{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T07:51:39Z","timestamp":1774425099474,"version":"3.50.1"},"reference-count":51,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2021,12,20]],"date-time":"2021-12-20T00:00:00Z","timestamp":1639958400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41901349"],"award-info":[{"award-number":["41901349"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and Technology Program of Guangdong Province, China","award":["2021B1111610001"],"award-info":[{"award-number":["2021B1111610001"]}]},{"name":"Startup Foundation for Talented Scholars in South China Normal University","award":["8S0472"],"award-info":[{"award-number":["8S0472"]}]},{"name":"Foundation for Young Innovation Talents in Higher Education of Guangdong, China (Natural Science)","award":["2018KQNCX054"],"award-info":[{"award-number":["2018KQNCX054"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The sustainable development goals of the United Nations, as well as the era of pandemics have introduced serious challenges for agricultural production and management. Precise management of agricultural practices based on satellite-borne remote sensing has been considered an effective means for monitoring cropping patterns and crop-farming patterns. Therefore, we proposed a simple and generic approach to identify multi-year cotton-cropping patterns based on time series of Landsat and Sentinel-2 images, with few ground samples that covered many years, a simple classification algorithm, and had a high classification accuracy. In this approach, we extended the size of training samples using active learning, and we employed a random forest algorithm to extract multi-year cotton planting patterns based on dense time series of Landsat and Sentinel-2 data from 2014 to 2018. We created annual crop cultivation maps based on training samples with an accuracy greater than 95.69%. The accuracy of multi-year cotton cropping patterns was 96.93%. The proposed approach was effective and robust in identifying multi-year cropping patterns, and it could be applied in other regions.<\/jats:p>","DOI":"10.3390\/rs13245183","type":"journal-article","created":{"date-parts":[[2021,12,21]],"date-time":"2021-12-21T04:23:47Z","timestamp":1640060627000},"page":"5183","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Toward a Simple and Generic Approach for Identifying Multi-Year Cotton Cropping Patterns Using Landsat and Sentinel-2 Time Series"],"prefix":"10.3390","volume":"13","author":[{"given":"Qiqi","family":"Li","sequence":"first","affiliation":[{"name":"School of Geography, South China Normal University, Guangzhou 510631, China"},{"name":"Key Laboratory of Natural Resources Monitoring in Tropical and Subtropical Area of South China, Ministry of Natural Resources, Guangzhou 510631, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8560-5209","authenticated-orcid":false,"given":"Guilin","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Geography, South China Normal University, Guangzhou 510631, China"},{"name":"Key Laboratory of Natural Resources Monitoring in Tropical and Subtropical Area of South China, Ministry of Natural Resources, Guangzhou 510631, China"}]},{"given":"Weijia","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Geography, South China Normal University, Guangzhou 510631, China"},{"name":"Key Laboratory of Natural Resources Monitoring in Tropical and Subtropical Area of South China, Ministry of Natural Resources, Guangzhou 510631, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,20]]},"reference":[{"key":"ref_1","unstructured":"United Nations (2017). 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