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DOI	10.1016/j.rse.2021.112404
	Estimating ultraviolet reflectance from visible bands in ocean colour remote sensing
	Liu H.; He X.; Li Q.; Kratzer S.; Wang J.; Shi T.; Hu Z.; Yang C.; Hu S.; Zhou Q.; Wu G.
发表日期	2021
ISSN	00344257
卷号	258
英文摘要	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. © 2021 Elsevier Inc.
英文关键词	Colour index; Inherent optical properties; Machine learning; Ocean colour remote sensing; Remote sensing reflectance; Ultraviolet
语种	英语
scopus关键词	Color; Learning systems; Reflection; Remote sensing; Satellites; Color index; Inherent optical property; Machine-learning; Ocean colour remote sensing; Optical properties retrieval; Remote-sensing reflectance; Satellite data; Satellite sensors; Ultraviolet; Visible band; Oceanography; accuracy assessment; algorithm; in situ measurement; numerical model; open ocean; optical property; remote sensing; satellite data; satellite imagery; sensor; Sentinel
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/178876
作者单位	MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen, 518060, China; State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou, 310012, China; Department of Ecology, Environment and Plant Sciences, Stockholm University, Stockholm, 106 91, Sweden; Department of Geography, Hong Kong Baptist University, Hong Kong; College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, 518060, China
推荐引用方式 GB/T 7714	Liu H.,He X.,Li Q.,et al. Estimating ultraviolet reflectance from visible bands in ocean colour remote sensing[J],2021,258.
APA	Liu H..,He X..,Li Q..,Kratzer S..,Wang J..,...&Wu G..(2021).Estimating ultraviolet reflectance from visible bands in ocean colour remote sensing.Remote Sensing of Environment,258.
MLA	Liu H.,et al."Estimating ultraviolet reflectance from visible bands in ocean colour remote sensing".Remote Sensing of Environment 258(2021).