{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T07:55:20Z","timestamp":1780386920443,"version":"3.54.1"},"reference-count":66,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,15]],"date-time":"2023-05-15T00:00:00Z","timestamp":1684108800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Key Research and Development Program of China","award":["2021YFD1500101"],"award-info":[{"award-number":["2021YFD1500101"]}]},{"name":"the National Key Research and Development Program of China","award":["2020YFA0714103"],"award-info":[{"award-number":["2020YFA0714103"]}]},{"name":"the National Key Research and Development Program of China","award":["20210201138GX"],"award-info":[{"award-number":["20210201138GX"]}]},{"name":"the National Key Research and Development Program of China","award":["71-Y50G10-9001-22\/23"],"award-info":[{"award-number":["71-Y50G10-9001-22\/23"]}]},{"name":"the Jilin Province Science and Technology development plan","award":["2021YFD1500101"],"award-info":[{"award-number":["2021YFD1500101"]}]},{"name":"the Jilin Province Science and Technology development plan","award":["2020YFA0714103"],"award-info":[{"award-number":["2020YFA0714103"]}]},{"name":"the Jilin Province Science and Technology development plan","award":["20210201138GX"],"award-info":[{"award-number":["20210201138GX"]}]},{"name":"the Jilin Province Science and Technology development plan","award":["71-Y50G10-9001-22\/23"],"award-info":[{"award-number":["71-Y50G10-9001-22\/23"]}]},{"name":"the major high-resolution Earth observation system project","award":["2021YFD1500101"],"award-info":[{"award-number":["2021YFD1500101"]}]},{"name":"the major high-resolution Earth observation system project","award":["2020YFA0714103"],"award-info":[{"award-number":["2020YFA0714103"]}]},{"name":"the major high-resolution Earth observation system project","award":["20210201138GX"],"award-info":[{"award-number":["20210201138GX"]}]},{"name":"the major high-resolution Earth observation system project","award":["71-Y50G10-9001-22\/23"],"award-info":[{"award-number":["71-Y50G10-9001-22\/23"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The assimilation of remote sensing data into mechanistic models of crop growth has become an available method for estimating yield. The objective of this study was to explore an effective assimilation approach for estimating maize grain protein content and yield using a canopy remote sensing data and crop growth model. Based on two years of field experiment data, the remote sensing inversion model using assimilation intermediate variables, namely leaf area index (LAI) and leaf nitrogen accumulation (LNA), was constructed with an R2 greater than 0.80 and a low root-mean-square error (RMSE). The different data assimilation approaches showed that when the LAI and LNA variables were used together in the assimilation process (VLAI+LNA), better accuracy was achieved for LNA estimations than the assimilation process using single variables of LAI or LNA (VLAI or VLNA). Similar differences in estimation accuracy were found in the maize yield and grain protein content (GPC) simulations. When the LAI and LNA were both intermediate variables in the assimilation process, the estimation accuracy of the yield and GPC were better than that of the assimilation process with only one variable. In summary, these results indicate that two physiological and biochemical parameters of maize retrieved from hyperspectral data can be combined with the crop growth model through the assimilation method, which provides a feasible method for improving the estimation accuracy of maize LAI, LNA, GPC and yield.<\/jats:p>","DOI":"10.3390\/rs15102576","type":"journal-article","created":{"date-parts":[[2023,5,16]],"date-time":"2023-05-16T02:27:04Z","timestamp":1684204024000},"page":"2576","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["The Estimation of Maize Grain Protein Content and Yield by Assimilating LAI and LNA, Retrieved from Canopy Remote Sensing Data, into the DSSAT Model"],"prefix":"10.3390","volume":"15","author":[{"given":"Bingxue","family":"Zhu","sequence":"first","affiliation":[{"name":"College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China"},{"name":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shengbo","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China"},{"name":"Jilin Institute of GF Remote Sensing Application, Changchun 130012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2284-3984","authenticated-orcid":false,"given":"Zhengyuan","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9186-7774","authenticated-orcid":false,"given":"Yinghui","family":"Ye","sequence":"additional","affiliation":[{"name":"College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cheng","family":"Han","sequence":"additional","affiliation":[{"name":"College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China"},{"name":"College of Tourism and Geographical Science, Jilin Normal University, Siping 136000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Lu","sequence":"additional","affiliation":[{"name":"College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9996-2450","authenticated-orcid":false,"given":"Kaishan","family":"Song","sequence":"additional","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1295","DOI":"10.1007\/s12571-022-01288-7","article-title":"Global maize production, consumption and trade: Trends and R&D implications","volume":"14","author":"Erenstein","year":"2022","journal-title":"Food Secur."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.rse.2019.04.016","article-title":"Smallholder maize area and yield mapping at national scales with Google Earth Engine","volume":"228","author":"Jin","year":"2019","journal-title":"Remote Sens. 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