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Special Issue "Aerosol and Atmospheric Correction"

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

Deadline for manuscript submissions: 31 December 2023 | Viewed by 7899

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Prof. Dr. Xingfa Gu

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Guest Editor

Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: remote sensing technology and application; information extraction and engineering; quantitative remote sensing
Special Issues, Collections and Topics in MDPI journals

Dr. Jing Wei

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Guest Editor

Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740, USA
Interests: remote sensing; artificial intelligence; big data; air pollution; aerosol; particulate matter; trace gas; cloud
Special Issues, Collections and Topics in MDPI journals

Dr. Shuaiyi Shi

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Guest Editor

Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: aerosol remote sensing; biomass burning aerosol aging characteristic; climate change
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Aerosol is the key factor in atmospheric correction of remote sensing images, while atmospheric aerosol severely influences global climate and air quality. Remote sensing is a complex system. The radiation signal received by the sensor is surface-atmosphere coupled, including the signal of path radiance, surface reflection, and surface-atmosphere interaction, a phenomenon which impedes quantitative information acquisition from both a surface and atmosphere aspect. Accurate aerosol estimation and atmospheric correction are needed to solve this puzzle.

The goal of this Special Issue is to discuss accurate retrieval and estimation of aerosol to help precise atmospheric correction and narrow down its uncertainty in climatic and environmental effect. Further, with the development of new technologies, such as high resolution and hyperspectral sensors as well as artificial intelligence, new ways of aerosol estimation and atmospheric correction are awaiting exploration. Therefore, we cordially invite our colleagues in the scientific community to submit their recent findings on “Aerosol and Atmospheric Correction” to this Special Issue of Remote Sensing. Potential topics include but are not limited to the following:

Aerosol retrieval;
Atmospheric correction;
Radiative transfer;
Surface–atmosphere signal decoupling;
Aerosol estimation;
Climatic and environmental effect of aerosol;
Remote sensing image preprocessing;
Artificial intelligence aid atmospheric correction;
High-resolution image atmospheric correction;
Hyperspectral image atmospheric correction.

Prof. Dr. Xingfa Gu
Dr. Jing Wei
Dr. Shuaiyi Shi
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

aerosol retrieval
atmospheric correction
radiative transfer
surface-atmosphere signal decoupling
aerosol estimation
climatic and environmental effect
image preprocessing
artificial intelligence
high resolution image
hyperspectral image

Published Papers (7 papers)

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16 pages, 6687 KiB

Open AccessArticle

The Identification and Analysis of Long-Range Aerosol Transport Pathways with Layered Cloud-Aerosol Lidar with Orthogonal Polarization Datasets from 2006 to 2016

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Remote Sens. 2023, 15(18), 4537; https://doi.org/10.3390/rs15184537 - 15 Sep 2023

Viewed by 376

Abstract

In this study, we used Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aerosol products acquired from 2006 to 2016 to identify global long-range aerosol transport pathways, including the trans-Atlantic, the trans-Pacific, and the trans-Arabian Sea pathways. Deep analyses were subsequently conducted focusing on two significant paths within the range of the trans-Pacific transport pathway, from which we generated a three-stage conceptual model mainly identifying aerosols from the Taklimakan Desert and aerosols from the North China Plain. The results show that in the first stage of the model, the dust or polluted-dust aerosols were emitted, raised, and mixed within the planetary boundary layer (PBL), characterized by high percentages (>70%) of aerosols in the PBL (AOD_PBL), while in the second stage, some aerosols were further raised into the free troposphere where the AOD_PBL percentages decreased to less than 40%, driven by vertical movements and turbulences; in the last stage, the aerosols gradually settled back to the surface layer due to gravity and wet deposition, inferred by increasing AOD_PBL percentages. We demonstrated that the proposed model is capable of characterizing different aerosol types and climate conditions on spatiotemporal scales, providing a straightforward and evident approach to exploring long-range aerosol transport pathways. Full article

(This article belongs to the Special Issue Aerosol and Atmospheric Correction)

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20 pages, 6322 KiB

Open AccessArticle

A Novel Algorithm of Haze Identification Based on FY3D/MERSI-II Remote Sensing Data

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Remote Sens. 2023, 15(2), 438; https://doi.org/10.3390/rs15020438 - 11 Jan 2023

Cited by 1 | Viewed by 983

Abstract

Since 2013, frequent haze pollution events in China have been attracting public attention, generating a demand to identify the haze areas using satellite observations. Many studies of haze recognition algorithms are based on observations from space-borne imagers, such as the Moderate Resolution Imaging Spectroradiometer (MODIS), the Visible Infrared Imaging Radiometer Suite (VIIRS) and the Advanced Himawari Imager (AHI). Since the haze pixels are frequently misidentified as clouds in the official cloud detection products, these algorithms mainly focus on recovering them from clouds. There are just a few studies that provide a more precise distinction between haze and clear pixels. The Medium Resolution Imaging Spectrometer II (MERSI-II), the imager aboard the FY-3D satellite, has similar bands to those of MODIS, hence, it appears to have equivalent application potential. This study proposes a novel MERSI haze mask (MHAM) algorithm to directly categorize haze pixels in addition to cloudy and clear ones. This algorithm is based on the fact that cloudy and clear pixels exhibit opposing visible channel reflectance and infrared channel brightness temperature characteristics, and clear pixels are relative brighter, and as well as this, there is a positive difference between their apparent reflectance values, at 0.865 μm and 1.64 μm, respectively, over bright surfaces. Compared with the Aqua/MODIS and MERSI-II official cloud detection products, these two datasets treat the dense aerosol loadings as certain clouds, possible clouds and possible clear pixels, and they treat distinguished light or moderate haze as possible clouds, possible clear pixels and certainly clear pixels, while the novel algorithm is capable of demonstrating the haze region’s boundary in a manner that is more substantially consistent with the true color image. Using the PM_2.5 (particle matter with a diameter that is less than 2.5 μm) data monitored by the national air quality monitoring stations as the test source, the results indicated that when the ground-based PM_2.5 ≥ 35 μg/cm³ is considered to be haze days, the samples with the recognition rate that is higher than 85% accounted for 72.22% of the total samples. When PM_2.5 ≥ 50 μg/cm³ is considered as haze days, 83.33% of the samples had an identification rate that was higher than 85%. A cross-comparison with similar research methods showed that the method proposed in this study had better sensitivity to bright surface clear and haze areas. This study will provide a haze mask for subsequent quantitative inversion of aerosol characteristics, and it will further exert the application benefits of MERSI-II instrument aboard on FY3D satellite. Full article

(This article belongs to the Special Issue Aerosol and Atmospheric Correction)

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16 pages, 3744 KiB

Open AccessArticle

Aerosol Retrieval Study from a Particulate Observing Scanning Polarimeter Onboard Gao-Fen 5B without Prior Surface Knowledge, Based on the Optimal Estimation Method

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Remote Sens. 2023, 15(2), 385; https://doi.org/10.3390/rs15020385 - 08 Jan 2023

Viewed by 1281

Abstract

To meet the demand for the aerosol detection of single-angle and multi-band polarization instrument containing short-wave infrared bands, an inversion algorithm that makes full use of multi-band intensity and polarization information is proposed based on optimal estimation theory. This method uses the polarization information in the short-wave infrared band to perform surface and atmosphere decoupling without a prior information on the surface. This obtains the initial value of the aerosol, and then it uses the scalar information to obtain the final result. Moreover, the multi-band information of the instrument is used for decoupling the surface and atmospheric information, which avoids the inversion error caused by the untimely update of the surface reflectance database and the error of spatio-temporal matching. The measured data of the Particulate Observing Scanning Polarimeter (POSP) are used to test the proposed algorithm. Firstly, to verify the effectiveness of the algorithm under different surface conditions, four regions with large geographical differences (Beijing, Hefei, Baotou, and Taiwan) are selected for aerosol optical depth (AOD) inversion, and they are compared with the aerosol robotic network (AERONET) products of the nearby stations. The validation against the AERONET products produces high correlation coefficients of 0.982, 0.986, 0.718, and 0.989, respectively, which verifies the effectiveness of the algorithm in different regions. Further, we analyzed the effectiveness of the proposed algorithm under different pollution conditions. Regions with AOD >0.7 and AOD < 0.7 are screened by using the AOD products of the Moderate-Resolution Imaging Spectroradiomete (MODIS), and the AOD of the corresponding region is inverted using POSP data. It was found to be spatially consistent with the MODIS products. The correlation coefficient and root mean square error (RMSE) in the AOD high region were 0.802 and 0.217, respectively, and 0.944 and 0.022 in the AOD low region, respectively, which verified the effectiveness of the proposed algorithm under different pollution conditions. Full article

(This article belongs to the Special Issue Aerosol and Atmospheric Correction)

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18 pages, 5702 KiB

Open AccessArticle

Evaluation of MODIS DT, DB, and MAIAC Aerosol Products over Different Land Cover Types in the Yangtze River Delta of China

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Remote Sens. 2023, 15(1), 275; https://doi.org/10.3390/rs15010275 - 03 Jan 2023

Cited by 2 | Viewed by 1508

Abstract

The Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) has been widely used in atmospheric environment and climate change research. Based on data of the Aerosol Robotic Network and Sun–Sky Radiometer Observation Network in the Yangtze River Delta, the retrieval accuracies of MODIS C6.1 Dark Target (DT), Deep Blue (DB), and C6.0 Multi-angle Implementation of Atmospheric Correction (MAIAC) products under different land cover types, aerosol types, and observation geometries were analyzed. About 65.64% of MAIAC AOD is within the expected error (Within EE), which is significantly higher than 41.43% for DT and 56.98% for DB. The DT product accuracy varies most obviously with the seasons, and the Within EE in winter is more than three times that in spring. The DB and MAIAC products have low accuracy in summer but high in other seasons. The accuracy of the DT product gradually decreases with the increase in urban and water land-cover proportion. After being corrected by bias and mean relative error, the DT accuracy is significantly improved, and the Within EE increases by 24.12% and 32.33%, respectively. The observation geometries and aerosol types were also examined to investigate their effects on AOD retrieval. Full article

(This article belongs to the Special Issue Aerosol and Atmospheric Correction)

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17 pages, 3456 KiB

Open AccessArticle

Analysis of Long-Term Aerosol Optical Properties Combining AERONET Sunphotometer and Satellite-Based Observations in Hong Kong

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Remote Sens. 2022, 14(20), 5220; https://doi.org/10.3390/rs14205220 - 18 Oct 2022

Cited by 6 | Viewed by 1449

Abstract

This study analyzes seasonal characteristics and long-term variations in aerosol optical parameters in Hong Kong from 2006 to 2021 using AERONET data and satellite-based observations based on the extreme-point symmetric mode decomposition (ESMD) model. The dominant aerosol types in Hong Kong are mixed aerosols and urban/industrial aerosols with fine-mode sizes, and slightly absorbing or non-absorbing properties. Aerosol optical depth (AOD), Angstrom exponent (AE) and single scattering albedo (SSA) varied seasonally with a lower AOD but higher AE and SSA in summer, and elevated AOD but lower AE and SSA in spring and winter. The long-term variations show the year 2012 to be a turning point, with an upward trend in AOD and AE before 2012 and then downwards after 2012. However, for SSA, a rising trend was exhibited in both pre- and post-2012 periods, but with a larger gradient in the first period. The ESMD analysis shows shorter-term, non-linear fluctuations in aerosol optical parameters, with alternating increasing and declining trends. The examination of the relationships between AOD and meteorological factors based on the extreme gradient boosting (XGBoost) method shows that the effects of weather conditions on AOD are complex and non-monotonic. A lower relative humidity, higher wind speed in southwest directions and lower temperature are beneficial to the abatement of aerosol loads in Hong Kong. In conclusion, the findings of this study enhance the understanding of aerosol properties and the interactions between aerosol loading and meteorological factors. Full article

(This article belongs to the Special Issue Aerosol and Atmospheric Correction)

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24 pages, 8244 KiB

Open AccessArticle

Impact of Atmospheric Correction Methods Parametrization on Soil Organic Carbon Estimation Based on Hyperion Hyperspectral Data

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Remote Sens. 2022, 14(20), 5117; https://doi.org/10.3390/rs14205117 - 13 Oct 2022

Cited by 2 | Viewed by 1182

Abstract

Visible Near infrared and Shortwave Infrared (VNIR/SWIR, 400–2500 nm) remote sensing data is becoming a tool for topsoil properties mapping, bringing spatial information for environmental modeling and land use management. These topsoil properties estimates are based on regression models, linking a key topsoil property to VNIR/SWIR reflectance data. Therefore, the regression model’s performances depend on the quality of both topsoil property analysis (measured on laboratory over-ground soil samples) and Bottom-of-Atmosphere (BOA) VNIR/SWIR reflectance which are retrieved from Top-Of-Atmosphere radiance using atmospheric correction (AC) methods. This paper examines the sensitivity of soil organic carbon (SOC) estimation to BOA images depending on two parameters used in AC methods: aerosol optical depth (AOD) in the FLAASH (Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes) method and water vapor (WV) in the ATCOR (ATmospheric CORrection) method. This work was based on Earth Observing-1 Hyperion Hyperspectral data acquired over a cultivated area in Australia in 2006. Hyperion radiance data were converted to BOA reflectance using seven values of AOD (from 0.2 to 1.4) and six values of WV (from 0.4 to 5 cm), in FLAASH and ATCOR, respectively. Then a Partial Least Squares regression (PLSR) model was built from each Hyperion BOA data to estimate SOC over bare soil pixels. This study demonstrated that the PLSR models were insensitive to the AOD variation used in the FLAASH method, with R²_cv and RMSE_cv of 0.79 and 0.4%, respectively. The PLSR models were slightly sensitive to the WV variation used in the ATCOR method, with R²_cv ranging from 0.72 to 0.79 and RMSE_cv ranging from 0.41 to 0.47. Regardless of the AOD values, the PLSR model based on the best parametrization of the ATCOR model provided similar SOC prediction accuracy to PLSR models using the FLAASH method. Variation in AOD using the FLAASH method did not impact the identification of bare soil pixels coverage which corresponded to 82.35% of the study area, while a variation in WV using the ATCOR method provided a variation of bare soil pixels coverage from 75.04 to 84.04%. Therefore, this work recommends (1) the use of the FLAASH AC method to provide BOA reflectance values from Earth Observing-1 Hyperion Hyperspectral data before SOC mapping or (2) a careful selection of the WV parameter when using ATCOR. Full article

(This article belongs to the Special Issue Aerosol and Atmospheric Correction)

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13 pages, 26284 KiB

Open AccessTechnical Note

The Detection of Desert Aerosol Incorporating Coherent Doppler Wind Lidar and Rayleigh–Mie–Raman Lidar

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Remote Sens. 2023, 15(23), 5453; https://doi.org/10.3390/rs15235453 - 22 Nov 2023

Viewed by 295

Abstract

Characterization of aerosol transportation is important in order to understand regional and global climatic changes. To obtain accurate aerosol profiles and wind profiles, aerosol lidar and Doppler wind lidar are generally combined in atmospheric measurements. In this work, a method for calibration and quantitative aerosol properties using coherent Doppler wind lidar (CDWL) is adopted, and data retrieval is verified by contrasting the process with synchronous Rayleigh–Mie–Raman lidar (RMRL). The comparison was applied to field measurements in the Taklimakan desert, from 16 to 21 February 2023. Good agreements between the two lidars was found, with the determination coefficients of 0.90 and 0.89 and the root-mean-square error (RMSE) values of 0.012 and 0.013. The comparative results of continuous experiments demonstrate the ability of the CDWL to retrieve aerosol properties accurately. Full article

(This article belongs to the Special Issue Aerosol and Atmospheric Correction)

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