Analyzing Performances of Different Atmospheric Correction Techniques for Landsat 8: Application for Coastal Remote Sensing
Ocean colour (OC) remote sensing is important for monitoring marine ecosystems. However, inverting the OC signal from the top-of-atmosphere (TOA) radiance measured by satellite sensors remains a challenge as the retrieval accuracy is highly dependent on the performance of the atmospheric correction...
Published in: | Remote Sensing |
---|---|
Main Authors: | , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
MDPI AG
2019
|
Subjects: | |
Online Access: | https://doi.org/10.3390/rs11040469 https://doaj.org/article/a606ac62982b4867a9e7905024cb6c96 |
id |
ftdoajarticles:oai:doaj.org/article:a606ac62982b4867a9e7905024cb6c96 |
---|---|
record_format |
openpolar |
spelling |
ftdoajarticles:oai:doaj.org/article:a606ac62982b4867a9e7905024cb6c96 2023-05-15T13:06:49+02:00 Analyzing Performances of Different Atmospheric Correction Techniques for Landsat 8: Application for Coastal Remote Sensing Christopher O. Ilori Nima Pahlevan Anders Knudby 2019-02-01T00:00:00Z https://doi.org/10.3390/rs11040469 https://doaj.org/article/a606ac62982b4867a9e7905024cb6c96 EN eng MDPI AG https://www.mdpi.com/2072-4292/11/4/469 https://doaj.org/toc/2072-4292 2072-4292 doi:10.3390/rs11040469 https://doaj.org/article/a606ac62982b4867a9e7905024cb6c96 Remote Sensing, Vol 11, Iss 4, p 469 (2019) atmospheric correction remote sensing reflectance Landsat 8 band adjustment validation AERONET-OC Science Q article 2019 ftdoajarticles https://doi.org/10.3390/rs11040469 2022-12-31T16:17:37Z Ocean colour (OC) remote sensing is important for monitoring marine ecosystems. However, inverting the OC signal from the top-of-atmosphere (TOA) radiance measured by satellite sensors remains a challenge as the retrieval accuracy is highly dependent on the performance of the atmospheric correction as well as sensor calibration. In this study, the performances of four atmospheric correction (AC) algorithms, the Atmospheric and Radiometric Correction of Satellite Imagery (ARCSI), Atmospheric Correction for OLI ‘lite’ (ACOLITE), Landsat 8 Surface Reflectance (LSR) Climate Data Record (Landsat CDR), herein referred to as LaSRC (Landsat 8 Surface Reflectance Code), and the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) Data Analysis System (SeaDAS), implemented for Landsat 8 Operational Land Imager (OLI) data, were evaluated. The OLI-derived remote sensing reflectance ( R rs ) products (also known as Level-2 products) were tested against near-simultaneous in-situ data acquired from the OC component of the Aerosol Robotic Network (AERONET-OC). Analyses of the match-ups revealed that generic atmospheric correction methods (i.e., ARCSI and LaSRC), which perform reasonably well over land, provide inaccurate Level-2 products over coastal waters, in particular, in the blue bands. Between water-specific AC methods (i.e., SeaDAS and ACOLITE), SeaDAS was found to perform better over complex waters with root-mean-square error (RMSE) varying from 0.0013 to 0.0005 sr −1 for the 443 and 655 nm channels, respectively. An assessment of the effects of dominant environmental variables revealed AC retrieval errors were influenced by the solar zenith angle and wind speed for ACOLITE and SeaDAS in the 443 and 482 nm channels. Recognizing that the AERONET-OC sites are not representative of inland waters, extensive research and analyses are required to further evaluate the performance of various AC methods for high-resolution imagers like Landsat 8 and Sentinel-2 under a broad range of aquatic/atmospheric conditions. Article in Journal/Newspaper Aerosol Robotic Network Directory of Open Access Journals: DOAJ Articles Remote Sensing 11 4 469 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
atmospheric correction remote sensing reflectance Landsat 8 band adjustment validation AERONET-OC Science Q |
spellingShingle |
atmospheric correction remote sensing reflectance Landsat 8 band adjustment validation AERONET-OC Science Q Christopher O. Ilori Nima Pahlevan Anders Knudby Analyzing Performances of Different Atmospheric Correction Techniques for Landsat 8: Application for Coastal Remote Sensing |
topic_facet |
atmospheric correction remote sensing reflectance Landsat 8 band adjustment validation AERONET-OC Science Q |
description |
Ocean colour (OC) remote sensing is important for monitoring marine ecosystems. However, inverting the OC signal from the top-of-atmosphere (TOA) radiance measured by satellite sensors remains a challenge as the retrieval accuracy is highly dependent on the performance of the atmospheric correction as well as sensor calibration. In this study, the performances of four atmospheric correction (AC) algorithms, the Atmospheric and Radiometric Correction of Satellite Imagery (ARCSI), Atmospheric Correction for OLI ‘lite’ (ACOLITE), Landsat 8 Surface Reflectance (LSR) Climate Data Record (Landsat CDR), herein referred to as LaSRC (Landsat 8 Surface Reflectance Code), and the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) Data Analysis System (SeaDAS), implemented for Landsat 8 Operational Land Imager (OLI) data, were evaluated. The OLI-derived remote sensing reflectance ( R rs ) products (also known as Level-2 products) were tested against near-simultaneous in-situ data acquired from the OC component of the Aerosol Robotic Network (AERONET-OC). Analyses of the match-ups revealed that generic atmospheric correction methods (i.e., ARCSI and LaSRC), which perform reasonably well over land, provide inaccurate Level-2 products over coastal waters, in particular, in the blue bands. Between water-specific AC methods (i.e., SeaDAS and ACOLITE), SeaDAS was found to perform better over complex waters with root-mean-square error (RMSE) varying from 0.0013 to 0.0005 sr −1 for the 443 and 655 nm channels, respectively. An assessment of the effects of dominant environmental variables revealed AC retrieval errors were influenced by the solar zenith angle and wind speed for ACOLITE and SeaDAS in the 443 and 482 nm channels. Recognizing that the AERONET-OC sites are not representative of inland waters, extensive research and analyses are required to further evaluate the performance of various AC methods for high-resolution imagers like Landsat 8 and Sentinel-2 under a broad range of aquatic/atmospheric conditions. |
format |
Article in Journal/Newspaper |
author |
Christopher O. Ilori Nima Pahlevan Anders Knudby |
author_facet |
Christopher O. Ilori Nima Pahlevan Anders Knudby |
author_sort |
Christopher O. Ilori |
title |
Analyzing Performances of Different Atmospheric Correction Techniques for Landsat 8: Application for Coastal Remote Sensing |
title_short |
Analyzing Performances of Different Atmospheric Correction Techniques for Landsat 8: Application for Coastal Remote Sensing |
title_full |
Analyzing Performances of Different Atmospheric Correction Techniques for Landsat 8: Application for Coastal Remote Sensing |
title_fullStr |
Analyzing Performances of Different Atmospheric Correction Techniques for Landsat 8: Application for Coastal Remote Sensing |
title_full_unstemmed |
Analyzing Performances of Different Atmospheric Correction Techniques for Landsat 8: Application for Coastal Remote Sensing |
title_sort |
analyzing performances of different atmospheric correction techniques for landsat 8: application for coastal remote sensing |
publisher |
MDPI AG |
publishDate |
2019 |
url |
https://doi.org/10.3390/rs11040469 https://doaj.org/article/a606ac62982b4867a9e7905024cb6c96 |
genre |
Aerosol Robotic Network |
genre_facet |
Aerosol Robotic Network |
op_source |
Remote Sensing, Vol 11, Iss 4, p 469 (2019) |
op_relation |
https://www.mdpi.com/2072-4292/11/4/469 https://doaj.org/toc/2072-4292 2072-4292 doi:10.3390/rs11040469 https://doaj.org/article/a606ac62982b4867a9e7905024cb6c96 |
op_doi |
https://doi.org/10.3390/rs11040469 |
container_title |
Remote Sensing |
container_volume |
11 |
container_issue |
4 |
container_start_page |
469 |
_version_ |
1766022341986353152 |