{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T12:12:28Z","timestamp":1774008748146,"version":"3.50.1"},"reference-count":71,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,2,26]],"date-time":"2023-02-26T00:00:00Z","timestamp":1677369600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"H2020 PrimeWater","award":["870497"],"award-info":[{"award-number":["870497"]}]},{"name":"H2020 PrimeWater","award":["101004186"],"award-info":[{"award-number":["101004186"]}]},{"name":"H2020 PrimeWater","award":["2022\/01107-2"],"award-info":[{"award-number":["2022\/01107-2"]}]},{"name":"H2020 PrimeWater","award":["2020\/14613-8"],"award-info":[{"award-number":["2020\/14613-8"]}]},{"name":"H2020 Water-ForCE","award":["870497"],"award-info":[{"award-number":["870497"]}]},{"name":"H2020 Water-ForCE","award":["101004186"],"award-info":[{"award-number":["101004186"]}]},{"name":"H2020 Water-ForCE","award":["2022\/01107-2"],"award-info":[{"award-number":["2022\/01107-2"]}]},{"name":"H2020 Water-ForCE","award":["2020\/14613-8"],"award-info":[{"award-number":["2020\/14613-8"]}]},{"name":"S\u00e3o Paulo Research Foundation (FAPESP)","award":["870497"],"award-info":[{"award-number":["870497"]}]},{"name":"S\u00e3o Paulo Research Foundation (FAPESP)","award":["101004186"],"award-info":[{"award-number":["101004186"]}]},{"name":"S\u00e3o Paulo Research Foundation (FAPESP)","award":["2022\/01107-2"],"award-info":[{"award-number":["2022\/01107-2"]}]},{"name":"S\u00e3o Paulo Research Foundation (FAPESP)","award":["2020\/14613-8"],"award-info":[{"award-number":["2020\/14613-8"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The aim of this work is to test the state-of-the-art of water constituent retrieval algorithms for phycocyanin (PC) and chlorophyll-a (chl-a) concentrations in Brazilian reservoirs from hyperspectral PRISMA images and concurrent in situ data. One near-coincident Sentinel-3 OLCI dataset has also been considered for PC mapping as its high revisit time is a relevant element for mapping cyanobacterial blooms. The testing was first performed on remote sensing reflectance (Rrs), as derived by applying two atmospheric correction methods (6SV, ACOLITE) to Level 1 data and as provided in the corresponding Level 2 products (PRISMA L2C and OLCI L2-WFR). Since PRISMA images were affected by sun glint, the testing of three de-glint models was also performed. The applicability of Semi-Analytical (SA) and Mixture Density Network (MDN) algorithms in enabling PC and chl-a concentration retrieval was then tested over three PRISMA scenes; in the case of PC concentration estimation, a Random Forest (RF) algorithm was further applied. Regarding OLCI, the SA algorithm was tested for PC estimation; notably, only SA was calibrated with site-specific data from the reservoir. The algorithms were applied to the Rrs spectra provided by PRISMA L2C products\u2014and those derived with ACOLITE, in the case of OLCI\u2014as these data showed better agreement with in situ measurements. The SA model provided low median absolute error (MdAE) for PRISMA-derived (MdAE = 3.06 mg.m\u22123) and OLCI-derived (MdAE = 3.93 mg.m\u22123) PC concentrations, while it overestimated PRISMA-derived chl-a (MdAE = 42.11 mg.m\u22123). The RF model for PC applied to PRISMA performed slightly worse than SA (MdAE = 5.21 mg.m\u22123). The MDN showed a rather different performance, with higher errors for PC (MdAE = 40.94 mg.m\u22123) and lower error for chl-a (MdAE = 23.21 mg.m\u22123). The results overall suggest that the model calibrated with site-specific measurements performed better and indicates that SA could be applied to PRISMA and OLCI for remote sensing of PC in Brazilian reservoirs.<\/jats:p>","DOI":"10.3390\/rs15051299","type":"journal-article","created":{"date-parts":[[2023,2,27]],"date-time":"2023-02-27T02:04:11Z","timestamp":1677463451000},"page":"1299","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Assessment of Estimated Phycocyanin and Chlorophyll-a Concentration from PRISMA and OLCI in Brazilian Inland Waters: A Comparison between Semi-Analytical and Machine Learning Algorithms"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6492-0330","authenticated-orcid":false,"given":"Thainara Munhoz Alexandre de","family":"Lima","sequence":"first","affiliation":[{"name":"Earth Observation and Geoinformatics Division (DIOTG), National Institute for Space Research (INPE), S\u00e3o Jos\u00e9 dos Campos 12227-010, SP, Brazil"},{"name":"Instrumentation Laboratory for Aquatic Systems (LabISA), Earth Sciences General Coordination of the National Institute for Space Research (INPE), S\u00e3o Jos\u00e9 dos Campos 12227-010, SP, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3937-4988","authenticated-orcid":false,"given":"Claudia","family":"Giardino","sequence":"additional","affiliation":[{"name":"CNR-IREA, National Research Council of Italy, Institute for Electromagnetic Sensing of the Environment, 20133 Milan, Italy"},{"name":"NBFC, National Biodiversity Future Center, 90133 Palermo, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7185-8464","authenticated-orcid":false,"given":"Mariano","family":"Bresciani","sequence":"additional","affiliation":[{"name":"CNR-IREA, National Research Council of Italy, Institute for Electromagnetic Sensing of the Environment, 20133 Milan, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3221-9774","authenticated-orcid":false,"given":"Claudio Clemente Faria","family":"Barbosa","sequence":"additional","affiliation":[{"name":"Earth Observation and Geoinformatics Division (DIOTG), National Institute for Space Research (INPE), S\u00e3o Jos\u00e9 dos Campos 12227-010, SP, Brazil"},{"name":"Instrumentation Laboratory for Aquatic Systems (LabISA), Earth Sciences General Coordination of the National Institute for Space Research (INPE), S\u00e3o Jos\u00e9 dos Campos 12227-010, SP, Brazil"}]},{"given":"Alice","family":"Fabbretto","sequence":"additional","affiliation":[{"name":"CNR-IREA, National Research Council of Italy, Institute for Electromagnetic Sensing of the Environment, 20133 Milan, Italy"},{"name":"Tartu Observatory, University of Tartu, 50411 Tartu, Estonia"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-4152-3409","authenticated-orcid":false,"given":"Andrea","family":"Pellegrino","sequence":"additional","affiliation":[{"name":"CNR-IREA, National Research Council of Italy, Institute for Electromagnetic Sensing of the Environment, 20133 Milan, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8008-941X","authenticated-orcid":false,"given":"Felipe Nincao","family":"Begliomini","sequence":"additional","affiliation":[{"name":"Cambridge Centre for Carbon Credits, Department of Computer Science and Technology, University of Cambridge, Cambridge CB2 1TN, UK"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"106379","DOI":"10.1016\/j.ecolecon.2019.106373","article-title":"Measuring sustainable development goals performance: How to monitor policy action in the 2030 Agenda implementation?","volume":"164","author":"Miola","year":"2019","journal-title":"Ecol. 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