A Multi-Temporal and Multi-Spectral Method to Estimate Aerosol Optical Thickness over Land, for the Atmospheric Correction of FormoSat-2, LandSat, VENμS and Sentinel-2 Images
The correction of atmospheric effects is one of the preliminary steps required to make quantitative use of time series of high resolution images from optical remote sensing satellites. An accurate atmospheric correction requires good knowledge of the aerosol optical thickness (AOT) and of the aeroso...
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ftmdpi:oai:mdpi.com:/2072-4292/7/3/2668/ 2023-08-20T03:59:11+02:00 A Multi-Temporal and Multi-Spectral Method to Estimate Aerosol Optical Thickness over Land, for the Atmospheric Correction of FormoSat-2, LandSat, VENμS and Sentinel-2 Images Olivier Hagolle Mireille Huc David Villa Pascual Gerard Dedieu agris 2015-03-09 application/pdf https://doi.org/10.3390/rs70302668 EN eng Multidisciplinary Digital Publishing Institute https://dx.doi.org/10.3390/rs70302668 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 7; Issue 3; Pages: 2668-2691 remote sensing atmospheric correction time series aerosols surface reflectance LandSat FormoSat-2 Sentinel-2,VENμS Text 2015 ftmdpi https://doi.org/10.3390/rs70302668 2023-07-31T20:42:12Z The correction of atmospheric effects is one of the preliminary steps required to make quantitative use of time series of high resolution images from optical remote sensing satellites. An accurate atmospheric correction requires good knowledge of the aerosol optical thickness (AOT) and of the aerosol type. As a first step, this study compares the performances of two kinds of AOT estimation methods applied to FormoSat-2 and LandSat time series of images: a multi-spectral method that assumes a constant relationship between surface reflectance measurements and a multi-temporal method that assumes that the surface reflectances are stable with time. In a second step, these methods are combined to obtain more accurate and robust estimates. The estimated AOTs are compared to in situ measurements on several sites of the AERONET (Aerosol Robotic Network). The methods, based on either spectral or temporal criteria, provide accuracies better than 0.07 in most cases, but show degraded accuracies in some special cases, such as the absence of vegetation for the spectral method or a very quick variation of landscape for the temporal method. The combination of both methods in a new spectro-temporal method increases the robustness of the results in all cases. Text Aerosol Robotic Network MDPI Open Access Publishing Remote Sensing 7 3 2668 2691 |
institution |
Open Polar |
collection |
MDPI Open Access Publishing |
op_collection_id |
ftmdpi |
language |
English |
topic |
remote sensing atmospheric correction time series aerosols surface reflectance LandSat FormoSat-2 Sentinel-2,VENμS |
spellingShingle |
remote sensing atmospheric correction time series aerosols surface reflectance LandSat FormoSat-2 Sentinel-2,VENμS Olivier Hagolle Mireille Huc David Villa Pascual Gerard Dedieu A Multi-Temporal and Multi-Spectral Method to Estimate Aerosol Optical Thickness over Land, for the Atmospheric Correction of FormoSat-2, LandSat, VENμS and Sentinel-2 Images |
topic_facet |
remote sensing atmospheric correction time series aerosols surface reflectance LandSat FormoSat-2 Sentinel-2,VENμS |
description |
The correction of atmospheric effects is one of the preliminary steps required to make quantitative use of time series of high resolution images from optical remote sensing satellites. An accurate atmospheric correction requires good knowledge of the aerosol optical thickness (AOT) and of the aerosol type. As a first step, this study compares the performances of two kinds of AOT estimation methods applied to FormoSat-2 and LandSat time series of images: a multi-spectral method that assumes a constant relationship between surface reflectance measurements and a multi-temporal method that assumes that the surface reflectances are stable with time. In a second step, these methods are combined to obtain more accurate and robust estimates. The estimated AOTs are compared to in situ measurements on several sites of the AERONET (Aerosol Robotic Network). The methods, based on either spectral or temporal criteria, provide accuracies better than 0.07 in most cases, but show degraded accuracies in some special cases, such as the absence of vegetation for the spectral method or a very quick variation of landscape for the temporal method. The combination of both methods in a new spectro-temporal method increases the robustness of the results in all cases. |
format |
Text |
author |
Olivier Hagolle Mireille Huc David Villa Pascual Gerard Dedieu |
author_facet |
Olivier Hagolle Mireille Huc David Villa Pascual Gerard Dedieu |
author_sort |
Olivier Hagolle |
title |
A Multi-Temporal and Multi-Spectral Method to Estimate Aerosol Optical Thickness over Land, for the Atmospheric Correction of FormoSat-2, LandSat, VENμS and Sentinel-2 Images |
title_short |
A Multi-Temporal and Multi-Spectral Method to Estimate Aerosol Optical Thickness over Land, for the Atmospheric Correction of FormoSat-2, LandSat, VENμS and Sentinel-2 Images |
title_full |
A Multi-Temporal and Multi-Spectral Method to Estimate Aerosol Optical Thickness over Land, for the Atmospheric Correction of FormoSat-2, LandSat, VENμS and Sentinel-2 Images |
title_fullStr |
A Multi-Temporal and Multi-Spectral Method to Estimate Aerosol Optical Thickness over Land, for the Atmospheric Correction of FormoSat-2, LandSat, VENμS and Sentinel-2 Images |
title_full_unstemmed |
A Multi-Temporal and Multi-Spectral Method to Estimate Aerosol Optical Thickness over Land, for the Atmospheric Correction of FormoSat-2, LandSat, VENμS and Sentinel-2 Images |
title_sort |
multi-temporal and multi-spectral method to estimate aerosol optical thickness over land, for the atmospheric correction of formosat-2, landsat, venμs and sentinel-2 images |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2015 |
url |
https://doi.org/10.3390/rs70302668 |
op_coverage |
agris |
genre |
Aerosol Robotic Network |
genre_facet |
Aerosol Robotic Network |
op_source |
Remote Sensing; Volume 7; Issue 3; Pages: 2668-2691 |
op_relation |
https://dx.doi.org/10.3390/rs70302668 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs70302668 |
container_title |
Remote Sensing |
container_volume |
7 |
container_issue |
3 |
container_start_page |
2668 |
op_container_end_page |
2691 |
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1774718638995013632 |