{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T03:23:02Z","timestamp":1768447382240,"version":"3.49.0"},"reference-count":44,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,8,3]],"date-time":"2022-08-03T00:00:00Z","timestamp":1659484800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["41971383"],"award-info":[{"award-number":["41971383"]}]},{"name":"National Natural Science Foundation of China","award":["ST\/V001388\/1"],"award-info":[{"award-number":["ST\/V001388\/1"]}]},{"name":"University College London Research Fund","award":["41971383"],"award-info":[{"award-number":["41971383"]}]},{"name":"University College London Research Fund","award":["ST\/V001388\/1"],"award-info":[{"award-number":["ST\/V001388\/1"]}]},{"name":"Ant Group","award":["41971383"],"award-info":[{"award-number":["41971383"]}]},{"name":"Ant Group","award":["ST\/V001388\/1"],"award-info":[{"award-number":["ST\/V001388\/1"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate and timely regional crop yield information, particularly field-level yield estimation, is essential for commodity traders and producers in planning production, growing, harvesting, and other interconnected marketing activities. In this study, we propose a novel data assimilation framework. Firstly, we construct the likelihood constraints for a process-based crop growth model based on the previous year\u2019s statistical yield and the current year\u2019s field observations. Then, we infer the posterior sets of model-simulated time-series LAI and the final yield of winter wheat with a Markov chain Monte Carlo (MCMC) method for each meteorological data grid of the European Centre for Medium-Range Weather Forecasts Reanalysis (v5ERA5). Finally, we estimate the winter wheat yield at the spatial resolution of 10 m by combining Sentinel-2 LAI and the WOFOST model in Hengshui, the prefecture-level city of Hebei province of China. The results show that the proposed framework can estimate the winter wheat yield with a coefficient of determination R2 equal to 0.29 and mean absolute percentage error MAPE equal to 7.20% compared to within-field measurements. However, the agricultural stress that crop growth models cannot quantitatively simulate, such as lodging, can greatly reduce the accuracy of yield estimates.<\/jats:p>","DOI":"10.3390\/rs14153727","type":"journal-article","created":{"date-parts":[[2022,8,3]],"date-time":"2022-08-03T23:33:01Z","timestamp":1659569581000},"page":"3727","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Bayesian Posterior-Based Winter Wheat Yield Estimation at the Field Scale through Assimilation of Sentinel-2 Data into WOFOST Model"],"prefix":"10.3390","volume":"14","author":[{"given":"Yantong","family":"Wu","sequence":"first","affiliation":[{"name":"School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"given":"Wenbo","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4099-8675","authenticated-orcid":false,"given":"Hai","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0341-1983","authenticated-orcid":false,"given":"Jianxi","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"},{"name":"Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1038\/s43017-020-00122-y","article-title":"Uniting remote sensing, crop modelling and economics for agricultural risk management","volume":"2","author":"Benami","year":"2021","journal-title":"Nat. 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