{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T17:54:11Z","timestamp":1772906051651,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,3,8]],"date-time":"2024-03-08T00:00:00Z","timestamp":1709856000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Common Application Support Platform for National Civil Space Infrastructure Land Observation Satellites","award":["2017-000052-73-01-001735"],"award-info":[{"award-number":["2017-000052-73-01-001735"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Timely and accurate acquisition of spatial distribution and changes in cropland is of significant importance for food security and ecological preservation. Most studies that monitor long-term changes in cropland tend to overlook the rationality in the process of cropland evolution, and there are conflicts between the interannual cropland data, so they cannot be used to analyze land use change. This study focuses on the rationality of annual identification results for cropland, considering the long-term evolution and short-term variations influenced by natural environmental changes and human activities. An approach for annual monitoring of cropland based on long time series and deep learning is also proposed. We acquired imagery related to cropland\u2019s vegetation lush period (VLP) and vegetation differential period (VDP) from Landsat images on the Google Earth Engine (GEE) platform and used the ResUNet-a structural model for training. Finally, a long-time-series cropland correction algorithm based on LandTrendr is introduced, and interannual cropland maps of Guangdong Province from 1991 to 2020 were generated. Evaluating the cropland monitoring results in Guangdong Province every five years, we found an overall accuracy of 0.91\u20130.93 and a kappa coefficient of 0.80\u20130.83. Our results demonstrate good consistency with agricultural statistical data. Over the past 30 years, the total cropland area in Guangdong Province has undergone three phases: a decrease, significant decrease, and stabilization. Significant regional variations have also been observed. Our approach can be applied to long-time-series interannual cropland monitoring in the southern regions of China, providing valuable data support for the further implementation of cropland protection.<\/jats:p>","DOI":"10.3390\/rs16060949","type":"journal-article","created":{"date-parts":[[2024,3,8]],"date-time":"2024-03-08T10:10:52Z","timestamp":1709892652000},"page":"949","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Interannual Monitoring of Cropland in South China from 1991 to 2020 Based on the Combination of Deep Learning and the LandTrendr Algorithm"],"prefix":"10.3390","volume":"16","author":[{"given":"Yue","family":"Qu","sequence":"first","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Land Use and Consolidation, South China Agricultural University, Guangzhou 510642, China"}]},{"given":"Boyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Land Use and Consolidation, South China Agricultural University, Guangzhou 510642, China"}]},{"given":"Han","family":"Xu","sequence":"additional","affiliation":[{"name":"Shenzhen Data Management Center of Planning and Natural Resource, Shenzhen 518034, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8971-4952","authenticated-orcid":false,"given":"Zhi","family":"Qiao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Indoor Air Environment Quality Control, School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1942-931X","authenticated-orcid":false,"given":"Luo","family":"Liu","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Land Use and Consolidation, South China Agricultural University, Guangzhou 510642, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"106924","DOI":"10.1016\/j.catena.2023.106924","article-title":"Mapping Abandoned Cropland Using Within-Year Sentinel-2 Time Series","volume":"223","author":"Liu","year":"2023","journal-title":"Catena"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1016\/j.landusepol.2018.01.013","article-title":"Peri-Urbanisation and Loss of Arable Land in Kumasi Metropolis in Three Decades: Evidence from Remote Sensing Image Analysis","volume":"72","author":"Abass","year":"2018","journal-title":"Land Use Policy"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Yuan, Z., Zhou, L., Sun, D., and Hu, F. 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