{"refrec":{"BRefID":361411,"RR":"<b>Wei, J.; Wang, M.; Mikelsons, K.; Jiang, L.; Kratzer, S.; Lee, Z.; Moore, T.; Sosik, H.M.; Van der Zande, D.</b> (2022). Global satellite water classification data products over oceanic, coastal, and inland waters. <i>Remote Sens. Environ. 282</i>: 113233. <a href=\"https://dx.doi.org/10.1016/j.rse.2022.113233\" target=\"_blank\">https://dx.doi.org/10.1016/j.rse.2022.113233</a>","BEntID":359127,"PublicFlag":1,"CheckedFlag":1,"wosflag":1,"vabbflag":1,"RefStringPartII":". <i>Remote Sens. Environ. 282</i>: 113233. <a href=\"https://dx.doi.org/10.1016/j.rse.2022.113233\" target=\"_blank\">https://dx.doi.org/10.1016/j.rse.2022.113233</a>","DocTypID":8,"DocType":"Journal article","MarineFlag":0,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Wei, J.; Wang, M.; Mikelsons, K.; Jiang, L.; Kratzer, S.; Lee, Z.; Moore, T.; Sosik, H.M.; Van der Zande, D.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Wei, J. <i>et al.</i>","Englishabstract":"Satellites have generated extensive data of remote sensing reflectance spectra (<i>R<sub>rs</sub>(λ)</i>) covering diverse water classes or types across global waters. Spectral classification of satellite <i>R<sub>rs</sub>(λ)</i> data allows for the distinguishing and grouping of waters with characteristic bio-optical/biogeochemical features that may influence the productivity of a given water body. This study reports new satellite water class products (Level-2 and Level-3) from the Visible Infrared Imaging Radiometer Suite (VIIRS). We developed and implemented a hyperspectral scheme that accounts for the <i>R<sub>rs</sub>(λ)</i> spectral shapes and globally resolves oceanic, coastal, and inland waters into 23 water classes. We characterized the light absorption and scattering coefficients, chlorophyll-a concentration, diffuse attenuation coefficient, and suspended particulate matter for individual water classes. It is shown that the water classes are separable by their distinct bio-optical and biogeochemical properties. Furthermore, validation result suggests that the VIIRS water class products are accurate globally. Finally, we examined the spatial and temporal variability of the water classes in case studies for a demonstration of applications. The water class data in open oceans reveal that the subtropical ocean gyres have experienced dramatic expansion over the last decade. In addition, the water class data appear to be a valuable (and qualitative) indicator for water quality in coastal and inland waters with compelling evidence. We stress that this new satellite product is an excellent addition to the aquatic science database, despite the need for continuous improvement toward perfection.","AbstractOtherLang":null,"BibLvlCode":"AS","StandardTitle":"Global satellite water classification data products over oceanic, coastal, and inland waters","OrigTitleLangCode":"en","OrigTitleLangCodeExtended":"eng","OrigTitleLangID":15,"DateLastModified":{"date":"2026-04-18 01:32:32.538201","timezone_type":1,"timezone":"+02:00"},"UserAccessRight":null,"UserAccID":null,"AuthorKeywords":"Water class; Optical water type; Remote sensing reflectance; Spectral similarity; VIIRS; Hyperspectral","OtherDescriptors":null,"Notes":null,"AnaPub":2022,"MonPub":null,"DateUpdate":"2023-02-24","DateCreate":"2023-02-23","SecASFANote":null,"ConfID":null,"PeerRev":1,"VlizCoreFlag":1,"WoScode":"WOS:000862484800002","VABBcode":null,"OpenAcc":1,"DOI":"10.1016/j.rse.2022.113233"},"refs":null,"anarec":{"AnaID":361411,"PubliDate":2022,"Pagination":"113233","XtraPublOfAnaID":null,"ISBN":null,"Volume":"282","Issue":null,"BRefMon":null,"BRefMonRR":null,"BRefXtra":null,"BRefXtraRR":null,"SerBRefID":43865,"SerRR":"Remote Sensing of Environment. 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