{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T03:05:52Z","timestamp":1784257552094,"version":"3.55.0"},"reference-count":102,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,12]],"date-time":"2022-05-12T00:00:00Z","timestamp":1652313600000},"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":["NSFC 42171075"],"award-info":[{"award-number":["NSFC 42171075"]}],"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":["BP0820003"],"award-info":[{"award-number":["BP0820003"]}],"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":["XDA28060200"],"award-info":[{"award-number":["XDA28060200"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"State Key Laboratory of Earth Surface Processes and Resource Ecology","award":["NSFC 42171075"],"award-info":[{"award-number":["NSFC 42171075"]}]},{"name":"State Key Laboratory of Earth Surface Processes and Resource Ecology","award":["BP0820003"],"award-info":[{"award-number":["BP0820003"]}]},{"name":"State Key Laboratory of Earth Surface Processes and Resource Ecology","award":["XDA28060200"],"award-info":[{"award-number":["XDA28060200"]}]},{"name":"Program of Introducing Talent to Universities of China","award":["NSFC 42171075"],"award-info":[{"award-number":["NSFC 42171075"]}]},{"name":"Program of Introducing Talent to Universities of China","award":["BP0820003"],"award-info":[{"award-number":["BP0820003"]}]},{"name":"Program of Introducing Talent to Universities of China","award":["XDA28060200"],"award-info":[{"award-number":["XDA28060200"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["NSFC 42171075"],"award-info":[{"award-number":["NSFC 42171075"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["BP0820003"],"award-info":[{"award-number":["BP0820003"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDA28060200"],"award-info":[{"award-number":["XDA28060200"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Spatially explicit crop yield datasets with continuous long-term series are essential for understanding the spatiotemporal variation of crop yield and the impact of climate change on it. There are several spatial disaggregation methods to generate gridded yield maps, but these either use an oversimplified approach with only a couple of ancillary data or an overly complex approach with limited flexibility and scalability. This study developed a spatial disaggregation method using improved spatial weights generated from machine learning. When applied to Chinese maize yield, extreme gradient boosting (XGB) derived the best prediction results, with a cross-validation coefficient of determination (R2) of 0.81 at the municipal level. The disaggregated yield at 1 km grids could explain 54% of the variance of the county-level statistical yield, which is superior to the existing gridded maize yield dataset in China. At the site level, the disaggregated yields also showed much better agreement with observations than the existing gridded maize yield dataset. This lightweight method is promising for generating spatially explicit crop yield datasets with finer resolution and higher accuracy, and for providing necessary information for maize production risk assessment in China under climate change.<\/jats:p>","DOI":"10.3390\/rs14102340","type":"journal-article","created":{"date-parts":[[2022,5,12]],"date-time":"2022-05-12T23:08:36Z","timestamp":1652396916000},"page":"2340","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Improving Spatial Disaggregation of Crop Yield by Incorporating Machine Learning with Multisource Data: A Case Study of Chinese Maize Yield"],"prefix":"10.3390","volume":"14","author":[{"given":"Shuo","family":"Chen","sequence":"first","affiliation":[{"name":"State Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE), Beijing Normal University, Beijing 100875, China"},{"name":"Key Laboratory of Environmental Change and Natural Disasters, Ministry of Education, Beijing Normal University, Beijing 100875, China"},{"name":"Academy of Disaster Reduction and Emergency Management, Ministry of Emergency Management and Ministry of Education, Beijing 100875, China"},{"name":"Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0732-4898","authenticated-orcid":false,"given":"Weihang","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE), Beijing Normal University, Beijing 100875, China"},{"name":"Key Laboratory of Environmental Change and Natural Disasters, Ministry of Education, Beijing Normal University, Beijing 100875, China"},{"name":"Academy of Disaster Reduction and Emergency Management, Ministry of Emergency Management and Ministry of Education, Beijing 100875, China"},{"name":"Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Puyu","family":"Feng","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100193, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Ye","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE), Beijing Normal University, Beijing 100875, China"},{"name":"Key Laboratory of Environmental Change and Natural Disasters, Ministry of Education, Beijing Normal University, Beijing 100875, China"},{"name":"Academy of Disaster Reduction and Emergency Management, Ministry of Emergency Management and Ministry of Education, Beijing 100875, China"},{"name":"Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuchi","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Biological Systems Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7816-672X","authenticated-orcid":false,"given":"Zhou","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Biological Systems Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1038\/s43016-021-00335-4","article-title":"Extreme Climate Events Increase Risk of Global Food Insecurity and Adaptation Needs","volume":"2","author":"Hasegawa","year":"2021","journal-title":"Nat. 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