Evaluation of atmospheric correction methods for low to high resolutions satellite remote sensing data
The objective of atmospheric correction is to retrieve surface reflectance from the top of atmosphere (TOA) reflectance. However, estimating surface reflectance from the TOA reflectance satellite data requires knowledge about the state of the atmosphere (e.g., water vapor and ozone) and the contribu...
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ftunivsussex:oai:sro.sussex.ac.uk:95376 2023-07-30T03:55:33+02:00 Evaluation of atmospheric correction methods for low to high resolutions satellite remote sensing data Nazeer, Majid Ilori, Christopher Olayinka Bilal, Muhammad Nichol, Janet Elizabeth Wu, Weicheng Qiu, Zhongfeng Gayene, Bijoy Krishna 2021-02-01 application/pdf http://sro.sussex.ac.uk/id/eprint/95376/ http://sro.sussex.ac.uk/id/eprint/95376/3/Atmosph_Res_Accepted_version.pdf https://doi.org/10.1016/j.atmosres.2020.105308 en eng Elsevier http://sro.sussex.ac.uk/id/eprint/95376/3/Atmosph_Res_Accepted_version.pdf Nazeer, Majid, Ilori, Christopher Olayinka, Bilal, Muhammad, Nichol, Janet Elizabeth, Wu, Weicheng, Qiu, Zhongfeng and Gayene, Bijoy Krishna (2021) Evaluation of atmospheric correction methods for low to high resolutions satellite remote sensing data. Atmospheric Research, 249. a105308. ISSN 0169-8095 cc_by_nc_nd_4 Article PeerReviewed 2021 ftunivsussex https://doi.org/10.1016/j.atmosres.2020.105308 2023-07-11T20:43:20Z The objective of atmospheric correction is to retrieve surface reflectance from the top of atmosphere (TOA) reflectance. However, estimating surface reflectance from the TOA reflectance satellite data requires knowledge about the state of the atmosphere (e.g., water vapor and ozone) and the contribution of aerosols to the atmospheric path radiance. Thus, obtaining precise measurements of these parameters, which is very difficult, is crucial for accurate estimation of surface reflectance. The SREM (Simplified and Robust Surface Reflectance Estimation Method) is a physical-based atmospheric correction method based on the Radiative transfer (RT) equations of the second simulation of the Satellite Signal in Solar Spectrum (6SV). Essentially the SREM is a simplified version of 6SV which does not require Aerosol Optical Depth (AOD), aerosol type, water vapor, and ozone. An initial study showed accuracy comparable to the Landsat operational Surface Reflectance Products (SRProd) which is generated through different RT models using AOD, water vapor, and ozone data. To further validate the SREM under varying atmospheric conditions and at different spatial resolutions, an independent Reference Surface Reflectance (SRRef) dataset was generated using the AERONET (Aerosol Robotic Network) measurements as input to the 6SV RT model. The surface reflectances estimated by SREM (SRSREM) and SRProd from Planet Scope (PS, at 3 m spatial resolution), Sentinel-2 AB (S2AB) Multi-spectral Instrument (MSI, at 10 to 60 m spatial resolution), and Landsat-8 (L8) operational Land Imager (OLI, at 30 m spatial resolution) were validated against SRRef. Results showed that SRSREM performed similar to the SRProd of PS, S2AB MSI, and L8 OLI against SRRef. An inferior performance (R of 0.35 and 0.57) of L8 OLI's SRProd in the coastal blue (SB1) and blue (SB2) bands was observed, compared to SREM. The comparison of SRSREM with SRProd reveals the robustness of SREM, without using AOD, water vapor, and ozone data, for estimation of surface reflectance ... Article in Journal/Newspaper Aerosol Robotic Network University of Sussex: Sussex Research Online Atmospheric Research 249 105308 |
institution |
Open Polar |
collection |
University of Sussex: Sussex Research Online |
op_collection_id |
ftunivsussex |
language |
English |
description |
The objective of atmospheric correction is to retrieve surface reflectance from the top of atmosphere (TOA) reflectance. However, estimating surface reflectance from the TOA reflectance satellite data requires knowledge about the state of the atmosphere (e.g., water vapor and ozone) and the contribution of aerosols to the atmospheric path radiance. Thus, obtaining precise measurements of these parameters, which is very difficult, is crucial for accurate estimation of surface reflectance. The SREM (Simplified and Robust Surface Reflectance Estimation Method) is a physical-based atmospheric correction method based on the Radiative transfer (RT) equations of the second simulation of the Satellite Signal in Solar Spectrum (6SV). Essentially the SREM is a simplified version of 6SV which does not require Aerosol Optical Depth (AOD), aerosol type, water vapor, and ozone. An initial study showed accuracy comparable to the Landsat operational Surface Reflectance Products (SRProd) which is generated through different RT models using AOD, water vapor, and ozone data. To further validate the SREM under varying atmospheric conditions and at different spatial resolutions, an independent Reference Surface Reflectance (SRRef) dataset was generated using the AERONET (Aerosol Robotic Network) measurements as input to the 6SV RT model. The surface reflectances estimated by SREM (SRSREM) and SRProd from Planet Scope (PS, at 3 m spatial resolution), Sentinel-2 AB (S2AB) Multi-spectral Instrument (MSI, at 10 to 60 m spatial resolution), and Landsat-8 (L8) operational Land Imager (OLI, at 30 m spatial resolution) were validated against SRRef. Results showed that SRSREM performed similar to the SRProd of PS, S2AB MSI, and L8 OLI against SRRef. An inferior performance (R of 0.35 and 0.57) of L8 OLI's SRProd in the coastal blue (SB1) and blue (SB2) bands was observed, compared to SREM. The comparison of SRSREM with SRProd reveals the robustness of SREM, without using AOD, water vapor, and ozone data, for estimation of surface reflectance ... |
format |
Article in Journal/Newspaper |
author |
Nazeer, Majid Ilori, Christopher Olayinka Bilal, Muhammad Nichol, Janet Elizabeth Wu, Weicheng Qiu, Zhongfeng Gayene, Bijoy Krishna |
spellingShingle |
Nazeer, Majid Ilori, Christopher Olayinka Bilal, Muhammad Nichol, Janet Elizabeth Wu, Weicheng Qiu, Zhongfeng Gayene, Bijoy Krishna Evaluation of atmospheric correction methods for low to high resolutions satellite remote sensing data |
author_facet |
Nazeer, Majid Ilori, Christopher Olayinka Bilal, Muhammad Nichol, Janet Elizabeth Wu, Weicheng Qiu, Zhongfeng Gayene, Bijoy Krishna |
author_sort |
Nazeer, Majid |
title |
Evaluation of atmospheric correction methods for low to high resolutions satellite remote sensing data |
title_short |
Evaluation of atmospheric correction methods for low to high resolutions satellite remote sensing data |
title_full |
Evaluation of atmospheric correction methods for low to high resolutions satellite remote sensing data |
title_fullStr |
Evaluation of atmospheric correction methods for low to high resolutions satellite remote sensing data |
title_full_unstemmed |
Evaluation of atmospheric correction methods for low to high resolutions satellite remote sensing data |
title_sort |
evaluation of atmospheric correction methods for low to high resolutions satellite remote sensing data |
publisher |
Elsevier |
publishDate |
2021 |
url |
http://sro.sussex.ac.uk/id/eprint/95376/ http://sro.sussex.ac.uk/id/eprint/95376/3/Atmosph_Res_Accepted_version.pdf https://doi.org/10.1016/j.atmosres.2020.105308 |
genre |
Aerosol Robotic Network |
genre_facet |
Aerosol Robotic Network |
op_relation |
http://sro.sussex.ac.uk/id/eprint/95376/3/Atmosph_Res_Accepted_version.pdf Nazeer, Majid, Ilori, Christopher Olayinka, Bilal, Muhammad, Nichol, Janet Elizabeth, Wu, Weicheng, Qiu, Zhongfeng and Gayene, Bijoy Krishna (2021) Evaluation of atmospheric correction methods for low to high resolutions satellite remote sensing data. Atmospheric Research, 249. a105308. ISSN 0169-8095 |
op_rights |
cc_by_nc_nd_4 |
op_doi |
https://doi.org/10.1016/j.atmosres.2020.105308 |
container_title |
Atmospheric Research |
container_volume |
249 |
container_start_page |
105308 |
_version_ |
1772816694765420544 |