{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T16:06:35Z","timestamp":1774627595185,"version":"3.50.1"},"reference-count":51,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2018,5,19]],"date-time":"2018-05-19T00:00:00Z","timestamp":1526688000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000781","name":"European Research Council","doi-asserted-by":"publisher","award":["647423"],"award-info":[{"award-number":["647423"]}],"id":[{"id":"10.13039\/501100000781","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The colored dissolved organic matter (CDOM) variable is the standard measure of humic substance in waters optics. CDOM is optically characterized by its spectral absorption coefficient, a C D O M at at reference wavelength (e.g., \u2248 440 nm). Retrieval of CDOM is traditionally done using bio-optical models. As an alternative, this paper presents a comparison of five machine learning methods applied to Sentinel-2 and Sentinel-3 simulated reflectance ( R r s ) data for the retrieval of CDOM: regularized linear regression (RLR), random forest regression (RFR), kernel ridge regression (KRR), Gaussian process regression (GPR) and support vector machines (SVR). Two different datasets of radiative transfer simulations are used for the development and training of the machine learning regression approaches. Statistics comparison with well-established polynomial regression algorithms shows optimistic results for all models and band combinations, highlighting the good performance of the methods, especially the GPR approach, when all bands are used as input. Application to an atmospheric corrected OLCI image using the reflectance derived form the alternative neural network (Case 2 Regional) is also shown. Python scripts and notebooks are provided to interested users.<\/jats:p>","DOI":"10.3390\/rs10050786","type":"journal-article","created":{"date-parts":[[2018,5,21]],"date-time":"2018-05-21T04:07:30Z","timestamp":1526875650000},"page":"786","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":80,"title":["Machine Learning Regression Approaches for Colored Dissolved Organic Matter (CDOM) Retrieval with S2-MSI and S3-OLCI Simulated Data"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0843-7288","authenticated-orcid":false,"given":"Ana Belen","family":"Ruescas","sequence":"first","affiliation":[{"name":"Image Processing Laboratory, Universitat de Val\u00e8ncia, 46980 Val\u00e8ncia, Spain"}]},{"given":"Martin","family":"Hieronymi","sequence":"additional","affiliation":[{"name":"Institute for Coastal Research, Helmholtz-Zentrum Geesthacht, 21502 Geestacht, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0569-393X","authenticated-orcid":false,"given":"Gonzalo","family":"Mateo-Garcia","sequence":"additional","affiliation":[{"name":"Image Processing Laboratory, Universitat de Val\u00e8ncia, 46980 Val\u00e8ncia, Spain"}]},{"given":"Sampsa","family":"Koponen","sequence":"additional","affiliation":[{"name":"Finnish Environment Institute (Suomen ymp\u00e4rist\u00f6keskus, SYKE), FI-00251 Helsinki, Finland"}]},{"given":"Kari","family":"Kallio","sequence":"additional","affiliation":[{"name":"Finnish Environment Institute (Suomen ymp\u00e4rist\u00f6keskus, SYKE), FI-00251 Helsinki, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1683-2138","authenticated-orcid":false,"given":"Gustau","family":"Camps-Valls","sequence":"additional","affiliation":[{"name":"Image Processing Laboratory, Universitat de Val\u00e8ncia, 46980 Val\u00e8ncia, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2018,5,19]]},"reference":[{"key":"ref_1","unstructured":"Preisendorfer, R.W. 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