{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T03:15:08Z","timestamp":1768706108196,"version":"3.49.0"},"reference-count":51,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,6,21]],"date-time":"2024-06-21T00:00:00Z","timestamp":1718928000000},"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":["U2243217"],"award-info":[{"award-number":["U2243217"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52220105007"],"award-info":[{"award-number":["52220105007"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52309066"],"award-info":[{"award-number":["52309066"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate identification of the spatio-temporal planting structure and analysis of its driving factors in an irrigation district are the important bases for scientific and reasonable utilization of irrigation water resources. In pursuit of this goal, the training sample migration method combined with the random forest algorithm were used to classify land use and planting structure over 2001\u20132022 in the lower Yellow River Basin. Moreover, an econometric regression model was applied to quantify the driving factors of the change in the crop-planted area. The results illustrated that the identification method has relatively high accuracy in identifying historical periods of land use and planting structures, with the average kappa coefficient equating to 0.953. From 2001 to 2022, the area of cultivated land was the largest, with the proportion of the total area increasing from 45.72% to 58.12%. The planted area of winter wheat\u2013summer maize rotation increased from 74.84% to 88.11% of the cultivated land. While the planted area of cotton declined by 96.36%, about 50% of cotton planting was converted to the winter wheat\u2013summer maize rotation planting. The government policies about grain purchase and storage were the dominant factors for the change in the crop-planted area. This resulted in an increase of 63.32 \u00d7 103 ha and 63.98 \u00d7 103 ha in the planted area of winter wheat and summer maize, respectively. The findings are of great significance to the allocation of water resources in irrigation districts of the lower Yellow River Basin.<\/jats:p>","DOI":"10.3390\/rs16132274","type":"journal-article","created":{"date-parts":[[2024,6,21]],"date-time":"2024-06-21T08:50:08Z","timestamp":1718959808000},"page":"2274","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Identification and Analysis of Long-Term Land Use and Planting Structure Dynamics in the Lower Yellow River Basin"],"prefix":"10.3390","volume":"16","author":[{"given":"Shengzhe","family":"Hong","sequence":"first","affiliation":[{"name":"National Key Laboratory for Efficient Utilization of Agricultural Water Resources, China Agricultural University, Beijing 100083, China"},{"name":"Chinese-Israeli International Center for Research and Training in Agriculture, College of Water Resources & Civil Engineering, China Agricultural University, Beijing 100083, China"},{"name":"Center for Agricultural Water Research in China, China Agricultural University, Beijing 100083, China"}]},{"given":"Yu","family":"Lou","sequence":"additional","affiliation":[{"name":"Science and Technology Promotion Centre Ministry of Water Resources. P.R.C., Beijing 100038, China"}]},{"given":"Xinguo","family":"Chen","sequence":"additional","affiliation":[{"name":"National Key Laboratory for Efficient Utilization of Agricultural Water Resources, China Agricultural University, Beijing 100083, China"},{"name":"Chinese-Israeli International Center for Research and Training in Agriculture, College of Water Resources & Civil Engineering, China Agricultural University, Beijing 100083, China"},{"name":"Center for Agricultural Water Research in China, China Agricultural University, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6306-785X","authenticated-orcid":false,"given":"Quanzhong","family":"Huang","sequence":"additional","affiliation":[{"name":"National Key Laboratory for Efficient Utilization of Agricultural Water Resources, China Agricultural University, Beijing 100083, China"},{"name":"Chinese-Israeli International Center for Research and Training in Agriculture, College of Water Resources & Civil Engineering, China Agricultural University, Beijing 100083, China"},{"name":"Center for Agricultural Water Research in China, China Agricultural University, Beijing 100083, China"}]},{"given":"Qianru","family":"Yang","sequence":"additional","affiliation":[{"name":"National Key Laboratory for Efficient Utilization of Agricultural Water Resources, China Agricultural University, Beijing 100083, China"},{"name":"Chinese-Israeli International Center for Research and Training in Agriculture, College of Water Resources & Civil Engineering, China Agricultural University, Beijing 100083, China"},{"name":"Center for Agricultural Water Research in China, China Agricultural University, Beijing 100083, China"}]},{"given":"Xinxin","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Key Laboratory for Efficient Utilization of Agricultural Water Resources, China Agricultural University, Beijing 100083, China"},{"name":"Chinese-Israeli International Center for Research and Training in Agriculture, College of Water Resources & Civil Engineering, China Agricultural University, Beijing 100083, China"},{"name":"Center for Agricultural Water Research in China, China Agricultural University, Beijing 100083, China"}]},{"given":"Haozhi","family":"Li","sequence":"additional","affiliation":[{"name":"National Key Laboratory for Efficient Utilization of Agricultural Water Resources, China Agricultural University, Beijing 100083, China"},{"name":"Chinese-Israeli International Center for Research and Training in Agriculture, College of Water Resources & Civil Engineering, China Agricultural University, Beijing 100083, China"},{"name":"Center for Agricultural Water Research in China, China Agricultural University, Beijing 100083, China"}]},{"given":"Guanhua","family":"Huang","sequence":"additional","affiliation":[{"name":"National Key Laboratory for Efficient Utilization of Agricultural Water Resources, China Agricultural University, Beijing 100083, China"},{"name":"Chinese-Israeli International Center for Research and Training in Agriculture, College of Water Resources & Civil Engineering, China Agricultural University, Beijing 100083, China"},{"name":"Center for Agricultural Water Research in China, China Agricultural University, Beijing 100083, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"153559","DOI":"10.1016\/j.scitotenv.2022.153559","article-title":"Machine learning in modelling land-use and land cover-change (LULCC), Current status, challenges and prospects","volume":"822","author":"Wang","year":"2022","journal-title":"Sci. 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