Assessment of Landsat atmospheric correction methods for water color applications using global AERONET-OC data

With the longest archive of satellite remote sensing images, the Landsat series of satellites have demonstrated their great potential in aquatic environmental studies. However, although various atmospheric correction (AC) methods have been developed for Landsat observations in water color applicatio...

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Published in:International Journal of Applied Earth Observation and Geoinformation
Main Authors: Yang Xu, Lian Feng, Dan Zhao, Jianzhong Lu
Format: Article in Journal/Newspaper
Language:English
Published: Elsevier 2020
Subjects:
Online Access:https://doi.org/10.1016/j.jag.2020.102192
https://doaj.org/article/26eb05cce985411abd5ccfabe9311c5a
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spelling ftdoajarticles:oai:doaj.org/article:26eb05cce985411abd5ccfabe9311c5a 2023-05-15T13:06:55+02:00 Assessment of Landsat atmospheric correction methods for water color applications using global AERONET-OC data Yang Xu Lian Feng Dan Zhao Jianzhong Lu 2020-12-01T00:00:00Z https://doi.org/10.1016/j.jag.2020.102192 https://doaj.org/article/26eb05cce985411abd5ccfabe9311c5a EN eng Elsevier http://www.sciencedirect.com/science/article/pii/S0303243419313170 https://doaj.org/toc/1569-8432 1569-8432 doi:10.1016/j.jag.2020.102192 https://doaj.org/article/26eb05cce985411abd5ccfabe9311c5a International Journal of Applied Earth Observations and Geoinformation, Vol 93, Iss , Pp 102192- (2020) Landsat AERONET-OC Atmospheric correction Remote sensing reflectance Physical geography GB3-5030 Environmental sciences GE1-350 article 2020 ftdoajarticles https://doi.org/10.1016/j.jag.2020.102192 2022-12-30T21:25:09Z With the longest archive of satellite remote sensing images, the Landsat series of satellites have demonstrated their great potential in aquatic environmental studies. However, although various atmospheric correction (AC) methods have been developed for Landsat observations in water color applications, a comprehensive assessment of their accuracies across different AC methods and instruments has yet to be performed. Using in situ spectral data collected by Aerosol Robotic Network-Ocean Color (AERONET-OC) sites, the performances of five types of AC methods over three different Landsat missions (i.e., Landsat 5/7/8) were evaluated. The Landsat 8 Operational Land Imager (OLI) showed more accurate AC retrievals than the other two instruments, and the results for its green and red bands appeared more reliable than those for the other wavelengths (uncertainty levels of ∼30 %). The iterative NIR algorithm with 2-bands (NIR-SWIR2) model selection embedded in SeaDAS showed the best performances for OLI in two blue bands. Moreover, larger residual errors were found for most Landsat 5/7 bands regardless of the AC methods and spectral bands employed with an uncertainty of >50 %. Interestingly, a simple aerosol subtraction method over the Rayleigh-corrected reflectance (Rrc) outperformed the exponential extrapolation (EXP) algorithms, especially for Landsat 5/7. Neither the image-based AC algorithm nor the surface reflectance (SR) products provided by the United States Geological Survey (USGS) showed acceptable performances over coastal environments. The uncertainties in the various Landsat reflectance products over water surfaces could be associated with a relatively poor signal-to-noise ratio (SNR) in addition to radiometric calibration uncertainties, imperfect aerosol removal methods. Future research is required to collect in situ data across a wider range of water optical properties (particularly more turbid inland waters) to examine the corresponding applicability of Landsat-series observations. Article in Journal/Newspaper Aerosol Robotic Network Directory of Open Access Journals: DOAJ Articles International Journal of Applied Earth Observation and Geoinformation 93 102192
institution Open Polar
collection Directory of Open Access Journals: DOAJ Articles
op_collection_id ftdoajarticles
language English
topic Landsat
AERONET-OC
Atmospheric correction
Remote sensing reflectance
Physical geography
GB3-5030
Environmental sciences
GE1-350
spellingShingle Landsat
AERONET-OC
Atmospheric correction
Remote sensing reflectance
Physical geography
GB3-5030
Environmental sciences
GE1-350
Yang Xu
Lian Feng
Dan Zhao
Jianzhong Lu
Assessment of Landsat atmospheric correction methods for water color applications using global AERONET-OC data
topic_facet Landsat
AERONET-OC
Atmospheric correction
Remote sensing reflectance
Physical geography
GB3-5030
Environmental sciences
GE1-350
description With the longest archive of satellite remote sensing images, the Landsat series of satellites have demonstrated their great potential in aquatic environmental studies. However, although various atmospheric correction (AC) methods have been developed for Landsat observations in water color applications, a comprehensive assessment of their accuracies across different AC methods and instruments has yet to be performed. Using in situ spectral data collected by Aerosol Robotic Network-Ocean Color (AERONET-OC) sites, the performances of five types of AC methods over three different Landsat missions (i.e., Landsat 5/7/8) were evaluated. The Landsat 8 Operational Land Imager (OLI) showed more accurate AC retrievals than the other two instruments, and the results for its green and red bands appeared more reliable than those for the other wavelengths (uncertainty levels of ∼30 %). The iterative NIR algorithm with 2-bands (NIR-SWIR2) model selection embedded in SeaDAS showed the best performances for OLI in two blue bands. Moreover, larger residual errors were found for most Landsat 5/7 bands regardless of the AC methods and spectral bands employed with an uncertainty of >50 %. Interestingly, a simple aerosol subtraction method over the Rayleigh-corrected reflectance (Rrc) outperformed the exponential extrapolation (EXP) algorithms, especially for Landsat 5/7. Neither the image-based AC algorithm nor the surface reflectance (SR) products provided by the United States Geological Survey (USGS) showed acceptable performances over coastal environments. The uncertainties in the various Landsat reflectance products over water surfaces could be associated with a relatively poor signal-to-noise ratio (SNR) in addition to radiometric calibration uncertainties, imperfect aerosol removal methods. Future research is required to collect in situ data across a wider range of water optical properties (particularly more turbid inland waters) to examine the corresponding applicability of Landsat-series observations.
format Article in Journal/Newspaper
author Yang Xu
Lian Feng
Dan Zhao
Jianzhong Lu
author_facet Yang Xu
Lian Feng
Dan Zhao
Jianzhong Lu
author_sort Yang Xu
title Assessment of Landsat atmospheric correction methods for water color applications using global AERONET-OC data
title_short Assessment of Landsat atmospheric correction methods for water color applications using global AERONET-OC data
title_full Assessment of Landsat atmospheric correction methods for water color applications using global AERONET-OC data
title_fullStr Assessment of Landsat atmospheric correction methods for water color applications using global AERONET-OC data
title_full_unstemmed Assessment of Landsat atmospheric correction methods for water color applications using global AERONET-OC data
title_sort assessment of landsat atmospheric correction methods for water color applications using global aeronet-oc data
publisher Elsevier
publishDate 2020
url https://doi.org/10.1016/j.jag.2020.102192
https://doaj.org/article/26eb05cce985411abd5ccfabe9311c5a
genre Aerosol Robotic Network
genre_facet Aerosol Robotic Network
op_source International Journal of Applied Earth Observations and Geoinformation, Vol 93, Iss , Pp 102192- (2020)
op_relation http://www.sciencedirect.com/science/article/pii/S0303243419313170
https://doaj.org/toc/1569-8432
1569-8432
doi:10.1016/j.jag.2020.102192
https://doaj.org/article/26eb05cce985411abd5ccfabe9311c5a
op_doi https://doi.org/10.1016/j.jag.2020.102192
container_title International Journal of Applied Earth Observation and Geoinformation
container_volume 93
container_start_page 102192
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