deconvolved at-sensor radiance.</p> Full article ">Figure 5
<p>PSFs determined for the HyPlant FLUO module, located close to the oxygen absorption, features O<sub>2</sub>-A and O<sub>2</sub>-B in the center of the sensor array: the graphs on the left (<b>A</b>) show the PSF at 680 nm, and the graphs on the right (<b>B</b>) at 760 nm. Graphs 1–4 illustrate the PSFs from different perspectives. While Graphs 1 depict the top view of both PSFs, Graphs 2 are three-dimensional (3D) representations. Graphs 3 and 4 illustrate cross-sections of the PSFs in the spatial and spectral domain.</p> Full article ">Figure 6
<p>Spectral characteristics of the HyPlant FLUO module in the O<sub>2</sub>-A (right) and the O<sub>2</sub>-B absorption feature (left). (<b>a</b>) figures show the at-sensor-radiance of green vegetation with the characteristic absorption features located at 687 and 760 nm. (<b>b</b>) and (<b>c</b>) illustrate the fine spectral resolution (FWHM) and the high signal-to-noise ratio (SNR) of the FLUO module in the same spectral range.</p> Full article ">Figure 7
<p>Flow chart of the fourth cluster of the HyPlant processing chain, presenting the workflow involved in processing HyPlant FLUO data from non-deconvolved/deconvolved at-sensor radiance to the final SIF products.</p> Full article ">Figure 8
<p>Results of the HyPlant DUAL, showing a part of the agricultural research station Campus Klein-Altendorf, acquired on 29 June 2018: (<b>a</b>) true-color composite (RGB 640/550/460 nm) of the TOC reflectance of HyPlant DUAL; (<b>b</b>) NDVI map calculated from HyPlant DUAL TOC reflectance data; (<b>c</b>) cPRI map calculated from HyPlant DUAL TOC reflectance data; (<b>d</b>) WBI map calculated from HyPlant DUAL TOC reflectance data.</p> Full article ">Figure 9
<p>SIF maps derived for the O<sub>2</sub>–A absorption feature, showing a part of the agricultural research station Campus Klein-Altendorf, acquired on 29 June 2018 and corresponding pixel value distributions for vegetation and soil pixels: (<b>a</b>) results of the SVD method; (<b>b</b>) results of the iFLD method; (<b>c</b>) results of the SFM method; (<b>d</b>) results of the NA method.</p> Full article ">Figure 10
<p>SIF maps derived for the O<sub>2</sub>–B absorption feature, showing a part of the agricultural research station at Campus Klein-Altendorf, acquired on 29 June 2018 and corresponding pixel value distributions for vegetation and soil pixels: (<b>a</b>) results of the SVD method; (<b>b</b>) results of the iFLD method; (<b>c</b>) results of the SFM method.</p> Full article ">Figure A1
<p>Overview of the HyPlant processing chain consisting of the four processing cluster.</p> Full article ">
22 pages, 3399 KiB  
Article
Nitrogen and Phosphorus Effect on Sun-Induced Fluorescence and Gross Primary Productivity in Mediterranean Grassland
by David Martini, Javier Pacheco-Labrador, Oscar Perez-Priego, Christiaan van der Tol, Tarek S. El-Madany, Tommaso Julitta, Micol Rossini, Markus Reichstein, Rune Christiansen, Uwe Rascher, Gerardo Moreno, M. Pilar Martín, Peiqi Yang, Arnaud Carrara, Jinhong Guan, Rosario González-Cascón and Mirco Migliavacca
Remote Sens. 2019, 11(21), 2562; https://doi.org/10.3390/rs11212562 - 31 Oct 2019
Cited by 23 | Viewed by 5183
Abstract
Sun-Induced fluorescence at 760 nm (F760) is increasingly being used to predict gross primary production (GPP) through light use efficiency (LUE) modeling, even though the mechanistic processes that link the two are not well understood. We analyzed the effect of nitrogen [...] Read more.
Sun-Induced fluorescence at 760 nm (F760) is increasingly being used to predict gross primary production (GPP) through light use efficiency (LUE) modeling, even though the mechanistic processes that link the two are not well understood. We analyzed the effect of nitrogen (N) and phosphorous (P) availability on the processes that link GPP and F760 in a Mediterranean grassland manipulated with nutrient addition. To do so, we used a combination of process-based modeling with Soil-Canopy Observation of Photosynthesis and Energy (SCOPE), and statistical analyses such as path modeling. With this study, we uncover the mechanisms that link the fertilization-driven changes in canopy nitrogen concentration (N%) to the observed changes in F760 and GPP. N addition changed plant community structure and increased canopy chlorophyll content, which jointly led to changes in photosynthetic active radiation (APAR), ultimately affecting both GPP and F760. Changes in the abundance of graminoids, (%graminoids) driven by N addition led to changes in structural properties of the canopy such as leaf angle distribution, that ultimately influenced observed F760 by controlling the escape probability of F760 (Fesc). In particular, we found a change in GPP–F760 relationship between the first and the second year of the experiment that was largely driven by the effect of plant type composition on Fesc, whose best predictor is %graminoids. The P addition led to a statistically significant increase on light use efficiency of fluorescence emission (LUEf), in particular in plots also with N addition, consistent with leaf level studies. The N addition induced changes in the biophysical properties of the canopy that led to a trade-off between surface temperature (Ts), which decreased, and F760 at leaf scale (F760leaf,fw), which increased. We found that Ts is an important predictor of the light use efficiency of photosynthesis, indicating the importance of Ts in LUE modeling approaches to predict GPP. Full article
(This article belongs to the Special Issue Recent Progress in Remote Sensing of Terrestrial and Aquatic Fluorescence)
►▼ Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Energy partitioning at the leaf and canopy level representing the processes involved in the photosynthetic light use efficiency model (GPP = APAR x LUE<sub>p</sub>) and fluorescence light use efficiency model (F<sub>760</sub> = APAR *LUE<sub>f</sub> * Fesc) are represented with solid arrows. Dotted arrows represent the hypothesized relationship between leaf traits, canopy structure and the various processes related to the allocation of energy and transfer of SIF within the canopy. Photosynthetic active radiation (PAR); absorbed (by vegetation) photosynthetic active radiation (APAR); PAR absorbed by chlorophyll a and b molecules (APAR<sub>green</sub>), represented as the green bar in the equations on both sides of the figure; gross primary production (GPP); sun-induced fluorescence emitted by all leaves at 760 nm (F<sub>760leaf</sub>); sun-induced fluorescence at 760 nm observed at top of canopy (F<sub>760</sub>); nitrogen concentration on a mass basis (N%); chlorophyll a and b on a mass basis (Cab); leaf mass per area (LMA); maximum carboxylation rate (Vcmax); leaf area index (LAI); leaf angle distribution (LAD).</p> Full article ">Figure 2
<p>Bar graphs representing differences among treatments (control treatment, C; nitrogen treatment, N; nitrogen and phosphorus treatment, NP; and control treatment, C) of Gross Primary Production (GPP) in 2014 (<b>a</b>) and 2015 (<b>b</b>); light use efficiency of photosynthesis (LUE<sub>p</sub>) in 2014 (<b>c</b>) and 2015 (<b>d</b>); Fluorescence at 760 nm (F<sub>760</sub>) in 2014 (<b>e</b>) and 2015 (<b>f</b>); light use efficiency of fluorescence emission at 760 nm (LUE<sub>f</sub>) in 2014 (<b>g</b>) and 2015 (<b>h</b>); and fraction of F<sub>760</sub> that escapes the canopy (Fesc<sub>fw</sub>) in 2014 (<b>i</b>) and 2015 (<b>l</b>). Data are divided among campaigns. Bar graphs represent means and error bars represent 1 standard error. Group differences in (<b>a</b>–<b>h</b>) were analyzed with ANOVA test and individual differences among groups were evaluated with Tukey HSD post hoc test. Group differences in (<b>i</b>,<b>l</b>) were analyzed with ANOVA with the Welch correction and individual differences among groups were evaluated with the Games–Howell post hoc test. “*” refers to a significant difference from the control treatment with <span class="html-italic">p</span> value &lt; 0.05 and “**” refers to a significant difference from the control treatment with <span class="html-italic">p</span> value &lt; 0.01.</p> Full article ">Figure 3
<p>Bar graph representing differences among treatments (control treatment, C; nitrogen treatment, N; nitrogen and phosphorus treatment, NP; and control treatment, C) of Canopy nitrogen content (N%) in 2014 (<b>a</b>) and 2015 (<b>b</b>); absorbed photosynthetic active radiation (APAR) in 2014 (<b>c</b>) and 2015 (<b>d</b>); Albedo<sub>400–900</sub> in 2014 (<b>e</b>) and 2015 (<b>f</b>); Surface Temperature (Ts) in 2014 (<b>g</b>) and 2015 (<b>h</b>); and graminoids relative abundance (%graminoids) in 2014 (<b>i</b>) and 2015 (<b>l</b>). Data are divided among campaigns. Bar graphs represent means and error bars represent 1 standard error. Group differences in (<b>e</b>–<b>h</b>) were analyzed with ANOVA test and individual differences among groups were evaluated with Tukey HSD post hoc test. Group differences in (<b>a</b>,<b>b</b>,<b>i</b>,<b>l</b>) were analyzed with ANOVA with the Welch correction and individual differences among groups were evaluated with the Games–Howell post hoc test. “*” refers to a significant difference from the control treatment with <span class="html-italic">p</span> value &lt; 0.05 and “**” refers to a significant difference from the control treatment with <span class="html-italic">p</span> value &lt; 0.01.</p> Full article ">Figure 4
<p>Scatterplot of observed fluorescence at 760 nm from top of canopy (F<sub>760</sub>) vs. Gross Primary Production (GPP) for 2014 (<b>a</b>) and for 2015 (<b>c</b>); and directional fluorescence emitted by all leaves at 760 nm calculated from forward SCOPE runs (F<sub>760leaf,fw</sub>) vs. GPP for 2014 (<b>b</b>) and for 2015 (<b>d</b>). Data are divided for the four treatments; control (C), nitrogen addition (N), nitrogen and phosphorus addition (NP) and phosphorus addition (P). P values of the interaction treatment with independent variable (in comparison with the control treatment, C) from an analysis of covariance (ANCOVA) are reported in the bottom-right of each panel. Colored lines represent the regression from the ordinary least square regression.</p> Full article ">Figure 5
<p>Relative importance analysis with “lmg”(Lindeman, Merenda and Gold) method of Light use efficiency of photosynthesis (LUE<sub>p</sub>), Light use efficiency of fluorescence emission at 760 nm (LUE<sub>f</sub>) and escape probability of sun-induced fluorescence at 760 nm obtained from forward runs of SCOPE (Fesc<sub>fw</sub>). Predictors included in the analysis are: soil moisture, Shannon biodiversity index (H), canopy nitrogen content (N%), surface temperature (Ts), relative abundance of legumes (%legumes), relative abundance of graminoids (%graminoids) and leaf area index (LAI). Error bars (1 SE) are calculated through bootstrapping (<span class="html-italic">n</span> = 1000), but are not shown in the figure. They are however reported in the result section.</p> Full article ">Figure 6
<p>Path analysis displays the role of canopy nitrogen content (Canopy N) and relative graminoids abundance (%graminoids) on the energy partitioning at the leaf and canopy level. Photosynthetic active radiation (PAR); absorbed by vegetation photosynthetic active radiation (APAR); fluorescence emission by all leaves at 760 nm calculated by forward runs of SCOPE (F<sub>760leaf,fw</sub>); gross primary production (GPP); surface temperature (Ts); and observed fluorescence at 760 nm (F<sub>760</sub>). The strength of the relationship among variables is expressed by the standardized coefficient (β) of the path analysis. Each standardized coefficient has a standard error obtained from bootstrapping (<span class="html-italic">n</span> = 100 times). The width of the arrows is proportional to their standardized coefficient (β). Colored lines (both solid or dotted) represent direct relationships between variables, whereas gray double-headed arrows represent the covariance among variables. Solid and dotted lines indicate significant (<span class="html-italic">p</span> &lt; 0.05) and non-significant relationships, respectively. The width of the arrows is proportional to their standardized coefficient (β). The different colors are introduced to increase readability of the standardized path coefficients. The fit by the overall model is measured by means of Chi-squared (χ2), comparative fit index (CFI) and standardized root mean square of residual (SRMR).</p> Full article ">
27 pages, 5096 KiB  
Article
Global Sensitivity Analysis of the SCOPE Model in Sentinel-3 Bands: Thermal Domain Focus
by Egor Prikaziuk and Christiaan van der Tol
Remote Sens. 2019, 11(20), 2424; https://doi.org/10.3390/rs11202424 - 18 Oct 2019
Cited by 25 | Viewed by 5289
Abstract
Sentinel-3 satellite has provided simultaneous observations in the optical (visible, near infrared (NIR), shortwave infrared (SWIR)) and thermal infrared (TIR) domains since 2016, with a revisit time of 1–2 days. The high temporal resolution and spectral coverage make the data of this mission [...] Read more.
Sentinel-3 satellite has provided simultaneous observations in the optical (visible, near infrared (NIR), shortwave infrared (SWIR)) and thermal infrared (TIR) domains since 2016, with a revisit time of 1–2 days. The high temporal resolution and spectral coverage make the data of this mission attractive for vegetation monitoring. This study explores the possibilities of using the Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) model together with Sentinel-3 to exploit the two sensors onboard of Sentinel-3 (the ocean and land color instrument (OLCI) and sea and land surface temperature radiometer (SLSTR)) in synergy. Sobol’ variance based global sensitivity analysis (GSA) of top of atmosphere (TOA) radiance produced with a coupled SCOPE-6S model was conducted for optical bands of OLCI and SLSTR, while another GSA of SCOPE was conducted for the land surface temperature (LST) product of SLSTR. The results show that in addition to ESA level-2 Sentinel-3 products, SCOPE is able to retrieve leaf area index (LAI), leaf chlorophyll content (Cab), leaf water content (Cw), leaf senescent material (Cs), leaf inclination distribution (LAD). Leaf dry matter content (Cdm) and soil brightness, despite being important, were not confidently retrieved in some cases. GSA of LST in TIR domain showed that plant biochemical parameters—maximum carboxylation rate (Vcmax) and stomata conductance-photosynthesis slope (Ball-Berry m)—can be constrained if prior information on near-surface weather conditions is available. We conclude that the combination of optical and thermal domains facilitates the constraint of the land surface energy balance using SCOPE. Full article
(This article belongs to the Special Issue Recent Progress in Remote Sensing of Terrestrial and Aquatic Fluorescence)
►▼ Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Sensor response functions of Sentinel-3 satellite instruments in typical vegetation top of canopy (TOC, green) and top of atmosphere (TOA, blue) radiance spectra. (<b>a</b>)—optical Sea and Land Surface Temperature Radiometer (SLSTR); (<b>b</b>)—thermal SLSTR; (<b>c</b>)—Ocean and Land Colour Imager (OLCI).</p> Full article ">Figure 2
<p>Workflow: sampling, model runs, sensitivity indices calculation. Varied input parameters are highlighted in green: atmospheric input consists of 3 parameters: AOT@550, water and ozone; leafbio consists of 7 leaf optical parameters: Cab, Cca, Cdm, Cs, Cw, Cant, N; canopy consists of 3 parameters: LAI, LIDFa, LIDFb; soil consists of 4 BSM model parameters: BSMBrightness, BSMlat, BSMlon and SMC. Notice, that angles (solar and viewing zenith and azimuth angles) were fixed to nadir or oblique configuration. Direct model outputs are highlighted in red. The output used for sensitivity index calculations is highlighted in blue. TOC—Top Of Canopy, TOA—Top Of Atmosphere. Definitions and ranges of input parameters can be found in <a href="#remotesensing-11-02424-t001" class="html-table">Table 1</a>.</p> Full article ">Figure 3
<p>(<b>a</b>)—33 simulated vegetation TOA reflected radiance spectra in bands of optical Sentinel-3 instruments (OLCI and SLSTR), (<b>b</b>)—added noise based on signal-to-noise ratio of the instruments.</p> Full article ">Figure 4
<p>Total order sensitivity indices (ST) calculated with coupled RTMo-6S model in the range 400–2400 nm. (<b>a</b>)—top of canopy (TOC), (<b>b</b>)—top of atmosphere (TOA). ST are expressed in percentages. Definitions of parameters can be found in <a href="#remotesensing-11-02424-t001" class="html-table">Table 1</a>.</p> Full article ">Figure 5
<p>Significant total order sensitivity indices (ST &gt; 0.05) for OLCI instrument. Parameters marked with diamonds had insignificant first order sensitivity index (S1 &lt; 0.05) and reached significant ST through interactions. (<b>a</b>)—visible domain, (<b>b</b>)—near infrared domain. Definitions of parameters can be found in <a href="#remotesensing-11-02424-t001" class="html-table">Table 1</a>.</p> Full article ">Figure 6
<p>Significant total order sensitivity indices (ST &gt; 0.05) for SLSTR instrument. Parameters marked with diamonds had insignificant first order sensitivity index (S1 &lt; 0.05) and reached significant ST through interactions. (<b>a</b>)—nadir view, (<b>b</b>)—oblique view. Definitions of parameters can be found in <a href="#remotesensing-11-02424-t001" class="html-table">Table 1</a>.</p> Full article ">Figure 7
<p>Quality of retrieval on a synthetic dataset. Parameters with names in italic (Cant, BSMlon, SMC)—parameters with low sensitivity index values. Top—canopy parameters, middle—leaf parameters, bottom—soil parameters. Red - retrieval from OLCI bands, blue - retrieval from both OLCI and SLSTR (Synergy) bands. Definitions of parameters can be found in <a href="#remotesensing-11-02424-t001" class="html-table">Table 1</a>.</p> Full article ">Figure 8
<p>First (S1) and total (ST) order sensitivity indices for top of canopy outgoing thermal radiance in observation direction simulated with complete SCOPE model presented as percentages. Definitions of parameters can be found in <a href="#remotesensing-11-02424-t002" class="html-table">Table 2</a>.</p> Full article ">Figure 9
<p>First (S1) and total (ST) order sensitivity indices for land surface temperature (LST) simulated with SCOPE model from Sentinel-3 SLSTR instrument bands S8 (10.9 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m) S9 (12 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m). (<b>a</b>)—varying all parameters, (<b>b</b>)—fixed air temperature (Ta = 20<math display="inline"><semantics> <msup> <mrow/> <mo>∘</mo> </msup> </semantics></math>), total incoming short- and longwave radiation (Rin = 600 W m<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>2</mn> </mrow> </msup> </semantics></math>, Rli = 300 W m<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>2</mn> </mrow> </msup> </semantics></math>). Red horizontal line denotes the significance threshold (0.05 units of index value). Definitions of parameters can be found in <a href="#remotesensing-11-02424-t002" class="html-table">Table 2</a>.</p> Full article ">Figure A1
<p>Total order sensitivity index (ST) as percent for land surface temperature (LST) simulated with SCOPE model from Sentinel-3 SLSTR instrument bands S8 (10.9 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m) S9 (12 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m) in different views: oblique (transparent), nadir (dense). Red horizontal line denotes the significance threshold (0.05 units of index value). Definitions of parameters can be found in <a href="#remotesensing-11-02424-t002" class="html-table">Table 2</a>.</p> Full article ">
22 pages, 4608 KiB  
Article
A Spectral Fitting Algorithm to Retrieve the Fluorescence Spectrum from Canopy Radiance
by Sergio Cogliati, Marco Celesti, Ilaria Cesana, Franco Miglietta, Lorenzo Genesio, Tommaso Julitta, Dirk Schuettemeyer, Matthias Drusch, Uwe Rascher, Pedro Jurado and Roberto Colombo
Remote Sens. 2019, 11(16), 1840; https://doi.org/10.3390/rs11161840 - 7 Aug 2019
Cited by 41 | Viewed by 5727
Abstract
Retrieval of Sun-Induced Chlorophyll Fluorescence (F) spectrum is one of the challenging perspectives for further advancing F studies towards a better characterization of vegetation structure and functioning. In this study, a simplified Spectral Fitting retrieval algorithm suitable for retrieving the F [...] Read more.
Retrieval of Sun-Induced Chlorophyll Fluorescence (F) spectrum is one of the challenging perspectives for further advancing F studies towards a better characterization of vegetation structure and functioning. In this study, a simplified Spectral Fitting retrieval algorithm suitable for retrieving the F spectrum with a limited number of parameters is proposed (two parameters for F). The novel algorithm is developed and tested on a set of radiative transfer simulations obtained by coupling SCOPE and MODTRAN5 codes, considering different chlorophyll content, leaf area index and noise levels to produce a large variability in fluorescence and reflectance spectra. The retrieval accuracy is quantified based on several metrics derived from the F spectrum (i.e., red and far-red peaks, O2 bands and spectrally-integrated values). Further, the algorithm is employed to process experimental field spectroscopy measurements collected over different crops during a long-lasting field campaign. The reliability of the retrieval algorithm on experimental measurements is evaluated by cross-comparison with F values computed by an independent retrieval method (i.e., SFM at O2 bands). For the first time, the evolution of the F spectrum along the entire growing season for a forage crop is analyzed and three diverse F spectra are identified at different growing stages. The results show that red F is larger for young canopy; while red and far-red F have similar intensity in an intermediate stage; finally, far-red F is significantly larger for the rest of the season. Full article
(This article belongs to the Special Issue Recent Progress in Remote Sensing of Terrestrial and Aquatic Fluorescence)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Flow chart of the fluorescence spectrum retrieval algorithm based on the Spectral Fitting technique for top-of-canopy observations (field spectroscopy).</p> Full article ">Figure 2
<p>Apparent reflectance (<b><span class="html-italic">R</span>*</b>, <b>dark green line</b>), spline fit of apparent reflectance (<b><span class="html-italic">R</span>*fit</b>, <b>blue line</b>) and true reflectance (<b><span class="html-italic">R</span></b>, <b>light green</b>). Inner boxes show details at O<sub>2</sub> bands.</p> Full article ">Figure 3
<p>Red and far-red <span class="html-italic">F</span> emission peaks initially modeled by the retrieval algorithm (<b>gray peaks</b>); 1-<span class="html-italic">R</span> correction function (<b>dashed green line</b>); resulting <span class="html-italic">F</span> spectrum (<b>red line</b>).</p> Full article ">Figure 4
<p>Fluorescence spectrum derived metrics.</p> Full article ">Figure 5
<p>Surface reflectance (<b>left</b>), sun-induced fluorescence (<b>middle</b>), upwelling radiance (<b>right</b>) simulated for the different RT simulations.</p> Full article ">Figure 6
<p>Field measurement set-up over the different targets investigated: (<b>A</b>) forage; (<b>B</b>) alfalfa; (<b>C</b>) corn; (<b>D</b>) chickpea.</p> Full article ">Figure 7
<p>Retrieval of full <span class="html-italic">F</span> spectrum in the 650–780 nm spectral window from RT simulations: (<b>left</b>) apparent reflectance (<b>dark green</b>), reference reflectance from SCOPE (<b>dashed blue</b>) and retrieved reflectance (<b>light green</b>); (<b>middle</b>) reference fluorescence from SCOPE (<b>blue</b>) and retrieved fluorescence (<b>red</b>); (<b>right</b>) reference and modeled upwelling radiance ((<b>dark and light blue</b>) respectively).</p> Full article ">Figure 8
<p>Comparison between <span class="html-italic">F</span> spectrum retrieved (<b>red</b>) and reference (<b>blue</b>) for all the simulated cases.</p> Full article ">Figure 9
<p>Scatterplots between reference and retrieved values at selected wavelengths: (<b>A</b>) maximum of the red peak; (<b>B</b>) maximum of far-red peak; (<b>C</b>) spectrally integrated <span class="html-italic">F</span>; (<b>D</b>) <span class="html-italic">F</span> at 687 nm (O<sub>2</sub>-B band); (<b>E</b>) <span class="html-italic">F</span> at 760 nm (O<sub>2</sub>-A band). The red line is the linear least square fit whereas the dashed blue line represents the 1:1. Data refers to SNR = 1000.</p> Full article ">Figure 10
<p>Retrieval of full <span class="html-italic">F</span> spectrum from FLOX: (<b>left</b>) apparent reflectance (<b>dark green</b>) and retrieved reflectance (<b>light green</b>); (<b>middle</b>) full <span class="html-italic">F</span> spectrum; (<b>right</b>) measured (<b>dark blue</b>) and modeled (<b>light blue</b>) canopy upwelling radiance and downwelling radiance (<b>gray</b>).</p> Full article ">Figure 11
<p>Scatterplot between fluorescence (<b>top</b>) and reflectance (<b>bottom</b>) at 760 nm (<b>left</b>) and 687 nm (<b>right</b>) estimated by SFM and the novel <span class="html-italic">F</span> spectrum algorithm. The dots represent hourly average values (standard deviation) for the entire time series acquired on different crops during the entire season. The red line is the OLS linear regression model; dashed blue line the 1:1.</p> Full article ">Figure 12
<p>Temporal evolution of fluorescence and reflectance along growing season of forage crop between February to May: (<b>top-left</b>) reflectance spectrum; (<b>bottom-left</b>) fluorescence spectrum; (<b>top-right</b>) MTCI and downwelling radiance; (<b>bottom-right</b>) F<sub>RED</sub>, F<sub>FAR-RED</sub> and F<sub>INT</sub> values.</p> Full article ">
26 pages, 6760 KiB  
Article
Hyplant-Derived Sun-Induced Fluorescence—A New Opportunity to Disentangle Complex Vegetation Signals from Diverse Vegetation Types
by Subhajit Bandopadhyay, Anshu Rastogi, Uwe Rascher, Patrick Rademske, Anke Schickling, Sergio Cogliati, Tommaso Julitta, Alasdair Mac Arthur, Andreas Hueni, Enrico Tomelleri, Marco Celesti, Andreas Burkart, Marcin Stróżecki, Karolina Sakowska, Maciej Gąbka, Stanisław Rosadziński, Mariusz Sojka, Marian-Daniel Iordache, Ils Reusen, Christiaan Van Der Tol, Alexander Damm, Dirk Schuettemeyer and Radosław Juszczakadd Show full author list remove Hide full author list
Remote Sens. 2019, 11(14), 1691; https://doi.org/10.3390/rs11141691 - 17 Jul 2019
Cited by 23 | Viewed by 6193
Abstract
Hyperspectral remote sensing (RS) provides unique possibilities to monitor peatland vegetation traits and their temporal dynamics at a fine spatial scale. Peatlands provide a vital contribution to ecosystem services by their massive carbon storage and wide heterogeneity. However, monitoring, understanding, and disentangling the [...] Read more.
Hyperspectral remote sensing (RS) provides unique possibilities to monitor peatland vegetation traits and their temporal dynamics at a fine spatial scale. Peatlands provide a vital contribution to ecosystem services by their massive carbon storage and wide heterogeneity. However, monitoring, understanding, and disentangling the diverse vegetation traits from a heterogeneous landscape using complex RS signal is challenging, due to its wide biodiversity and distinctive plant species composition. In this work, we aim to demonstrate, for the first time, the large heterogeneity of peatland vegetation traits using well-established vegetation indices (VIs) and Sun-Induced Fluorescence (SIF) for describing the spatial heterogeneity of the signals which may correspond to spatial diversity of biochemical and structural traits. SIF originates from the initial reactions in photosystems and is emitted at wavelengths between 650–780 nm, with the first peak at around 687 nm and the second peak around 760 nm. We used the first HyPlant airborne data set recorded over a heterogeneous peatland area and its surrounding ecosystems (i.e., forest, grassland) in Poland. We deployed a comparative analysis of SIF and VIs obtained from differently managed and natural vegetation ecosystems, as well as from diverse small-scale peatland plant communities. Furthermore, spatial relationships between SIF and VIs from large-scale vegetation ecosystems to small-scale peatland plant communities were examined. Apart from signal variations, we observed a positive correlation between SIF and greenness-sensitive VIs, whereas a negative correlation between SIF and a VI sensitive to photosynthesis was observed for large-scale vegetation ecosystems. In general, higher values of SIF were associated with higher biomass of vascular plants (associated with higher Leaf Area Index (LAI)). SIF signals, especially SIF760, were strongly associated with the functional diversity of the peatland vegetation. At the peatland area, higher values of SIF760 were associated with plant communities of high perennials, whereas, lower values of SIF760 indicated peatland patches dominated by Sphagnum. In general, SIF760 reflected the productivity gradient on the fen peatland, from Sphagnum-dominated patches with the lowest SIF and fAPAR values indicating lowest productivity to the Carex-dominated patches with the highest SIF and fAPAR values indicating highest productivity. Full article
(This article belongs to the Special Issue Recent Progress in Remote Sensing of Terrestrial and Aquatic Fluorescence)
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Figure 1

Figure 1
<p>Location of the Rzecin peatland experimental site in the Wielkopolska region, Poland. An RGB composite map was obtained by combining reflectance bands at 156 nm, 105 nm, and 51 nm for the red, green, and blue bands, respectively—including flight lines of the HyPlant over the site during the Spectrometry of a Wetland and Modelling of Photosynthesis (SWAMP) campaign on 11 July 2015.</p> Full article ">Figure 2
<p>Location of the ground validation plots (V1–V9) at the Rzecin peatland area during the SWAMP campaign on 11 July 2015. The plots were located on both sides of the boardwalk shown in the UAV (Unmanned Aerial Vehicle) map.</p> Full article ">Figure 3
<p>Boundaries of 158 ROIs identified within the HyPlant image and categorized into 19 unique vegetation groups (<b>a</b>); and location of 52 plots inside the peatland and its boundaries, categorized into 20 unique plant communities (<b>b</b>). Detailed characteristics of the plant communities within the 52 plots are presented in <a href="#app1-remotesensing-11-01691" class="html-app">Table S1 (supplementary table)</a>.</p> Full article ">Figure 4
<p>Airborne maps of the different vegetation indices from the experimental site, derived from DUAL module (370–2500 nm) of the HyPlant with a spatial resolution of 1 m × 1 m per pixel. The data was recorded on 11 July 2015, and was acquired during the afternoon overpasses of HyPlant. (<b>a</b>) Simple Ratio (SR), (<b>b</b>) Normalized Difference Vegetation Index (NDVI), (<b>c</b>) Enhanced Vegetation Index (EVI), (<b>d</b>) Photochemical Reflectance Index (PRI), (<b>e</b>) SIF map for O<sub>2</sub>A (760 nm), and (<b>f</b>) SIF map for O<sub>2</sub>B (687 nm). The associated range of each of the vegetation indices and SIF maps is represented in color stretch on the left.</p> Full article ">Figure 5
<p>Validation of airborne HyPlant-derived VIs and SIF with ground observations on corresponding plots; (<b>a</b>) SR, (<b>b</b>) NDVI, (<b>c</b>) EVI, (<b>d</b>) PRI, (<b>e</b>) SIF at 760 nm, and (<b>f</b>) SIF at 687 nm. Error bars represent the spatial variability of the index values within the selected plots during HyPlant overpasses. Note: HyPlant SIF values were retrieved from the rescaled data (for details see <a href="#sec2dot6-remotesensing-11-01691" class="html-sec">Section 2.6</a>.</p> Full article ">Figure 6
<p>Relationship between HyPlant-derived SIF and LAI and fAPAR at the ground validation plots. Note: HyPlant SIF values were retrieved from the rescaled data (for details see <a href="#sec2dot6-remotesensing-11-01691" class="html-sec">Section 2.6</a>).</p> Full article ">Figure 7
<p>Bar diagram presenting the average values of the observed VIs and SIF derived from HyPlant data; (<b>a</b>) SR, (<b>b</b>) NDVI, (<b>c</b>) EVI, (<b>d</b>) PRI, (<b>e</b>) SIF<sub>760</sub>, and (<b>f</b>) SIF<sub>687</sub>. The names of the individual vegetation groups are written at the bottom of the bars and correspond to the abbreviations provided for <a href="#remotesensing-11-01691-f003" class="html-fig">Figure 3</a>a. The dark grey group of bars represents the peatland ecosystem; light grey bars represent different kinds of grasslands (including the post-agricultural land, pioneer vegetation of sandy and shallow soil); and white grey bars stand for different kinds of forests. Error bars represent standard deviation.</p> Full article ">Figure 8
<p>Correlation analysis of SIF at 760 nm (<a href="#remotesensing-11-01691-f008" class="html-fig">Figure 8</a>a–d) and at 687 nm (<a href="#remotesensing-11-01691-f008" class="html-fig">Figure 8</a>e–h) with selected remotely sensed VIs (SR, NDVI, and PRI) at the vegetation group level. Error bars represent standard deviation. Here, black dots denote peatland, light grey dots denote grasslands, and white represents forest vegetation groups.</p> Full article ">Figure 9
<p>Bar diagram presenting the average values of the observed vegetation indices and SIF derived from HyPlant data; (<b>a</b>) SR, (<b>b</b>) NDVI, (<b>c</b>) EVI, (<b>d</b>) PRI, (<b>e</b>) SIF<sub>760</sub>, and (<b>f</b>) SIF<sub>687</sub>. The codes corresponding to the names of the individual plant communities are written at the bottom of the bars and correspond to the abbreviations provided with <a href="#remotesensing-11-01691-f003" class="html-fig">Figure 3</a>b. Detailed description of these plant communities is provided in <a href="#app1-remotesensing-11-01691" class="html-app">Table S1 (Supplementary Material)</a>. The first group of bars represents meadows (ME), the second group of bars represents peatland rush (PR) vegetation communities, and the third group of bars stands for the fen (FE) vegetation communities. Error bars represent standard deviation.</p> Full article ">Figure 10
<p>Correlation analysis of SIF at 760 nm and at 687 nm with selected remotely-sensed vegetation indices (SR, NDVI, EVI, and PRI) at vegetation community level restricted to peatland area. Plant communities were grouped in three categories comprised of meadows (ME), peatland rush vegetation (PR), and fen vegetation (FE), and were presented separately for each group. Codes names correspond to those provided with <a href="#remotesensing-11-01691-f003" class="html-fig">Figure 3</a>. Error bars represent standard deviation.</p> Full article ">
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