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New Perspectives for Atmospheric Correction: Theory, Methods and Applications

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

Deadline for manuscript submissions: closed (31 December 2019) | Viewed by 19121

Special Issue Editors

University of Maryland
Interests: land surface temperature & emissivity, thermal infrared remote sensing, radiative transfer modeling
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences
Interests: remote sensing of aerosol; remote sensing data processing; land use and landcover change; remote sensing production system R&D
Institute of Environmental Physics, University of Bremen, NW1, Room 2130, Otto-Hahn-Allee 1, 28359 Bremen, Germany
Interests: satelite remote sensing of aerosol and cloud; cryosphere; radiative transfer; retrieval theory; trend analysis
Special Issues, Collections and Topics in MDPI journals
University of Maryland
Interests: radiative transfer modeling; radiation budget; vegetation; sea ice; climate change

Special Issue Information

Dear Colleagues,

Atmospheric correction is critical in deriving land surface biophysical parameters from both optical and thermal infrared remotely sensed data. Radiative transfer model rigorously describes the scattering, absorption and emission characteristics of cloud, aerosols and gasses in the atmosphere, which is the key theory serving for atmospheric parameters retrieving and atmospheric correction.

A variety of empirical and radiative transfer algorithms have been proposed during past decades; however, the cutting-edge atmospheric correction researches are expected to meet the requirement of the quantitative analysis, while many new instruments have been continuously launched and operated, and decades of time series remote sensing data have been accumulated to support faster and better retrieval of atmospheric parameters as well as the improvement of radiative transfer models. To date, the aerosol retrieval at bright surface and sparsely vegetated surface is still full of problems, and atmospheric effect in the thermal infrared retrieval is a challenging issue, in which the quality of land surface temperature and emissivity are largely rely on the accuracy of atmospheric correction. Moreover, fast radiative transfer models attract increasing attentions for the real-time operational atmospheric correction and physical retrieval.

This Special Issue is aimed at the most recent progresses of the following topics, but not limited to:

  • Retrieval of aerosol properties from moderate-high spatial resolution satellite observations or by combining different instruments.
  • Better aerosol optical depth retrieval based on historical accumulated data and validation, especially for bright surface and sparely vegetated surface.
  • Radiative transfer modeling, atmospheric parameters inversion and validation.
  • Novel or operational atmospheric correction algorithms for optical and thermal infrared images.
  • Land surface variables retrieval and evaluation under various atmospheric conditions.

Dr. Heshun Wang
Prof. Bo Zhong
Dr. Linlu Mei
Dr. Jingjing Peng
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 transfer model
  • atmospheric correction
  • aerosol properties retrieval
  • land surface variables inversion

Published Papers (5 papers)

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20 pages, 6159 KiB  
Article
An Atmospheric Correction Using High Resolution Numerical Weather Prediction Models for Satellite-Borne Single-Channel Mid-Wavelength and Thermal Infrared Imaging Sensors
by Hongtak Lee, Joong-Sun Won and Wook Park
Remote Sens. 2020, 12(5), 853; https://doi.org/10.3390/rs12050853 - 06 Mar 2020
Cited by 3 | Viewed by 3042
Abstract
This paper presents a single-channel atmospheric correction method for remotely sensed infrared (wavelength of 3–15 μm) images with various observation angles. The method is based on basic radiative transfer equations with a simple absorption-focused regression model to calculate the optical thickness of each [...] Read more.
This paper presents a single-channel atmospheric correction method for remotely sensed infrared (wavelength of 3–15 μm) images with various observation angles. The method is based on basic radiative transfer equations with a simple absorption-focused regression model to calculate the optical thickness of each atmospheric layer. By employing a simple regression model and re-organization of atmospheric profiles by considering viewing geometry, the proposed method conducts atmospheric correction at every pixel of a numerical weather prediction model in a single step calculation. The Visible Infrared Imaging Radiometer Suite (VIIRS) imaging channel (375 m) I4 (3.55~3.93 μm) and I5 (10.50~12.40 μm) bands were used as mid-wavelength and thermal infrared images to demonstrate the effectiveness of the proposed single-channel atmospheric correction method. The estimated sea surface temperatures (SSTs) obtained by the proposed method with high resolution numerical weather prediction models were compared with sea-truth temperature data from ocean buoys, multichannel-based SST products from VIIRS/MODIS, and results from MODerate resolution atmospheric TRANsmission 5 (MODTRAN 5), for validation. High resolution (1.5 km and 12 km) numerical weather prediction (NWP) models distributed by the Korea Meteorological Administration (KMA) were employed as input atmospheric data. Nighttime SST estimations with the I4 band showed a root mean squared error (RMSE) of 0.95 °C, similar to that of the VIIRS product (RMSE: 0.92 °C) and lower than that of the MODIS product (RMSE: 1.74 °C), while estimations with the I5 band showed an RMSE of 1.81 °C. RMSEs from MODTRAN simulations were similar (within 0.2 °C) to those of the proposed method (I4: 0.81 °C, I5: 1.67 °C). These results demonstrated the competitive performance of a regression-based method using high-resolution numerical weather prediction (NWP) models for atmospheric correction of single-channel infrared imaging sensors. Full article
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13 pages, 15750 KiB  
Article
Dust Storm Remote Sensing Monitoring Supported by MODIS Land Surface Reflectance Database
by Ke Sun, Qinghua Su and Yanfang Ming
Remote Sens. 2019, 11(15), 1772; https://doi.org/10.3390/rs11151772 - 27 Jul 2019
Cited by 15 | Viewed by 6032
Abstract
MODIS (Moderate Resolution Imaging Spectroradiometer) land product subsets can provide high-quality prior knowledge for the quantitative inversion of land and atmospheric parameters. Using the LSR (Land Surface Reflectance) dataset, dust storm remote sensing monitoring in this study was carried out via quality control [...] Read more.
MODIS (Moderate Resolution Imaging Spectroradiometer) land product subsets can provide high-quality prior knowledge for the quantitative inversion of land and atmospheric parameters. Using the LSR (Land Surface Reflectance) dataset, dust storm remote sensing monitoring in this study was carried out via quality control and data synthesis. A dynamic threshold supported dust storm monitoring method was proposed based on a monthly synthesized LSR database, which is produced using MOD09A1 data. The apparent reflectance of clear-pixels with different atmospheric conditions was simulated by the radiative transfer model. A pixel can be identified as a dust pixel if the apparent reflectance is larger than that of the simulated data. The proposed method was applied to the monitoring of four dust storms, the results of which were evaluated and analyzed via visual interpretation, MICAPS (Meteorological Information Comprehensive Analysis and Process System), and the OMI AI (Ozone Monitoring Instrument Aerosol Index) with the following conclusions: the dust storm monitoring results showed that most of the dust areas could be accurately detected when compared with the true color composite images, and the dust monitoring results agreed well with the MICAPS observation station data and the OMI AI dust products. Full article
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27 pages, 6953 KiB  
Article
Evaluation of Four Atmospheric Correction Algorithms for GOCI Images over the Yellow Sea
by Xiaocan Huang, Jianhua Zhu, Bing Han, Cédric Jamet, Zhen Tian, Yili Zhao, Jun Li and Tongji Li
Remote Sens. 2019, 11(14), 1631; https://doi.org/10.3390/rs11141631 - 10 Jul 2019
Cited by 21 | Viewed by 3392
Abstract
Atmospheric correction (AC) for coastal waters is an important issue in ocean color remote sensing. AC performance is fundamental in retrieving reliable water-leaving radiances and then bio-optical parameters. Unlike polar-orbiting satellites, geostationary ocean color sensors allow high-frequency (15–60 min) monitoring of ocean color [...] Read more.
Atmospheric correction (AC) for coastal waters is an important issue in ocean color remote sensing. AC performance is fundamental in retrieving reliable water-leaving radiances and then bio-optical parameters. Unlike polar-orbiting satellites, geostationary ocean color sensors allow high-frequency (15–60 min) monitoring of ocean color over the same area. The first geostationary ocean color sensor, i.e., the Geostationary Ocean Color Imager (GOCI), was launched in 2010. Using GOCI data acquired over the Yellow Sea in summer 2017 at three principal overpass times (02:16, 03:16, 04:16 UTC) with ±1 and ±3 h match-up times, this study compared four GOCI AC algorithms: (1) the standard near infrared (NIR) algorithm of NASA (NASA-STD), (2) the Korea Ocean Satellite Center (KOSC) standard algorithm for GOCI (KOSC-STD), (3) the diffuse attenuation coefficient at 490 nm Kd (490)-based NIR correction algorithm (Kd-based), and (4) the Management Unit of the North Sea Mathematical Models (MUMM). The GOCI-estimated remote sensing reflectance (Rrs), aerosol parameters [aerosol optical thickness (AOT), Angström Exponent (AE)], and chlorophyll-a (Chla) were validated using in situ data. For Rrs, AOT, AE, and Chla, GOCI-retrieved results performed well within the ±1 h temporal window, but the number of match-ups was extended within the ±3 h match-up window. For ±3 h GOCI-derived Rrs, all algorithms had an absolute percentage difference (APD) at 490 and 555 nm of <40%, while other bands showed larger differences (APD > 60%). Compared with in situ values, the APD of the Rrs(490)/Rrs(555) band ratio was <20% for all ACs. For AOT and AE, the APD was >40% and >200%, respectively. Of the four algorithms, the KOSC-STD algorithm demonstrated satisfactory performance in deriving Rrs for the region of interest (Rrs APD: 22.23%–73.95%) in the visible bands. The Kd-based algorithm worked well obtaining Ocean Color 3 GOCI Chla because Rrs(443) is more accurate than the KOSC-STD. The poorest Rrs retrievals were achieved using the NASA-STD and the MUMM algorithms. Statistical analysis indicated that all methods had optimal performance at 04:16 UTC. Full article
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10 pages, 4182 KiB  
Article
Aerosol Optical Depth over the Arctic Snow-Covered Regions Derived from Dual-Viewing Satellite Observations
by Zheng Shi, Tingyan Xing, Jie Guang, Yong Xue and Yahui Che
Remote Sens. 2019, 11(8), 891; https://doi.org/10.3390/rs11080891 - 12 Apr 2019
Cited by 8 | Viewed by 3100
Abstract
Aerosol properties over the Arctic snow-covered regions are sparsely provided by temporal and spatially limited in situ measurements or active Lidar observations. This introduces large uncertainties for the understanding of aerosol effects on Arctic climate change. In this paper, aerosol optical depth (AOD) [...] Read more.
Aerosol properties over the Arctic snow-covered regions are sparsely provided by temporal and spatially limited in situ measurements or active Lidar observations. This introduces large uncertainties for the understanding of aerosol effects on Arctic climate change. In this paper, aerosol optical depth (AOD) is derived using the advanced along-track scanning radiometer (AATSR) instrument. The basic idea is to utilize the dual-viewing observation capability of AATSR to reduce the impacts of AOD uncertainties introduced by the absolute wavelength-dependent error on surface reflectance estimation. AOD is derived assuming that the satellite observed surface reflectance ratio can be well characterized by a snow bidirectional reflectance distribution function (BRDF) model with a certain correction direct from satellite top of the atmosphere (TOA) observation. The aerosol types include an Arctic haze aerosol obtained from campaign measurement and Arctic background aerosol (maritime aerosol) types. The proper aerosol type is selected during the iteration step based on the minimization residual. The algorithm has been used over Spitsbergen for the spring period (April–May) and the AOD spatial distribution indicates that the retrieval AOD can capture the Arctic haze event. The comparison with AERONET observations shows promising results, with a correlation coefficient R = 0.70. The time series analysis shows no systematical biases between AATSR retrieved AOD and AERONET observed ones. Full article
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16 pages, 4975 KiB  
Technical Note
An Atmospheric Correction Method over Bright and Stable Surfaces for Moderate to High Spatial-Resolution Optical Remotely Sensed Imagery
by Bo Zhong, Shanlong Wu, Aixia Yang, Kai Ao, Jinhua Wu, Junjun Wu, Xueshuang Gong, Haibo Wang and Qinhuo Liu
Remote Sens. 2020, 12(4), 733; https://doi.org/10.3390/rs12040733 - 22 Feb 2020
Cited by 2 | Viewed by 2974
Abstract
Although many attempts have been made, it has remained a challenge to retrieve the aerosol optical depth (AOD) at 550 nm from moderate to high spatial-resolution (MHSR) optical remotely sensed imagery in arid areas with bright surfaces, such as deserts and bare ground. [...] Read more.
Although many attempts have been made, it has remained a challenge to retrieve the aerosol optical depth (AOD) at 550 nm from moderate to high spatial-resolution (MHSR) optical remotely sensed imagery in arid areas with bright surfaces, such as deserts and bare ground. Atmospheric correction for remote-sensing images in these areas has not been good. In this paper, we proposed a new algorithm that can effectively estimate the spatial distribution of atmospheric aerosols and retrieve surface reflectance from moderate to high spatial-resolution imagery in arid areas with bright surfaces. Land surface in arid areas is usually bright and stable and the variation of atmosphere in these areas is also very small; consequently, the land-surface characteristics, specifically the bidirectional reflectance distribution factor (BRDF), can be retrieved easily and accurately using time series of satellite images with relatively lower spatial resolution like the Moderate-resolution Imaging Spectroradiometer (MODIS) with 500 m resolution and the retrieved BRDF is then used to retrieve the AOD from MHSR images. This algorithm has three advantages: (i) it is well suited to arid areas with bright surfaces; (ii) it is very efficient because of employed lower resolution BRDF; and (iii) it is completely automatic. The derived AODs from the Multispectral Instrument (MSI) on board Sentinel-2, Landsat 5 Thematic Mapper (TM), Landsat 8 Operational Land Imager (OLI), Gao Fen 1 Wide Field Viewer (GF-1/WFV), Gao Fen 6 Wide Field Viewer (GF-6/WFV), and Huan Jing 1 CCD (HJ-1/CCD) data are validated using ground measurements from 4 stations of the AErosol Robotic NETwork (AERONET) around the world. Full article
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