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Philosophy and Social Sciences Research, Ministry of Education, China","award":["2662021JC013"],"award-info":[{"award-number":["2662021JC013"]}]},{"name":"Key Project of Philosophy and Social Sciences Research, Ministry of Education, China","award":["20JZD015"],"award-info":[{"award-number":["20JZD015"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Paddy rice cropping patterns (PRCPs) play important roles in both agroecosystem modeling and food security. Although paddy rice maps have been generated over several regions using satellite observations, few studies have focused on mapping diverse smallholder PRCPs, which include crop rotation and are dominant cropping structures in South China. Here, an approach called the feature selection and hierarchical classification (FSHC) method was proposed to effectively identify paddy rice and its rotation types. Considering the cloudy and rainy weather in South China, a harmonized Landsat and Sentinel-2 (HLS) surface reflectance product was employed to increase high-quality observations. The FSHC method consists of three processes: cropping intensity mapping, feature selection, and decision tree (DT) model development. The FSHC performance was carefully evaluated using crop field samples obtained in 2018 and 2019. Results suggested that the derived cropping intensity map based on the Savitzky\u2013Golay (S-G) filtered normalized difference vegetation index (NDVI) time series was reliable, with an overall accuracy greater than 93%. Additionally, the optimal spectral (i.e., normalized difference water index (NDWI) and land surface water index (LSWI)) and temporal (start-of-season (SOS) date) features for distinguishing different PRCPs were successfully identified, and these features are highly related to the critical growth stage of paddy rice. The developed DT model with three hierarchical levels based on optimal features performed satisfactorily, and the identification accuracy of each PRCP can be achieved approximately 85%. Furthermore, the FSHC method exhibited similar performances when mapping PRCPs in adjacent years. These results demonstrate that the proposed FSHC approach with HLS data can accurately extract diverse PRCPs over fragmented croplands; thus, this approach represents a promising opportunity for generating refined crop type maps.<\/jats:p>","DOI":"10.3390\/rs15041034","type":"journal-article","created":{"date-parts":[[2023,2,14]],"date-time":"2023-02-14T04:38:29Z","timestamp":1676349509000},"page":"1034","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Mapping Diverse Paddy Rice Cropping Patterns in South China Using Harmonized Landsat and Sentinel-2 Data"],"prefix":"10.3390","volume":"15","author":[{"given":"Jie","family":"Hu","sequence":"first","affiliation":[{"name":"Macro Agriculture Research Institute, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China"}]},{"given":"Yunping","family":"Chen","sequence":"additional","affiliation":[{"name":"Macro Agriculture Research Institute, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China"}]},{"given":"Zhiwen","family":"Cai","sequence":"additional","affiliation":[{"name":"College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China"}]},{"given":"Haodong","family":"Wei","sequence":"additional","affiliation":[{"name":"Macro Agriculture Research Institute, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China"}]},{"given":"Xinyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China"}]},{"given":"Wei","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China"}]},{"given":"Cong","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province, College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7930-8814","authenticated-orcid":false,"given":"Liangzhi","family":"You","sequence":"additional","affiliation":[{"name":"Macro Agriculture Research Institute, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China"},{"name":"International Food Policy Research Institute, 1201 I Street, NW, Washington, DC 20005, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2068-8610","authenticated-orcid":false,"given":"Baodong","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China"},{"name":"State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Aerospace Information Research Institute, Chinese Academy of Sciences and Beijing Normal University, Beijing 100101, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2101","DOI":"10.1080\/01431161.2012.738946","article-title":"Remote sensing of rice crop areas","volume":"34","author":"Kuenzer","year":"2012","journal-title":"Int. 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