{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T18:28:22Z","timestamp":1773944902456,"version":"3.50.1"},"reference-count":51,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,2,2]],"date-time":"2022-02-02T00:00:00Z","timestamp":1643760000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["ICER\/EAR1829999"],"award-info":[{"award-number":["ICER\/EAR1829999"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In India, the second-largest sugarcane producing country in the world, accurate mapping of sugarcane land is a key to designing targeted agricultural policies. Such a map is not available, however, as it is challenging to reliably identify sugarcane areas using remote sensing due to sugarcane\u2019s phenological characteristics, coupled with a range of cultivation periods for different varieties. To produce a modern sugarcane map for the Bhima Basin in central India, we utilized crowdsourced data and applied supervised machine learning (neural network) and unsupervised classification methods individually and in combination. We highlight four points. First, smartphone crowdsourced data can be used as an alternative ground truth for sugarcane mapping but requires careful correction of potential errors. Second, although the supervised machine learning method performs best for sugarcane mapping, the combined use of both classification methods improves sugarcane mapping precision at the cost of worsening sugarcane recall and missing some actual sugarcane area. Third, machine learning image classification using high-resolution satellite imagery showed significant potential for sugarcane mapping. Fourth, our best estimate of the sugarcane area in the Bhima Basin is twice that shown in government statistics. This study provides useful insights into sugarcane mapping that can improve the approaches taken in other regions.<\/jats:p>","DOI":"10.3390\/rs14030703","type":"journal-article","created":{"date-parts":[[2022,2,6]],"date-time":"2022-02-06T20:38:40Z","timestamp":1644179920000},"page":"703","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Mapping Sugarcane in Central India with Smartphone Crowdsourcing"],"prefix":"10.3390","volume":"14","author":[{"given":"Ju Young","family":"Lee","sequence":"first","affiliation":[{"name":"Department of Earth System Science, Stanford University, Stanford, CA 94305, USA"},{"name":"Center on Food Security and the Environment, Stanford University, Stanford, CA 94305, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4618-5675","authenticated-orcid":false,"given":"Sherrie","family":"Wang","sequence":"additional","affiliation":[{"name":"Center on Food Security and the Environment, Stanford University, Stanford, CA 94305, USA"},{"name":"Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA 94305, USA"},{"name":"Goldman School of Public Policy, University of California, Berkeley, CA 94720, USA"}]},{"given":"Anjuli Jain","family":"Figueroa","sequence":"additional","affiliation":[{"name":"Department of Earth System Science, Stanford University, Stanford, CA 94305, USA"}]},{"given":"Rob","family":"Strey","sequence":"additional","affiliation":[{"name":"Progressive Environmental & Agricultural Technologies, 10435 Berlin, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5969-3476","authenticated-orcid":false,"given":"David B.","family":"Lobell","sequence":"additional","affiliation":[{"name":"Department of Earth System Science, Stanford University, Stanford, CA 94305, USA"},{"name":"Center on Food Security and the Environment, Stanford University, Stanford, CA 94305, USA"}]},{"given":"Rosamond L.","family":"Naylor","sequence":"additional","affiliation":[{"name":"Department of Earth System Science, Stanford University, Stanford, CA 94305, USA"},{"name":"Center on Food Security and the Environment, Stanford University, Stanford, CA 94305, USA"}]},{"given":"Steven M.","family":"Gorelick","sequence":"additional","affiliation":[{"name":"Department of Earth System Science, Stanford University, Stanford, CA 94305, USA"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,2]]},"reference":[{"key":"ref_1","unstructured":"Food and Agriculture Organization\u2019s Statistical Database (FAOSTAT) (2021, November 01). 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