Assessment of Normalized Water-Leaving Radiance Derived From Goci Using AERONET-OC Data
The geostationary ocean color imager (GOCI), as the world’s first operational geostationary ocean color sensor, is aiming at monitoring short-term and small-scale changes of waters over the northwestern Pacific Ocean. Before assessing its capability of detecting subdiurnal changes of seawater proper...
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ftsouthmissispun:oai:aquila.usm.edu:fac_pubs-20170 2023-07-30T03:55:33+02:00 Assessment of Normalized Water-Leaving Radiance Derived From Goci Using AERONET-OC Data He, Mingjun He, Shuangyan Zhang, Xiaodong Zhou, Feng Li, Peiliang 2021-05-01T07:00:00Z application/pdf https://aquila.usm.edu/fac_pubs/18856 https://aquila.usm.edu/context/fac_pubs/article/20170/viewcontent/pdf.pdf unknown The Aquila Digital Community https://aquila.usm.edu/fac_pubs/18856 https://aquila.usm.edu/context/fac_pubs/article/20170/viewcontent/pdf.pdf Faculty Publications Atmospheric correction GDPS Geostationary ocean color imager (GOCI) Normalized water-leaving radiance SeaDAS Geography Remote Sensing Social and Behavioral Sciences text 2021 ftsouthmissispun 2023-07-15T18:55:30Z The geostationary ocean color imager (GOCI), as the world’s first operational geostationary ocean color sensor, is aiming at monitoring short-term and small-scale changes of waters over the northwestern Pacific Ocean. Before assessing its capability of detecting subdiurnal changes of seawater properties, a fundamental understanding of the uncertainties of normalized water-leaving radiance (nLw) products introduced by atmospheric correction algorithms is necessarily required. This paper presents the uncertainties by accessing GOCI-derived nLw products generated by two commonly used operational atmospheric algorithms, the Korea Ocean Satellite Center (KOSC) standard atmospheric algorithm adopted in GOCI Data Processing System (GDPS) and the NASA standard atmospheric algorithm implemented in Sea-Viewing Wide Field-of-View Sensor Data Analysis System (SeaDAS/l2gen package), with Aerosol Robotic Network Ocean Color (AERONET-OC) provided nLw data. The nLw data acquired from the GOCI sensor based on two algorithms and four AERONET-OC sites of Ariake, Ieodo, Socheongcho, and Gageocho from October 2011 to March 2019 were obtained, matched, and analyzed. The GDPS-generated nLw data are slightly better than that with SeaDAS at visible bands; however, the mean percentage relative errors for both algorithms at blue bands are over 30%. The nLw data derived by GDPS is of better quality both in clear and turbid water, although underestimation is observed at near-infrared (NIR) band (865 nm) in turbid water. The nLw data derived by SeaDAS are underestimated in both clear and turbid water, and the underestimation worsens toward short visible bands. Moreover, both algorithms perform better at noon (02 and 03 Universal Time Coordinated (UTC)), and worse in the early morning and late afternoon. It is speculated that the uncertainties in nLw measurements arose from aerosol models, NIR water-leaving radiance correction method, and bidirectional reflectance distribution function (BRDF) correction method in corresponding atmospheric ... Text Aerosol Robotic Network The University of Southern Mississippi: The Aquila Digital Community Pacific |
institution |
Open Polar |
collection |
The University of Southern Mississippi: The Aquila Digital Community |
op_collection_id |
ftsouthmissispun |
language |
unknown |
topic |
Atmospheric correction GDPS Geostationary ocean color imager (GOCI) Normalized water-leaving radiance SeaDAS Geography Remote Sensing Social and Behavioral Sciences |
spellingShingle |
Atmospheric correction GDPS Geostationary ocean color imager (GOCI) Normalized water-leaving radiance SeaDAS Geography Remote Sensing Social and Behavioral Sciences He, Mingjun He, Shuangyan Zhang, Xiaodong Zhou, Feng Li, Peiliang Assessment of Normalized Water-Leaving Radiance Derived From Goci Using AERONET-OC Data |
topic_facet |
Atmospheric correction GDPS Geostationary ocean color imager (GOCI) Normalized water-leaving radiance SeaDAS Geography Remote Sensing Social and Behavioral Sciences |
description |
The geostationary ocean color imager (GOCI), as the world’s first operational geostationary ocean color sensor, is aiming at monitoring short-term and small-scale changes of waters over the northwestern Pacific Ocean. Before assessing its capability of detecting subdiurnal changes of seawater properties, a fundamental understanding of the uncertainties of normalized water-leaving radiance (nLw) products introduced by atmospheric correction algorithms is necessarily required. This paper presents the uncertainties by accessing GOCI-derived nLw products generated by two commonly used operational atmospheric algorithms, the Korea Ocean Satellite Center (KOSC) standard atmospheric algorithm adopted in GOCI Data Processing System (GDPS) and the NASA standard atmospheric algorithm implemented in Sea-Viewing Wide Field-of-View Sensor Data Analysis System (SeaDAS/l2gen package), with Aerosol Robotic Network Ocean Color (AERONET-OC) provided nLw data. The nLw data acquired from the GOCI sensor based on two algorithms and four AERONET-OC sites of Ariake, Ieodo, Socheongcho, and Gageocho from October 2011 to March 2019 were obtained, matched, and analyzed. The GDPS-generated nLw data are slightly better than that with SeaDAS at visible bands; however, the mean percentage relative errors for both algorithms at blue bands are over 30%. The nLw data derived by GDPS is of better quality both in clear and turbid water, although underestimation is observed at near-infrared (NIR) band (865 nm) in turbid water. The nLw data derived by SeaDAS are underestimated in both clear and turbid water, and the underestimation worsens toward short visible bands. Moreover, both algorithms perform better at noon (02 and 03 Universal Time Coordinated (UTC)), and worse in the early morning and late afternoon. It is speculated that the uncertainties in nLw measurements arose from aerosol models, NIR water-leaving radiance correction method, and bidirectional reflectance distribution function (BRDF) correction method in corresponding atmospheric ... |
format |
Text |
author |
He, Mingjun He, Shuangyan Zhang, Xiaodong Zhou, Feng Li, Peiliang |
author_facet |
He, Mingjun He, Shuangyan Zhang, Xiaodong Zhou, Feng Li, Peiliang |
author_sort |
He, Mingjun |
title |
Assessment of Normalized Water-Leaving Radiance Derived From Goci Using AERONET-OC Data |
title_short |
Assessment of Normalized Water-Leaving Radiance Derived From Goci Using AERONET-OC Data |
title_full |
Assessment of Normalized Water-Leaving Radiance Derived From Goci Using AERONET-OC Data |
title_fullStr |
Assessment of Normalized Water-Leaving Radiance Derived From Goci Using AERONET-OC Data |
title_full_unstemmed |
Assessment of Normalized Water-Leaving Radiance Derived From Goci Using AERONET-OC Data |
title_sort |
assessment of normalized water-leaving radiance derived from goci using aeronet-oc data |
publisher |
The Aquila Digital Community |
publishDate |
2021 |
url |
https://aquila.usm.edu/fac_pubs/18856 https://aquila.usm.edu/context/fac_pubs/article/20170/viewcontent/pdf.pdf |
geographic |
Pacific |
geographic_facet |
Pacific |
genre |
Aerosol Robotic Network |
genre_facet |
Aerosol Robotic Network |
op_source |
Faculty Publications |
op_relation |
https://aquila.usm.edu/fac_pubs/18856 https://aquila.usm.edu/context/fac_pubs/article/20170/viewcontent/pdf.pdf |
_version_ |
1772817540083351552 |