{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T20:33:36Z","timestamp":1773952416029,"version":"3.50.1"},"reference-count":83,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2017,2,23]],"date-time":"2017-02-23T00:00:00Z","timestamp":1487808000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Youth Science Funds of LREIS, CAS","award":["O8R8A081YA"],"award-info":[{"award-number":["O8R8A081YA"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41371396"],"award-info":[{"award-number":["41371396"]}],"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":["61661136006"],"award-info":[{"award-number":["61661136006"]}],"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":["41471335"],"award-info":[{"award-number":["41471335"]}],"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":["41401491"],"award-info":[{"award-number":["41401491"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Introduction of International Advanced Agricultural Science and Technology, Ministry of Agriculture, P.R. China","award":["2016-X38"],"award-info":[{"award-number":["2016-X38"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>To improve the accuracy of winter wheat yield estimation, the Crop Environment Resource Synthesis for Wheat (CERES-Wheat) model with an assimilation strategy was performed by assimilating measured or remotely-sensed leaf area index (LAI) values. The performances of the crop model for two different assimilation methods were compared by employing particle filters (PF) and the proper orthogonal decomposition-based ensemble four-dimensional variational (POD4DVar) strategies. The uncertainties of wheat yield estimates due to different assimilation temporal scales (phenological stages and temporal frequencies) and spatial scale were also analyzed. The results showed that, compared with the crop model without assimilation and with PF-based assimilation, a better yield estimate performance resulted when the POD4DVar-based strategy was used at the field scale. When using this strategy, root mean square errors (RMSE) of 523 kg\u00b7ha\u22121, 543 kg\u00b7ha\u22121 and 172 kg\u00b7ha\u22121 and relative errors (RE) of 5.65%, 5.91% and 7.77% were obtained at the field plot scale, a pixel scale of 1 km and the county scale, respectively. Although the best yield estimates were obtained when all of the observed LAIs were assimilated into the crop model, an acceptable estimate of crop yield could also be achieved by assimilating fewer observations between jointing and anthesis periods of the crop growth season. With decreasing assimilation frequency and pixel resolution, the accuracy of the crop yield estimates decreased; however, the computation time decreased. Thus, it is important to consider reasonable  spatio-temporal scales to obtain tradeoffs between accuracy and effectiveness in regional  wheat estimates.<\/jats:p>","DOI":"10.3390\/rs9030190","type":"journal-article","created":{"date-parts":[[2017,2,23]],"date-time":"2017-02-23T11:45:01Z","timestamp":1487850301000},"page":"190","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":50,"title":["Improving Winter Wheat Yield Estimation from the CERES-Wheat Model to Assimilate Leaf Area Index with Different Assimilation Methods and  Spatio-Temporal Scales"],"prefix":"10.3390","volume":"9","author":[{"given":"He","family":"Li","sequence":"first","affiliation":[{"name":"State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}]},{"given":"Zhongxin","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Agri-informatics, Ministry of Agriculture\/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}]},{"given":"Gaohuan","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}]},{"given":"Zhiwei","family":"Jiang","sequence":"additional","affiliation":[{"name":"National Meteorological Information Science, Beijing 100081, China"}]},{"given":"Chong","family":"Huang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}]}],"member":"1968","published-online":{"date-parts":[[2017,2,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1111\/1467-8489.12091","article-title":"Global food security-introduction","volume":"58","author":"Rae","year":"2014","journal-title":"Aust. 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