Estimating ultraviolet reflectance from visible bands in ocean colour remote sensing

REMOTE SENSING OF ENVIRONMENT

卷:258

Article

Liu, Huizeng^1,2,3,7  He, Xianqiang⁴  Li, Qingquan^1,2,3  Kratzer, Susanne⁵  Wang, Junjie^1,2,3  Shi, Tiezhu^1,2,3  Hu, Zhongwen^1,2,3  Yang, Chao^1,2,3  Hu, Shuibo^1,2,3  Zhou, Qiming⁶  Wu, Guofeng^1,2,3 

[1] Shenzhen Univ, MNR Key Lab Geoenvironm Monitoring Great Bay Area, Shenzhen 518060, Peoples R China

[2] Shenzhen Univ, Guangdong Key Lab Urban Informat, Shenzhen 518060, Peoples R China

[3] Shenzhen Univ, Shenzhen Key Lab Spatial Smart Sensing & Serv, Shenzhen 518060, Peoples R China

[4] Minist Nat Resources, State Key Lab Satellite Ocean Environm Dynam, Inst Oceanog 2, Hangzhou 310012, Peoples R China

[5] Stockholm Univ, Dept Ecol Environm & Plant Sci, S-10691 Stockholm, Sweden

[6] Hong Kong Baptist Univ, Dept Geog, Hong Kong, Peoples R China

[7] Shenzhen Univ, Coll Civil & Transportat Engn, Shenzhen 518060, Peoples R China

关键词: Ultraviolet;    Remote sensing reflectance;    Ocean colour remote sensing;    Colour index;    Machine learning;    Inherent optical properties;   

DOI  :  10.1016/j.rse.2021.112404

来源: Elsevier

【 摘 要 】

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.

【 授权许可】

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