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Supervised Classifications of Optical Water Types in Spanish Inland Waters

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Supervised Classifications of Optical Water Types in Spanish Inland Waters

dc.contributor.author	Pereira Sandoval, Marcela Andrea
dc.contributor.author	Ruescas Orient, Ana Belén
dc.contributor.author	García Jiménez, Jorge
dc.contributor.author	Blix, Katalin
dc.contributor.author	Delegido Gómez, Jesús
dc.contributor.author	Moreno, José
dc.date.accessioned	2023-01-30T09:26:51Z
dc.date.available	2023-01-30T09:26:51Z
dc.date.issued	2022
dc.identifier.citation	Pereira Sandoval, Marcela Ruescas Orient, Ana Belén García Jiménez, Jorge Blix, Katalin Delegido Gómez, Jesús Moreno, José 2022 Supervised Classifications of Optical Water Types in Spanish Inland Waters Remote Sensing 14 1 20
dc.identifier.uri	https://hdl.handle.net/10550/85173
dc.description.abstract	Remote sensing of lake water quality assumes there is no universal method or algorithm that can be applied in a general way on all inland waters, which usually have different in-water components affecting their optical properties. Depending on the place and time of year, the lake dynamics, and the particular components of the water, non-tailor-designed algorithms can lead to large errors or lags in the quantification of the water quality parameters, such as the suspended mineral sediments, dissolved organic matter, and chlorophyll-a concentration. Selecting the most suitable algorithm for each type of water is not a simple matter. One way to make selecting the most suitable water quality algorithm easier on each occasion is by knowing ahead of time the type of water being handled. This approach is used, for instance, in the Lake Water Quality production chain of the Copernicus Global Land Service. The objective of this work is to determine which supervised classification approach might give the most accurate results. We use a dataset of manually labeled pixels on lakes and reservoirs in Eastern Spain. High-resolution images from the Multispectral Instrument sensor on board the ESA Sentinel-2 satellite, atmospherically corrected with the Case 2 Regional Coast Colour algorithm, are used as the basis for extracting the pixels for the dataset. Three families of different supervised classifiers have been implemented and compared: the K-nearest neighbor, decision trees, and support vector machine. Based on the results, the most appropriate for our study area is the random forest classifier, which was selected and applied on a series of images to derive the temporal series of the optical water types per lake. An evaluation of the results is presented, and an analysis is made using expert knowledge.
dc.language.iso	eng
dc.relation.ispartof	Remote Sensing, 2022, vol. 14, p. 1-20
dc.subject	Aigua
dc.title	Supervised Classifications of Optical Water Types in Spanish Inland Waters
dc.type	journal article	es_ES
dc.date.updated	2023-01-30T09:26:51Z
dc.identifier.doi	10.3390/rs14215568
dc.identifier.idgrec	156123
dc.rights.accessRights	open access	es_ES