{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T15:21:28Z","timestamp":1768836088700,"version":"3.49.0"},"reference-count":80,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2024,8,8]],"date-time":"2024-08-08T00:00:00Z","timestamp":1723075200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002322","name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior (CAPES)","doi-asserted-by":"publisher","award":["001"],"award-info":[{"award-number":["001"]}],"id":[{"id":"10.13039\/501100002322","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Brazil, a leading rice producer globally, faces challenges in systematically mapping its diverse rice fields due to varying cropping systems, climates, and planting calendars. Existing rice mapping methods often rely on complex techniques like deep learning or microwave imagery, posing limitations for large-scale mapping. This study proposes a novel approach utilizing Sentinel-2 spectral\u2013temporal metrics (STMs) in conjunction with a random forest classifier for rice paddy mapping. By extracting diverse STMs and training both regional and global classifiers, we validated the method across independent areas. While regional models tended to overestimate rice areas, the global model effectively reduced discrepancies between our data and the reference maps, achieving an overall classifier accuracy exceeding 80%. Despite the need for further refinement to address confusion with other crops, STM exhibits promise for national-scale rice paddy mapping in Brazil.<\/jats:p>","DOI":"10.3390\/rs16162900","type":"journal-article","created":{"date-parts":[[2024,8,8]],"date-time":"2024-08-08T12:13:14Z","timestamp":1723119194000},"page":"2900","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Mapping Irrigated Rice in Brazil Using Sentinel-2 Spectral\u2013Temporal Metrics and Random Forest Algorithm"],"prefix":"10.3390","volume":"16","author":[{"given":"Alexandre S.","family":"Fernandes Filho","sequence":"first","affiliation":[{"name":"Earth Observation and Geoinformatics Division, National Institute for Space Research (INPE), Avenida dos Astronautas, 1758, Jardim da Granja, Sao Jose dos Campos 12227-010, SP, Brazil"}]},{"given":"Leila M. G.","family":"Fonseca","sequence":"additional","affiliation":[{"name":"Earth Observation and Geoinformatics Division, National Institute for Space Research (INPE), Avenida dos Astronautas, 1758, Jardim da Granja, Sao Jose dos Campos 12227-010, SP, Brazil"},{"name":"Brazilian Space Agency (AEB), SPO, ASA Sul, Bras\u00edlia 70610-200, DF, Brazil"}]},{"given":"Hugo do N.","family":"Bendini","sequence":"additional","affiliation":[{"name":"Earth Observation and Geoinformatics Division, National Institute for Space Research (INPE), Avenida dos Astronautas, 1758, Jardim da Granja, Sao Jose dos Campos 12227-010, SP, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,8]]},"reference":[{"key":"ref_1","unstructured":"Ag\u00eancia Nacional de \u00c1guas e Saneamento B\u00e1sico (ANA) (2020). 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