{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T09:14:18Z","timestamp":1775466858520,"version":"3.50.1"},"reference-count":44,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,4,24]],"date-time":"2024-04-24T00:00:00Z","timestamp":1713916800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2022YFB3903504"],"award-info":[{"award-number":["2022YFB3903504"]}]},{"name":"National Key Research and Development Program of China","award":["42371281"],"award-info":[{"award-number":["42371281"]}]},{"name":"National Natural Science Foundation of China","award":["2022YFB3903504"],"award-info":[{"award-number":["2022YFB3903504"]}]},{"name":"National Natural Science Foundation of China","award":["42371281"],"award-info":[{"award-number":["42371281"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Crop mapping using satellite imagery is crucial for agriculture applications. However, a fundamental challenge that hinders crop mapping progress is the scarcity of samples. The latest foundation model, Segment Anything Model (SAM), provides an opportunity to address this issue, yet few studies have been conducted in this area. This study investigated the parcel segmentation performance of SAM on commonly used medium-resolution satellite imagery (i.e., Sentinel-2 and Landsat-8) and proposed a novel automated sample generation framework based on SAM. The framework comprises three steps. First, an image optimization automatically selects high-quality images as the inputs for SAM. Then, potential samples are generated based on the masks produced by SAM. Finally, the potential samples are subsequently subjected to a sample cleaning procedure to acquire the most reliable samples. Experiments were conducted in Henan Province, China, and southern Ontario, Canada, using six proven effective classifiers. The effectiveness of our method is demonstrated through the combination of field-survey-collected samples and differently proportioned generated samples. Our results indicated that directly using SAM for parcel segmentation remains challenging, unless the parcels are large, regular in shape, and have distinct color differences from surroundings. Additionally, the proposed approach significantly improved the performance of classifiers and alleviated the sample scarcity problem. Compared to classifiers trained only by field-survey-collected samples, our method resulted in an average improvement of 16% and 78.5% in Henan and Ontario, respectively. The random forest achieved relatively good performance, with weighted-average F1 of 0.97 and 0.996 obtained using Sentinel-2 imagery in the two study areas, respectively. Our study contributes insights into solutions for sample scarcity in crop mapping and highlights the promising application of foundation models like SAM.<\/jats:p>","DOI":"10.3390\/rs16091505","type":"journal-article","created":{"date-parts":[[2024,4,25]],"date-time":"2024-04-25T05:26:13Z","timestamp":1714022773000},"page":"1505","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Enhancing Crop Mapping through Automated Sample Generation Based on Segment Anything Model with Medium-Resolution Satellite Imagery"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-0601-7014","authenticated-orcid":false,"given":"Jialin","family":"Sun","sequence":"first","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100193, China"}]},{"given":"Shuai","family":"Yan","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100193, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1893-6301","authenticated-orcid":false,"given":"Thomas","family":"Alexandridis","sequence":"additional","affiliation":[{"name":"School of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8068-9415","authenticated-orcid":false,"given":"Xiaochuang","family":"Yao","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100193, China"},{"name":"Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100193, China"}]},{"given":"Han","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100193, China"}]},{"given":"Bingbo","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100193, China"},{"name":"Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100193, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0341-1983","authenticated-orcid":false,"given":"Jianxi","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100193, China"},{"name":"Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100193, China"}]},{"given":"Jianyu","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100193, China"},{"name":"Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100193, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-1315-6559","authenticated-orcid":false,"given":"Ying","family":"Li","sequence":"additional","affiliation":[{"name":"Henan Institute of Meteorological Sciences, Zhengzhou 450003, China"},{"name":"CMA\u00b7Henan Agrometeorological Support and Applied Technique Key Laboratory, Zhengzhou 450003, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"112831","DOI":"10.1016\/j.rse.2021.112795","article-title":"Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany","volume":"269","author":"Schwieder","year":"2022","journal-title":"Remote Sens. 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