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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| Format: | Article |
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MDPI AG
2021-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/9/1640 |
| _version_ | 1797536628116291584 |
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| author | Mingjun He Shuangyan He Xiaodong Zhang Feng Zhou Peiliang Li |
| author_facet | Mingjun He Shuangyan He Xiaodong Zhang Feng Zhou Peiliang Li |
| author_sort | Mingjun He |
| collection | DOAJ |
| 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 correction procedure. |
| first_indexed | 2024-03-10T12:03:28Z |
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| id | doaj.art-1501f86e72984023828a9567c138ff07 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T12:03:28Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-1501f86e72984023828a9567c138ff072023-11-21T16:44:07ZengMDPI AGRemote Sensing2072-42922021-04-01139164010.3390/rs13091640Assessment of Normalized Water-Leaving Radiance Derived from GOCI Using AERONET-OC DataMingjun He0Shuangyan He1Xiaodong Zhang2Feng Zhou3Peiliang Li4Ocean College, Zhejiang University, Zhoushan 316021, ChinaOcean College, Zhejiang University, Zhoushan 316021, ChinaDivision of Marine Science, School of Ocean Science and Engineering, The University of Southern Mississippi, Stennis Space Center, MS 39529, USAOcean College, Zhejiang University, Zhoushan 316021, ChinaOcean College, Zhejiang University, Zhoushan 316021, ChinaThe 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 correction procedure.https://www.mdpi.com/2072-4292/13/9/1640geostationary ocean color imager (GOCI)GDPSSeaDASnormalized water-leaving radianceatmospheric correction |
| spellingShingle | Mingjun He Shuangyan He Xiaodong Zhang Feng Zhou Peiliang Li Assessment of Normalized Water-Leaving Radiance Derived from GOCI Using AERONET-OC Data Remote Sensing geostationary ocean color imager (GOCI) GDPS SeaDAS normalized water-leaving radiance atmospheric correction |
| title | 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_short | 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 |
| topic | geostationary ocean color imager (GOCI) GDPS SeaDAS normalized water-leaving radiance atmospheric correction |
| url | https://www.mdpi.com/2072-4292/13/9/1640 |
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