{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T12:52:03Z","timestamp":1765889523544,"version":"build-2065373602"},"reference-count":33,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Secretar\u00eda de Investigaci\u00f3n y Posgrado of the Instituto Polit\u00e9cnico Nacional","award":["20221330","20230026"],"award-info":[{"award-number":["20221330","20230026"]}]},{"DOI":"10.13039\/501100003141","name":"Secretar\u00eda de Ciencia, Humanidades, Tecnolog\u00eda e Innovaci\u00f3n (SECIHTI) and the Beca de Est\u00edmulo Institucional de Formaci\u00f3n de Investigadores","doi-asserted-by":"publisher","award":["1043025"],"award-info":[{"award-number":["1043025"]}],"id":[{"id":"10.13039\/501100003141","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Secretar\u00eda de Investigaci\u00f3n y Posgrado"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>Over the past decade, recurring influxes of pelagic Sargassum have posed significant environmental and economic challenges in the Caribbean Sea. Effective monitoring is crucial for understanding bloom dynamics and mitigating their impacts. This study presents a comprehensive machine learning (ML) and deep learning (DL) framework for detecting Sargassum and estimating its fractional cover using imagery from key satellite sensors: the Operational Land Imager (OLI) on Landsat-8 and the Multispectral Instrument (MSI) on Sentinel-2. A spectral library was constructed from five core spectral bands (Blue, Green, Red, Near-Infrared, and Short-Wave Infrared). It was used to train an ensemble of five diverse classifiers: Random Forest (RF), K-Nearest Neighbors (KNN), XGBoost (XGB), a Multi-Layer Perceptron (MLP), and a 1D Convolutional Neural Network (1D-CNN). All models achieved high classification performance on a held-out test set, with weighted F1-scores exceeding 0.976. The probabilistic outputs from these classifiers were then leveraged as a direct proxy for the sub-pixel fractional cover of Sargassum. Critically, an inter-algorithm agreement analysis revealed that detections on real-world imagery are typically either of very high (unanimous) or very low (contentious) confidence, highlighting the diagnostic power of the ensemble approach. The resulting framework provides a robust and quantitative pathway for generating confidence-aware estimates of Sargassum distribution. This work supports efforts to manage these harmful algal blooms by providing vital information on detection certainty, while underscoring the critical need to empirically validate fractional cover proxies against in situ or UAV measurements.<\/jats:p>","DOI":"10.3390\/data10110177","type":"journal-article","created":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T13:47:58Z","timestamp":1762177678000},"page":"177","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Machine and Deep Learning Framework for Sargassum Detection and Fractional Cover Estimation Using Multi-Sensor Satellite Imagery"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-5332-8683","authenticated-orcid":false,"given":"Jos\u00e9 Manuel","family":"Echevarr\u00eda-Rubio","sequence":"first","affiliation":[{"name":"Departamento de Oceanolog\u00eda, Centro Interdisciplinario de Ciencias Marinas, Instituto Polit\u00e9cnico Nacional, Av. Instituto Polit\u00e9cnico Nacional s\/n, Colonia Playa Palo de Santa Rita, La Paz 23096, Baja California Sur, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8415-6114","authenticated-orcid":false,"given":"Guillermo","family":"Mart\u00ednez-Flores","sequence":"additional","affiliation":[{"name":"Departamento de Oceanolog\u00eda, Centro Interdisciplinario de Ciencias Marinas, Instituto Polit\u00e9cnico Nacional, Av. Instituto Polit\u00e9cnico Nacional s\/n, Colonia Playa Palo de Santa Rita, La Paz 23096, Baja California Sur, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5323-5060","authenticated-orcid":false,"given":"Rub\u00e9n Antelmo","family":"Morales-P\u00e9rez","sequence":"additional","affiliation":[{"name":"Instituto Mexicano de Tecnolog\u00eda del Agua, Paseo Cuauhn\u00e1huac 8532, Colonia Progreso, Jiutepec 62550, Morelos, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1126\/science.aaw7912","article-title":"The Great Atlantic Sargassum Belt","volume":"365","author":"Wang","year":"2019","journal-title":"Science"},{"key":"ref_2","first-page":"100767","article-title":"Spatio-Temporal Variability of Pelagic Sargassum Landings on the Northern Mexican Caribbean","volume":"27","author":"Hu","year":"2022","journal-title":"Remote Sens. 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