{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T00:20:19Z","timestamp":1792542019096,"version":"4.5.0"},"reference-count":54,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2022,8,9]],"date-time":"2022-08-09T00:00:00Z","timestamp":1660003200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003141","name":"CONACYT","doi-asserted-by":"publisher","award":["2019-000002-01NACF-07726"],"award-info":[{"award-number":["2019-000002-01NACF-07726"]}],"id":[{"id":"10.13039\/501100003141","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Coffee is one of the most important agricultural commodities of Mexico. Mapping coffee land cover is still a challenge because it is grown mainly on small areas in agroforestry systems (AFS), which are located in hard-to-access mountainous regions. The objective of this research was to map coffee AFS types in a mountainous region using the changing spectral response patterns over the dry season as well as supplementary data. We employed Sentinel-1, Sentinel-2 and ALOS-Palsar images, a digital elevation model, soil moisture layers, and 150 field plots. First, we defined three coffee AFS types based on their structural and spectral characteristics. Then, we performed a recursive feature elimination analysis to identify the most relevant predictor variables for each land use\/cover class in the region. Next, we constructed a predictor variable dataset for each AFS type and one for the remaining land use\/cover classes. Afterward, four maps were generated using a random forest (RF) classifier. Finally, we combined the four maps into a unique land-cover map through a maximum likelihood algorithm. Using a validation sample of 932 sites derived from Planet images (4.5 m pixel size), we estimated a 95% map overall accuracy. Two AFS types were classified as having low error; the third, with the highest tree density, had the lowest accuracy. The results obtained show that the infrared and near-infrared bands from the Sentinel-2 scenes are particularly useful for coffee AFS discrimination. However, supplementary data are required to improve the performance of the classifier. Our findings also highlight the importance of the multi-temporal and multi-dataset approach for identifying complex production systems in areas of high topographic heterogeneity.<\/jats:p>","DOI":"10.3390\/rs14163847","type":"journal-article","created":{"date-parts":[[2022,8,10]],"date-time":"2022-08-10T04:20:32Z","timestamp":1660105232000},"page":"3847","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Identifying Coffee Agroforestry System Types Using Multitemporal Sentinel-2 Data and Auxiliary Information"],"prefix":"10.3390","volume":"14","author":[{"given":"Agust\u00edn","family":"Escobar-L\u00f3pez","sequence":"first","affiliation":[{"name":"El Colegio de la Frontera Sur, Departamento de Observaci\u00f3n y Estudio de la Tierra, la Atm\u00f3sfera y el Oc\u00e9ano, Carretera Panamericana y Perif\u00e9rico Sur s\/n, San Crist\u00f3bal de las Casas 29290, Chiapas, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3024-5514","authenticated-orcid":false,"given":"Miguel \u00c1ngel","family":"Castillo-Santiago","sequence":"additional","affiliation":[{"name":"El Colegio de la Frontera Sur, Departamento de Observaci\u00f3n y Estudio de la Tierra, la Atm\u00f3sfera y el Oc\u00e9ano, Carretera Panamericana y Perif\u00e9rico Sur s\/n, San Crist\u00f3bal de las Casas 29290, Chiapas, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9559-7131","authenticated-orcid":false,"given":"Jos\u00e9 Luis","family":"Hern\u00e1ndez-Stefanoni","sequence":"additional","affiliation":[{"name":"Centro de Investigaci\u00f3n Cient\u00edfica de Yucat\u00e1n A. C. Unidad de Recursos Naturales, Calle 43 #130, M\u00e9rida 97205, Yucat\u00e1n, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6138-9879","authenticated-orcid":false,"given":"Jean Fran\u00e7ois","family":"Mas","sequence":"additional","affiliation":[{"name":"Centro de Investigaciones en Geograf\u00eda Ambiental, Universidad Nacional Aut\u00f3noma de M\u00e9xico, Morelia 58090, Michoac\u00e1n, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7903-8498","authenticated-orcid":false,"given":"Jorge Omar","family":"L\u00f3pez-Mart\u00ednez","sequence":"additional","affiliation":[{"name":"C\u00e1tedra CONACYT, Avenida Insurgentes Sur 1582, Colonia Cr\u00e9dito Constructor, Benito Ju\u00e1rez, Mexico City 03940, Distrito Federal, Mexico"},{"name":"El Colegio de la Frontera Sur, Departamento de Agricultura, Sociedad y Ambiente, Avenida Centenario Km 5.5, Chetumal 77014, Quintana Roo, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,9]]},"reference":[{"key":"ref_1","unstructured":"ITC (2021). 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