Robust algorithm for estimating total suspended solids (TSS) in inland and nearshore coastal waters SCIE SCOPUS
DC Field | Value | Language |
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dc.contributor.author | Balasubramanian S.V. | - |
dc.contributor.author | Pahlevan N. | - |
dc.contributor.author | Smith B. | - |
dc.contributor.author | Binding C. | - |
dc.contributor.author | Schalles J. | - |
dc.contributor.author | Loisel H. | - |
dc.contributor.author | Gurlin D. | - |
dc.contributor.author | Greb S. | - |
dc.contributor.author | Alikas K. | - |
dc.contributor.author | Randla M. | - |
dc.contributor.author | Bunkei M. | - |
dc.contributor.author | Moses W. | - |
dc.contributor.author | Nguyễn H. | - |
dc.contributor.author | Lehmann M.K. | - |
dc.contributor.author | O'Donnell D. | - |
dc.contributor.author | Ondrusek M. | - |
dc.contributor.author | Han, Tai Hyun | - |
dc.contributor.author | Fichot C.G. | - |
dc.contributor.author | Moore T. | - |
dc.contributor.author | Boss E. | - |
dc.date.accessioned | 2020-12-10T07:46:18Z | - |
dc.date.available | 2020-12-10T07:46:18Z | - |
dc.date.created | 2020-06-08 | - |
dc.date.issued | 2020-09 | - |
dc.identifier.issn | 0034-4257 | - |
dc.identifier.uri | https://sciwatch.kiost.ac.kr/handle/2020.kiost/38588 | - |
dc.description.abstract | One of the challenging tasks in modern aquatic remote sensing is the retrieval of near-surface concentrations of Total Suspended Solids (TSS). This study aims to present a Statistical, inherent Optical property (IOP)-based, and muLti-conditional Inversion proceDure (SOLID) for enhanced retrievals of satellite-derived TSS under a wide range of in-water bio-optical conditions in rivers, lakes, estuaries, and coastal waters. In this study, using a large in situ database (N > 3500), the SOLID model is devised using a three-step procedure: (a) water-type classification of the input remote sensing reflectance (R-rs), (b) retrieval of particulate backscattering (b(bp)) in the red or near-infrared (NIR) regions using semi-analytical, machine-learning, and empirical models, and (c) estimation of TSS from b(bp) via water-type-specific empirical models. Using an independent subset of our in situ data (N = 2729) with TSS ranging from 0.1 to 2626.8 [g/m(3)], the SOLID model is thoroughly examined and compared against several state-of-the-art algorithms (Miller and McKee, 2004; Nechad et al., 2010; Novoa et al., 2017; Ondrusek et al., 2012; Petus et al., 2010). We show that SOLID outperforms all the other models to varying degrees, i.e.,from 10 to> 100%, depending on the statistical attributes (e.g., global versus water-type-specific metrics). For demonstration purposes, the model is implemented for images acquired by the MultiSpectral Imager aboard Sentinel-2A/B over the Chesapeake Bay, San-Francisco-Bay-Delta Estuary, Lake Okeechobee, and Lake Taihu. To enable generating consistent, multimission TSS products, its performance is further extended to, and evaluated for, other missions, such as the Ocean and Land Color Instrument (OLCI), Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), and Operational Land Imager (OLI). Sensitivity analyses on uncertainties induced by the atmospheric correction indicate that 10% uncertainty in R-rs leads to< 20% uncertainty in TSS retrievals from SOLID. While this study suggests that SOLID has a potential for producing TSS products in global coastal and inland waters, our statistical analysis certainly verifies that there is still a need for improving retrievals across a wide spectrum of particle loads. | - |
dc.description.uri | 1 | - |
dc.language | English | - |
dc.publisher | Elsevier Inc. | - |
dc.title | Robust algorithm for estimating total suspended solids (TSS) in inland and nearshore coastal waters | - |
dc.type | Article | - |
dc.citation.title | Remote Sensing of Environment | - |
dc.contributor.alternativeName | 한태현 | - |
dc.identifier.bibliographicCitation | Remote Sensing of Environment | - |
dc.identifier.doi | 10.1016/j.rse.2020.111768 | - |
dc.identifier.scopusid | 2-s2.0-85085340652 | - |
dc.identifier.wosid | 000537691800001 | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.subject.keywordPlus | INHERENT OPTICAL-PROPERTIES | - |
dc.subject.keywordPlus | REMOTE-SENSING REFLECTANCE | - |
dc.subject.keywordPlus | QUASI-ANALYTICAL ALGORITHM | - |
dc.subject.keywordPlus | OCEAN COLOR | - |
dc.subject.keywordPlus | PARTICULATE MATTER | - |
dc.subject.keywordPlus | MINERAL PARTICLES | - |
dc.subject.keywordPlus | SATELLITE DATA | - |
dc.subject.keywordPlus | SEDIMENT CONCENTRATION | - |
dc.subject.keywordPlus | ATMOSPHERIC CORRECTION | - |
dc.subject.keywordPlus | FIELD-MEASUREMENTS | - |
dc.subject.keywordAuthor | Total suspended solids | - |
dc.subject.keywordAuthor | Remote sensing reflectance | - |
dc.subject.keywordAuthor | Backscattering | - |
dc.subject.keywordAuthor | Coastal and inland waters | - |
dc.subject.keywordAuthor | Inversion models | - |
dc.subject.keywordAuthor | Inherent optical properties | - |
dc.subject.keywordAuthor | Aquatic remote sensing | - |
dc.subject.keywordAuthor | Sentinel-3 | - |
dc.relation.journalWebOfScienceCategory | Environmental Sciences | - |
dc.relation.journalWebOfScienceCategory | Remote Sensing | - |
dc.relation.journalWebOfScienceCategory | Imaging Science & Photographic Technology | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |