{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T19:06:39Z","timestamp":1774638399222,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,1,4]],"date-time":"2022-01-04T00:00:00Z","timestamp":1641254400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Project of Natural Science Foundation of Xinjiang Uygur Autonomous Region","award":["2021D01D06"],"award-info":[{"award-number":["2021D01D06"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41961059"],"award-info":[{"award-number":["41961059"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Xinjiang Uygur Autonomous Region Postgraduate Education Innovation Project","award":["XJ2021G042"],"award-info":[{"award-number":["XJ2021G042"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Rapid and accurate mapping of the spatial distribution of cotton fields is helpful to ensure safe production of cotton fields and the rationalization of land-resource planning. As cotton is an important economic pillar in Xinjiang, accurate and efficient mapping of cotton fields helps the implementation of rural revitalization strategy in Xinjiang region. In this paper, based on the Google Earth Engine cloud computing platform, we use a random forest machine-learning algorithm to classify Landsat 5 and 8 and Sentinel 2 satellite images to obtain the spatial distribution characteristics of cotton fields in 2011, 2015 and 2020 in the Ogan-Kucha River oasis, Xinjiang. Unlike previous studies, the mulching process was considered when using cotton field phenology information as a classification feature. The results show that both Landsat 5, Landsat 8 and Sentinel 2 satellites can successfully classify cotton field information when the mulching process is considered, but Sentinel 2 satellite classification results have the best user accuracy of 0.947. Sentinel 2 images can distinguish some cotton fields from roads well because they have higher spatial resolution than Landsat 8. After the cotton fields were mulched, there was a significant increase in spectral reflectance in the visible, red-edge and near-infrared bands, and a decrease in the short-wave infrared band. The increase in the area of oasis cotton fields and the extensive use of mulched drip-irrigation water saving facilities may lead to a decrease in the groundwater level. Overall, the use of mulch as a phenological feature for classification mapping is a good indicator in cotton-growing areas covered by mulch, and mulch drip irrigation may lead to a decrease in groundwater levels in oases in arid areas.<\/jats:p>","DOI":"10.3390\/rs14010225","type":"journal-article","created":{"date-parts":[[2022,1,9]],"date-time":"2022-01-09T23:06:15Z","timestamp":1641769575000},"page":"225","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Monitoring Oasis Cotton Fields Expansion in Arid Zones Using the Google Earth Engine: A Case Study in the Ogan-Kucha River Oasis, Xinjiang, China"],"prefix":"10.3390","volume":"14","author":[{"given":"Lijing","family":"Han","sequence":"first","affiliation":[{"name":"Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, College of Resources and Environment Science, Xinjiang University, Urumqi 830046, China"},{"name":"MOE Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China"}]},{"given":"Jianli","family":"Ding","sequence":"additional","affiliation":[{"name":"Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, College of Resources and Environment Science, Xinjiang University, Urumqi 830046, China"},{"name":"MOE Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China"},{"name":"MNR Technology Innovation Center for Central Asia Geo-Information Exploitation and Utilization, Ministry of Natural Resources, Urumqi 830046, China"}]},{"given":"Jinjie","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, College of Resources and Environment Science, Xinjiang University, Urumqi 830046, China"},{"name":"MOE Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China"}]},{"given":"Junyong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, College of Resources and Environment Science, Xinjiang University, Urumqi 830046, China"},{"name":"MOE Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China"}]},{"given":"Boqiang","family":"Xie","sequence":"additional","affiliation":[{"name":"Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, College of Resources and Environment Science, Xinjiang University, Urumqi 830046, China"},{"name":"MOE Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China"}]},{"given":"Jianping","family":"Hao","sequence":"additional","affiliation":[{"name":"Groundwater and Salt Monitoring Station, Ogan River Basin Management Office, Aksu 842000, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,4]]},"reference":[{"key":"ref_1","unstructured":"(2020). 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