TY - JOUR
T1 - Estimating ultraviolet reflectance from visible bands in ocean colour remote sensing
AU - Liu, Huizeng
AU - He, Xianqiang
AU - Li, Qingquan
AU - Kratzer, Susanne
AU - Wang, Junjie
AU - Shi, Tiezhu
AU - Hu, Zhongwen
AU - Yang, Chao
AU - Hu, Shuibo
AU - Zhou, Qiming
AU - Wu, Guofeng
N1 - Funding Information:
We greatly appreciate Prof. Lee Zhongping for his precious comments and suggestions during paper preparation. This study was supported by the Key-Area Research and Development Program of Guangdong Province (No. 2020B1111020005 ), the National Natural Science Foundation of China grants (Grant Nos. 42001281 , 41890854 , 41890852 , 41606199 and 41971386 ) National Key R&D Program of China (No. 2017YFC0506200 ) and Hong Kong Research Grant Council (RGC) General Research Fund ( 12301820 ). We thank the NASA Ocean Biology Processing Group and SeaBASS for archiving and distributing the in situ matchup data. The authors are grateful to the MOBY team for providing the in situ data. The principal investigators, including those responsible for AERONET-OC observatories, are appreciated for sharing their field measurements. Specifically, we thank Joji Ishizaka, Kohei Arai and the JAXA GCOM-C project for maintaining and sharing the Rrs data from AERONET-OC station of ARIAKE_TOWER, and Nima Pahlevan for sharing data from the Grizzly_Bay station, and thank Prof. Giuseppe Zibordi for AERONET-OC stations of Casablana_Platform, Galata_Platform, Gloria, Gustav_dalen_Tower, Helsinki_lighthouse, Irbe_lighthouse and Section_7_Platform. We also would like to acknowledge the Swedish National Space Board, project Dnr 175/17 and 61/17 as well as the European Space Agency ESRIN 12352/08/I-OL and ARGANS ESA MERIS 4thRP.
Funding Information:
We greatly appreciate Prof. Lee Zhongping for his precious comments and suggestions during paper preparation. This study was supported by the Key-Area Research and Development Program of Guangdong Province (No. 2020B1111020005), the National Natural Science Foundation of China grants (Grant Nos. 42001281, 41890854, 41890852, 41606199 and 41971386) National Key R&D Program of China (No. 2017YFC0506200) and Hong Kong Research Grant Council (RGC) General Research Fund (12301820). We thank the NASA Ocean Biology Processing Group and SeaBASS for archiving and distributing the in situ matchup data. The authors are grateful to the MOBY team for providing the in situ data. The principal investigators, including those responsible for AERONET-OC observatories, are appreciated for sharing their field measurements. Specifically, we thank Joji Ishizaka, Kohei Arai and the JAXA GCOM-C project for maintaining and sharing the Rrs data from AERONET-OC station of ARIAKE_TOWER, and Nima Pahlevan for sharing data from the Grizzly_Bay station, and thank Prof. Giuseppe Zibordi for AERONET-OC stations of Casablana_Platform, Galata_Platform, Gloria, Gustav_dalen_Tower, Helsinki_lighthouse, Irbe_lighthouse and Section_7_Platform. We also would like to acknowledge the Swedish National Space Board, project Dnr 175/17 and 61/17 as well as the European Space Agency ESRIN 12352/08/I-OL and ARGANS ESA MERIS 4thRP.
PY - 2021/6/1
Y1 - 2021/6/1
N2 - In recent years, ultraviolet (UV) bands have received increasing attention from the ocean colour remote sensing community, as they may contribute to improving atmospheric correction and inherent optical properties (IOPs) retrieval. However, most ocean colour satellite sensors do not have UV bands, and the accurate retrieval of UV remote sensing reflectance (Rrs) from UV satellite data is still a challenge. In order to address this problem, this study proposes a hybrid approach for estimating UV Rrs from the visible bands. The approach was implemented with two popular ocean colour satellite sensors, i.e. GCOM-C SGLI and Sentinel-3 OLCI. In situ Rrs collected globally and simulated Rrs spectra were used to develop UV Rrs retrieval models, and UV Rrs values at 360, 380 and 400 nm were estimated from visible Rrs spectra. The performances of the established models were evaluated using in situ Rrs and satellite data, and applied to a semi-analytical algorithm for IOPs retrieval. The results showed that: (i) UV Rrs retrieval models had low uncertainties with mean absolute percentage differences (MAPD) less than 5%; (ii) the model assessment with in situ Rrs showed high accuracy (r = 0.92–1.00 and MAPD = 1.11%–10.95%) in both clear open ocean and optically complex waters; (iii) the model assessment with satellite data indicated that model-estimated UV Rrs were more consistent with in situ values than satellite-derived UV Rrs; and (iv) model-estimated UV Rrs may improve the decomposition accuracy of absorption coefficients in semi-analytical IOPs algorithm. Thus, the proposed method has great potentials for reconstructing UV Rrs data and improving IOPs retrieval for historical satellite sensors, and might also be useful for UV-based atmospheric correction algorithms.
AB - In recent years, ultraviolet (UV) bands have received increasing attention from the ocean colour remote sensing community, as they may contribute to improving atmospheric correction and inherent optical properties (IOPs) retrieval. However, most ocean colour satellite sensors do not have UV bands, and the accurate retrieval of UV remote sensing reflectance (Rrs) from UV satellite data is still a challenge. In order to address this problem, this study proposes a hybrid approach for estimating UV Rrs from the visible bands. The approach was implemented with two popular ocean colour satellite sensors, i.e. GCOM-C SGLI and Sentinel-3 OLCI. In situ Rrs collected globally and simulated Rrs spectra were used to develop UV Rrs retrieval models, and UV Rrs values at 360, 380 and 400 nm were estimated from visible Rrs spectra. The performances of the established models were evaluated using in situ Rrs and satellite data, and applied to a semi-analytical algorithm for IOPs retrieval. The results showed that: (i) UV Rrs retrieval models had low uncertainties with mean absolute percentage differences (MAPD) less than 5%; (ii) the model assessment with in situ Rrs showed high accuracy (r = 0.92–1.00 and MAPD = 1.11%–10.95%) in both clear open ocean and optically complex waters; (iii) the model assessment with satellite data indicated that model-estimated UV Rrs were more consistent with in situ values than satellite-derived UV Rrs; and (iv) model-estimated UV Rrs may improve the decomposition accuracy of absorption coefficients in semi-analytical IOPs algorithm. Thus, the proposed method has great potentials for reconstructing UV Rrs data and improving IOPs retrieval for historical satellite sensors, and might also be useful for UV-based atmospheric correction algorithms.
KW - Colour index
KW - Inherent optical properties
KW - Machine learning
KW - Ocean colour remote sensing
KW - Remote sensing reflectance
KW - Ultraviolet
UR - http://www.scopus.com/inward/record.url?scp=85103099824&partnerID=8YFLogxK
U2 - 10.1016/j.rse.2021.112404
DO - 10.1016/j.rse.2021.112404
M3 - Journal article
AN - SCOPUS:85103099824
SN - 0034-4257
VL - 258
JO - Remote Sensing of Environment
JF - Remote Sensing of Environment
M1 - 112404
ER -