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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Published in:Remote Sensing
Main Authors: Olivier Hagolle, Mireille Huc, David Villa Pascual, Gerard Dedieu
Format: Text
Language:English
Published: Multidisciplinary Digital Publishing Institute 2015
Subjects:
Online Access:https://doi.org/10.3390/rs70302668
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spelling 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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