{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:29:58Z","timestamp":1760146198586,"version":"build-2065373602"},"reference-count":53,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2024,10,15]],"date-time":"2024-10-15T00:00:00Z","timestamp":1728950400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002322","name":"CAPES Foundation","doi-asserted-by":"publisher","award":["88887.847995\/2023-00","307438\/2023-6"],"award-info":[{"award-number":["88887.847995\/2023-00","307438\/2023-6"]}],"id":[{"id":"10.13039\/501100002322","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003593","name":"Brazilian National Council for Scientific and Technological Development CNPq","doi-asserted-by":"publisher","award":["88887.847995\/2023-00","307438\/2023-6"],"award-info":[{"award-number":["88887.847995\/2023-00","307438\/2023-6"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Numerous challenges are associated with the classification of satellite images of coffee plantations. The spectral similarity with other types of land use, variations in altitude, topography, production system (shaded and sun), and the change in spectral signature throughout the phenological cycle are examples that affect the process. This research investigates the influence of biennial Arabica coffee productivity on the accuracy of Landsat-8 image classification. The Google Earth Engine (GEE) platform and the Random Forest algorithm were used to process the annual and biennial mosaics of the M\u00e9dia Mogiana Region, S\u00e3o Paulo (Brazil), from 2017 to 2023. The parameters evaluated were the general hits of the seven classes of land use and coffee errors of commission and omission. It was found that the seasonality of the plant and its development phases were fundamental in the quality of coffee classification. The use of biennial mosaics, with Landsat-8 images, Brightness, Greenness, Wetness, SRTM data (elevation, aspect, slope), and LST data (Land Surface Temperature) also contributed to improving the process, generating a classification accuracy of 88.8% and reducing coffee omission errors to 22%.<\/jats:p>","DOI":"10.3390\/rs16203833","type":"journal-article","created":{"date-parts":[[2024,10,15]],"date-time":"2024-10-15T09:12:50Z","timestamp":1728983570000},"page":"3833","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Analysis of the Biennial Productivity of Arabica Coffee with Google Earth Engine in the Northeast Region of S\u00e3o Paulo, Brazil"],"prefix":"10.3390","volume":"16","author":[{"given":"Maria Cecilia","family":"Manoel","sequence":"first","affiliation":[{"name":"Department of Geography, University of S\u00e3o Paulo, Av. Lineu Prestes, 338, S\u00e3o Paulo 05508-000, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5367-8059","authenticated-orcid":false,"given":"Marcos Reis","family":"Rosa","sequence":"additional","affiliation":[{"name":"Department of Geography, University of S\u00e3o Paulo, Av. Lineu Prestes, 338, S\u00e3o Paulo 05508-000, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4038-4953","authenticated-orcid":false,"given":"Alfredo Pereira de","family":"Queiroz","sequence":"additional","affiliation":[{"name":"Department of Geography, University of S\u00e3o Paulo, Av. Lineu Prestes, 338, S\u00e3o Paulo 05508-000, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hunt, D.A., Tabor, K., Hewson, J.H., Wood, M.A., Reymondin, L., Koenig, K., and Follett, F. (2020). Review of remote sensing methods to map coffee production systems. 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