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Advances in Remote Sensing of Atmospheric Aerosols and Their Radiative Effects

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmospheric Remote Sensing".

Deadline for manuscript submissions: 15 December 2024 | Viewed by 1331

Special Issue Editors

Dr. Omar Torres

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Guest Editor
NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
Interests: algorithm development; aerosol absorption
Special Issues, Collections and Topics in MDPI journals
Dr. Hiren Jethva

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Guest Editor
GESTAR-II, NASA Goddard Space Flight Center, Morgan State University, Greenbelt, MD 20771, USA
Interests: aolgorith development; long term record analyses
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The scattering and absorption of incoming solar radiation by natural and anthropogenic aerosols are important radiative processes that affect the energy balance of the Earth–atmosphere system. As a result of the discovery of the high sensitivity to aerosol absorption in the near UV spectral region, recent satellite aerosol retrieval algorithms have evolved into UV-to-NIR multi-wavelength applications capable of simultaneously deriving spectral aerosol optical depth and single-scattering albedo, along with aerosol layer height (DSCOVR-EPIC, S5P-TROPOMI, PACE-OCI). Spaceborne lidar observations from the CALIPSO-CALIOP sensor (2005-2023), and the currently operational ICESAT-2 mission provide information on aerosol vertical distribution.

Generally, aerosol retrieval algorithms improved as new theoretical developments allow for obtaining a better understanding of instrument capabilities. The aim of this Special Issue is documenting retrieval algorithm upgrades or the description of new algorithmic approaches applied to satellite-borne instrumentation deployed over the last twenty-five years, using spectral measurements of backscattered near-UV radiation (OMI and TROPOMI), visible and near-infrared radiation (MODIS and VIIRS), multi-angle spectral measurements (MiSR) and polarization observations (POLDER). Papers on retrieval algorithmic approaches applied to both low and geostationary orbital configurations (i.e., GEMS and TEMPO) and to lidar observations are encouraged.

For this Special Issue of Remote Sensing, we invite papers on the use of surface-based and space-borne observations by current and upcoming missions for the retrieval of aerosol properties. We invite submissions on different aspects of aerosol remote sensing including, new sensor capabilities,  surface characterization and retrieval algorithm development and improvement. Papers on analyses of long-term records and the estimation of aerosol radiative effects are strongly encouraged.

Dr. Omar Torres
Dr. Hiren Jethva
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.

Keywords

  • radiative effects
  • aerosol properties
  • polarization
  • retrieval algorithm
  • long-term record
  • satellite
  • cloud screening
  • surface reflectance

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Published Papers (2 papers)

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Research

27 pages, 28409 KiB  
Article
Non-Dominated Sorting Genetic Algorithm II (NSGA2)-Based Parameter Optimization of the MSMGWB Model Used in Remote Infrared Sensing Prediction for Hot Combustion Gas Plume
by Yihan Li, Haiyang Hu and Qiang Wang
Remote Sens. 2024, 16(17), 3116; https://doi.org/10.3390/rs16173116 - 23 Aug 2024
Viewed by 468
Abstract
The Multi-Scale Multi-Group Wide-Band (MSMGWB) model was used to calculate radiative transfer in strongly non-isothermal and inhomogeneous media such as the remote infrared sensing of aircraft exhaust system and jet plume scenario. In this work, the reference temperature was introduced into the model [...] Read more.
The Multi-Scale Multi-Group Wide-Band (MSMGWB) model was used to calculate radiative transfer in strongly non-isothermal and inhomogeneous media such as the remote infrared sensing of aircraft exhaust system and jet plume scenario. In this work, the reference temperature was introduced into the model as an independent variable for each spectral subinterval group. Then, to deal with the exceedingly vast parameter sample space (i.e., the combination of spectral subinterval grouping results, reference temperatures and Gaussian quadrature schemes), an MSMGWB model’s parameter optimization process superior to the exhaustive approach employed in previous studies was established, which was consisted of the Non-dominated Sorting Genetic Algorithm II method (NSGA2) and an iterative scan method. Through a series of 0-D test cases and two real 3-D remote infrared imaging results of an aircraft exhaust system, it was observed that the MSMGWB model established and optimiazed in current work demonstrated notable improvements in both accuracy and computational efficiency. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Atmospheric Aerosols and Their Radiative Effects)
►▼ Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>The relationship between <span class="html-italic">k</span> and <math display="inline"><semantics> <msub> <mi>g</mi> <mrow> <mi>n</mi> <mi>m</mi> </mrow> </msub> </semantics></math> of a group in different reference temperatures and thermodynamic states at 8~14 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m band.</p> Full article ">Figure 2
<p>Relationship between probability density of objective function value and three critical factors (Gaussian quadrature point quantity, reference temperature, and wavenumber subinterval grouping).</p> Full article ">Figure 3
<p>Genotype and crossover process diagram.</p> Full article ">Figure 4
<p>NSGA2 algorithm workflow diagram.</p> Full article ">Figure 5
<p>Offspring generation workflow diagram.</p> Full article ">Figure 6
<p>Convergence results of the NSGA2 method: (<b>a</b>) the foremost 10 Pareto front results, (<b>b</b>) convergence iteration process of 10 random grouping strategy combinations.</p> Full article ">Figure 7
<p><math display="inline"><semantics> <msub> <mi>f</mi> <mrow> <mi>e</mi> <mi>r</mi> <mi>r</mi> <mn>0</mn> </mrow> </msub> </semantics></math> results between exhaustive search method and NSGA2 method.</p> Full article ">Figure 8
<p>The <math display="inline"><semantics> <msub> <mi>f</mi> <mrow> <mi>e</mi> <mi>r</mi> <mi>r</mi> <mn>0</mn> </mrow> </msub> </semantics></math> results among 100 <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">H</mi> <mn>2</mn> </msub> <mi mathvariant="normal">O</mi> </mrow> </semantics></math> and 400 <math display="inline"><semantics> <msub> <mi>CO</mi> <mn>2</mn> </msub> </semantics></math> grouping strategy combinations.</p> Full article ">Figure 9
<p>Diagram of 4 iterative scan method process plans.</p> Full article ">Figure 10
<p>Convergence perfomance of 4 plans for scan iteration process.</p> Full article ">Figure 11
<p>Ratio of the <math display="inline"><semantics> <msub> <mi>f</mi> <mrow> <mi>e</mi> <mi>r</mi> <mi>r</mi> <mn>0</mn> </mrow> </msub> </semantics></math> at the current sample population size to its corresponding baseline value.</p> Full article ">Figure 12
<p><math display="inline"><semantics> <msub> <mi>f</mi> <mrow> <mi>e</mi> <mi>r</mi> <mi>r</mi> <mn>0</mn> </mrow> </msub> </semantics></math> results between the same grouping result combination in the NSGA2 model population sizes of 5000 and 40,000.</p> Full article ">Figure 13
<p>Optimization results at 2~2.5 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m band: (<b>a</b>) Pareto front, (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>e</mi> <mi>r</mi> <mi>r</mi> <mi>o</mi> <msub> <mi>r</mi> <mrow> <mi>j</mi> <mo>,</mo> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> </mrow> </semantics></math> in 56 0-D cases.</p> Full article ">Figure 14
<p>Optimization results at 3.7~4.8 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m band: (<b>a</b>) Pareto front, (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>e</mi> <mi>r</mi> <mi>r</mi> <mi>o</mi> <msub> <mi>r</mi> <mrow> <mi>j</mi> <mo>,</mo> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> </mrow> </semantics></math> in 56 0-D cases.</p> Full article ">Figure 15
<p>Optimization results at 3~5 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m band: (<b>a</b>) Pareto front, (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>e</mi> <mi>r</mi> <mi>r</mi> <mi>o</mi> <msub> <mi>r</mi> <mrow> <mi>j</mi> <mo>,</mo> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> </mrow> </semantics></math> in 56 0-D cases.</p> Full article ">Figure 16
<p>Optimization results at 7.7~9.7 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m band: (<b>a</b>) Pareto front, (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>e</mi> <mi>r</mi> <mi>r</mi> <mi>o</mi> <msub> <mi>r</mi> <mrow> <mi>j</mi> <mo>,</mo> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> </mrow> </semantics></math> in 56 0-D cases.</p> Full article ">Figure 17
<p>Optimization results at 8~14 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m band: (<b>a</b>) Pareto front, (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>e</mi> <mi>r</mi> <mi>r</mi> <mi>o</mi> <msub> <mi>r</mi> <mrow> <mi>j</mi> <mo>,</mo> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> </mrow> </semantics></math> in 56 0-D cases.</p> Full article ">Figure 18
<p>Aerosol spectral extinction coefficient at 0~7 km altitude and 2~14 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m: (<b>a</b>) large-sized case, (<b>b</b>) small-sized case.</p> Full article ">Figure 19
<p>Diagram of the Large-sized exhaust system with a cooling structure.</p> Full article ">Figure 20
<p>Distribution of temperature (<span class="html-italic">T</span>), pressure (<span class="html-italic">p</span>), carbon dioxide mass fraction (<math display="inline"><semantics> <msub> <mi>y</mi> <mrow> <mi>C</mi> <msub> <mi>O</mi> <mn>2</mn> </msub> </mrow> </msub> </semantics></math>), and Mach number (<math display="inline"><semantics> <mrow> <mi>M</mi> <mi>a</mi> </mrow> </semantics></math>) in the meridional and axial sections of the fluid field of the large-sized exhaust system with a cooling structure.</p> Full article ">Figure 21
<p>Temperature (<span class="html-italic">T</span>) distribution of the solid part of the large-sized exhaust system with a cooling structure.</p> Full article ">Figure 22
<p>Remote infrared imaging of the large-sized exhaust system with a cooling structure in different atmospheric window bands (<b>left</b>), and the distribution of calculation errors of the optimized MSMGWB model (<b>right</b>), (<b>a</b>,<b>b</b>) 2~2.5 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m band, (<b>c</b>,<b>d</b>) 3.7~4.8 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m band, (<b>e</b>,<b>f</b>) 3~5 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m band, (<b>g</b>,<b>h</b>) 7.7~9.7 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m band, (<b>i</b>,<b>j</b>) 8~14 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m band.</p> Full article ">Figure 23
<p>Diagram of the small-sized exhaust system without a cooling structure.</p> Full article ">Figure 24
<p>Distribution of temperature (<span class="html-italic">T</span>), pressure (<span class="html-italic">p</span>), carbon dioxide mass fraction (<math display="inline"><semantics> <msub> <mi>y</mi> <mrow> <mi>C</mi> <msub> <mi>O</mi> <mn>2</mn> </msub> </mrow> </msub> </semantics></math>), and Mach number (<math display="inline"><semantics> <mrow> <mi>M</mi> <mi>a</mi> </mrow> </semantics></math>) in the meridional and axial sections of the fluid field of the small-sized exhaust system without a cooling structure.</p> Full article ">Figure 25
<p>Temperature (<span class="html-italic">T</span>) distribution of the major components of the small-sized exhaust system without a cooling structure.</p> Full article ">Figure 26
<p>Remote infrared imaging of the small-sized exhaust system without a cooling structure in different atmospheric window bands (<b>left</b>) and the distribution of calculation errors of the optimized MSMGWB model (<b>right</b>), (<b>a</b>,<b>b</b>) 2~2.5 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m band, (<b>c</b>,<b>d</b>) 3.7~4.8 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m band, (<b>e</b>,<b>f</b>) 3~5 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m band, (<b>g</b>,<b>h</b>) 7.7~9.7 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m band, (<b>i</b>,<b>j</b>) 8~14 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m band.</p> Full article ">Figure A1
<p>Two types of radiative transfer paths, diagram of 56 0-D cases.</p> Full article ">
23 pages, 6862 KiB  
Article
Landsat-8/9 Atmospheric Correction Reliability Using Scene Statistics
by David Groeneveld, Tim Ruggles and Bo-Cai Gao
Remote Sens. 2024, 16(12), 2216; https://doi.org/10.3390/rs16122216 - 19 Jun 2024
Viewed by 553
Abstract
Landsat data correction using the Land Surface Reflectance Code (LaSRC) has been proposed as the basis for the atmospheric correction of smallsats. While atmospheric correction can enhance smallsat data, the Landsat/LaSRC pathway delays output and may constrain accuracy and utility. The alternative, the [...] Read more.
Landsat data correction using the Land Surface Reflectance Code (LaSRC) has been proposed as the basis for the atmospheric correction of smallsats. While atmospheric correction can enhance smallsat data, the Landsat/LaSRC pathway delays output and may constrain accuracy and utility. The alternative, the Closed-form Method for Atmospheric Correction (CMAC), developed for smallsat application, provides surface reflectance derived solely from scene statistics. In a prior paper, CMAC closely agreed with LaSRC software for correction of the four VNIR bands of Landsat-8/9 images for conditions of low to moderate atmospheric effect over quasi-invariant warehouse-industrial targets. Those results were accepted as surrogate surface reflectance to support analysis of CMAC and LaSRC reliability for surface reflectance retrieval in two contrasting environments: shortgrass prairie and barren desert. Reliability was defined and tested through a null hypothesis: the same top-of-atmosphere reflectance under the same atmospheric condition will provide the same estimate of surface reflectance. Evaluated against the prior surrogate surface reflectance, the results found decreasing error with increasing wavelength for both methods. From 58 comparisons across the four bands, the LaSRC average absolute error ranged from 0.59% (NIR) to 50.30% (blue). CMAC provided reliable results: error was well constrained from 0.01% (NIR) to 0.98% (blue). Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Atmospheric Aerosols and Their Radiative Effects)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>The quality of atmospheric correction can be judged by image clarity and color balance. Landsat-8 image of Kelowna, British Columbia, 8 August 2021, portrayed in true color: (<b>a</b>) uncorrected TOAR, (<b>b</b>) CMAC corrected, and (<b>c</b>) LaSRC corrected.</p> Full article ">Figure 2
<p>Bandwise reflectance CDFs for the four VNIR bands averaged for all 31 images of Landsat-8 and -9 of TOAR, CMAC, and LaSRC treatments extracted from AOIs in two Southern California municipalities. The resulting curves show nearly complete agreement between CMAC and LaSRC. High dynamic spectral range, dark to bright, for each band is demonstrated by the wide range of extracted reflectance values.</p> Full article ">Figure 3
<p>AOIs of Lake Newell (L8, 5 August 2023; <b>top</b>) and El Pinacate (L8, 4 July 2022; <b>bottom</b>) shown for three treatments: (<b>a</b>) TOAR; (<b>b</b>) CMAC; and (<b>c</b>) LaSRC. The area inside the Lake Newell AOI is 27.46 km<sup>2</sup> and 12.62 km<sup>2</sup> for the El Pinacate AOI.</p> Full article ">Figure 4
<p>Extracted reflectance by treatment, with dot, dash, and solid patterns that identify each image evaluated by the two methods. Other than TOAR for Lake Newell, the bands in the graphs stack from left to right according to increasing wavelength: blue, green, red and NIR. Both AOIs have very low spectral dynamic range compared to <a href="#remotesensing-16-02216-f002" class="html-fig">Figure 2</a> and plot here as almost vertical lines.</p> Full article ">Figure 5
<p>Lake Newell CDFs per band displayed with the points determined from the SoCal AOIs: Rochester (for 14 August 2023) and Fontana (for 29 July 2023 and 6 August 2023). The scaling of the x-axes is optimized to provide equivalent reflectance intervals for the same range per band. Where points overlie each other, the LaSRC SoCal points are depicted in white and are of smaller size.</p> Full article ">Figure 6
<p>Per-band El Pinacate CDFs displayed with the points reconstructed from the SoCal AOI of Fontana. The scaling of the x-axes is optimized to provide equivalent reflectance intervals for the same range per band. Where points overlie each other, the LaSRC SoCal points are depicted in white and are of smaller size.</p> Full article ">Figure A1
<p>CDFs of two Sentinel-2 spectral bands extracted from an AOI with consistent surface reflectance across both image snapshots. Arrows show the direction of CDF rotation from increasing haze. This effect occurs in all VNIR bands.</p> Full article ">Figure A2
<p>The CMAC conceptual model. Blue arrows indicate the rotational direction for increasing atmospheric effect. The dashed line represents all pixels, dark to bright, under one atmospheric effect for a single spectral band.</p> Full article ">Figure A3
<p><a href="#remotesensing-16-02216-f002" class="html-fig">Figure 2</a>, copied from Fraser and Kaufman, 1985 [<a href="#B11-remotesensing-16-02216" class="html-bibr">11</a>]. The solid lines represent common atmospheric aerosols and are equivalent to the dashed line in <a href="#remotesensing-16-02216-f004" class="html-fig">Figure 4</a>. Dashed lines represent highly absorptive carbon particles to illustrate the importance of aerosol absorption upon reflectance.</p> Full article ">Figure A4
<p>This annotated sample spreadsheet shows the calculation workflow for comparison of extracted L8 blue band data. Note that the TOAR, CMAC, and LaSRC tables are stacked vertically in the original spreadsheet but are rearranged here for ease of illustration. Tables of the five images are from the Fontana AOI, whose TOAR values correspond with the TOAR column for the Lake Newell AOI (shaded). Portions of the distribution are enclosed in boxes defining where the average TOAR of Fontana was found by interpolation of the Lake Newell distributions. The process began at <b>A</b> with the selection of five sequential images of Fontana, whose median Atm-I values were averaged and found to equal the median Atm-I of the Lake Newell AOI. The interpolations of the data in the shaded column boxes were performed graphically in the original spreadsheets, which are downloadable from the <a href="#app1-remotesensing-16-02216" class="html-app">Supplementary Materials</a>.</p> Full article ">Figure A5
<p>TOAR View of the El Pinacate region. The moderate level of haze obscures ground features over the desert. Light-colored features of the ocean result from a mix of entrained sediments in the water column and (hypothetically) from specular reflectance of the sky, This was influenced by wind, in evidence as streaks from the northwest. The AOI outline from which data were extracted is shown in red.</p> Full article ">Figure A6
<p>Atm-I grayscale view of the El Pinacate region. This atmospheric model output was applied to scale the degree of correction removing the atmospheric effect in the 4 July 2022 image: the brighter the response, the greater the correction. Of note is the brightness of the grayscale over the Sea of Corez, hypothetically induced by specular reflectance. Atm-I is a statistical representation of the atmospheric effect and, as such, has lower resolution than the original image; hence, the faint streaks from wind effects visible in the TOAR view are smeared in this view.</p> Full article ">Figure A7
<p>CMAC-corrected view with specular reflectance largely removed from the TOAR view of the ocean, patterns of entrained sediments and green-tinted water are now visible. Terrestrial features of windblown dunes and the complex hydrology surrounding the bay are visible after CMAC processing, though indistinct in the TOAR view. Future research is expected to define a relationship for the atmospheric statistical model (Atm-I) measurement of specular reflectance to enable reliable atmospheric correction over water.</p> Full article ">Figure A8
<p>LaSRC-Corrected View. Like the CMAC correction, finer features of the image are visible after LaSRC correction. Image artifacts over the water are a common feature created by LaSRC correction. Such artifacts are also visible in the LaSRC view of <a href="#remotesensing-16-02216-f001" class="html-fig">Figure 1</a>c in the main body of the text.</p> Full article ">Figure A9
<p>Change detection confirmed rainfall prior to 14 August, visible as a darkened smear to the west of Lake Newell. <a href="#remotesensing-16-02216-f005" class="html-fig">Figure 5</a> provides a color reference for the 6 August 2023 image. This analysis was performed to confirm the validity of the extracted NIR values to explain why the 14 August NIR results did not conform with the other two dates in <a href="#remotesensing-16-02216-f004" class="html-fig">Figure 4</a>. The linear features that cross the area west of Lake Newell are gravel roads (confirmed on Google Earth) that drain rapidly and dry much quicker than the surrounding prairie. The prominence of these roads on the change detection image confirms that the darker area is not an atmospheric issue. Lakes appear black here because the TOAR reflectance was elevated due to haze (higher Atm-I); they are brighter on the 6 August image.</p> Full article ">
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