{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T21:37:21Z","timestamp":1775943441426,"version":"3.50.1"},"reference-count":68,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2021,4,21]],"date-time":"2021-04-21T00:00:00Z","timestamp":1618963200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Project of China-Europe Cooperation Project","award":["2018YFE01070008ASP462"],"award-info":[{"award-number":["2018YFE01070008ASP462"]}]},{"name":"the \u201cSTS\u201d Project from Chinese Academy of Sciences","award":["KFJ-STS-QYZX-047"],"award-info":[{"award-number":["KFJ-STS-QYZX-047"]}]},{"name":"the Key Innovation Project Form Shandong Province","award":["2019JZZY010713"],"award-info":[{"award-number":["2019JZZY010713"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Estimating plant nitrogen concentration (PNC) has been conducted using vegetation indices (VIs) from UAV-based imagery, but color features have been rarely considered as additional variables. In this study, the VIs and color moments (color feature) were calculated from UAV-based RGB images, then partial least square regression (PLSR) and random forest regression (RF) models were established to estimate PNC through fusing VIs and color moments. The results demonstrated that the fusion of VIs and color moments as inputs yielded higher accuracies of PNC estimation compared to VIs or color moments as input; the RF models based on the combination of VIs and color moments (R2 ranging from 0.69 to 0.91 and NRMSE ranging from 0.07 to 0.13) showed similar performances to the PLSR models (R2 ranging from 0.68 to 0.87 and NRMSE ranging from 0.10 to 0.29); Among the top five important variables in the RF models, there was at least one variable which belonged to the color moments in different datasets, indicating the significant contribution of color moments in improving PNC estimation accuracy. This revealed the great potential of combination of RGB-VIs and color moments for the estimation of rice PNC.<\/jats:p>","DOI":"10.3390\/rs13091620","type":"journal-article","created":{"date-parts":[[2021,4,21]],"date-time":"2021-04-21T21:25:10Z","timestamp":1619040310000},"page":"1620","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["Estimating Plant Nitrogen Concentration of Rice through Fusing Vegetation Indices and Color Moments Derived from UAV-RGB Images"],"prefix":"10.3390","volume":"13","author":[{"given":"Haixiao","family":"Ge","sequence":"first","affiliation":[{"name":"The State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science Chinese Academy of Sciences, Nanjing 210008, China"},{"name":"College of Advanced Agricultural Sciences, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Haitao","family":"Xiang","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science Chinese Academy of Sciences, Nanjing 210008, China"}]},{"given":"Fei","family":"Ma","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science Chinese Academy of Sciences, Nanjing 210008, China"}]},{"given":"Zhenwang","family":"Li","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science Chinese Academy of Sciences, Nanjing 210008, China"}]},{"given":"Zhengchao","family":"Qiu","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science Chinese Academy of Sciences, Nanjing 210008, China"}]},{"given":"Zhengzheng","family":"Tan","sequence":"additional","affiliation":[{"name":"Yuan Longping High-Tech Agriculture Co., Ltd., Changsha 410001, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9064-3581","authenticated-orcid":false,"given":"Changwen","family":"Du","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science Chinese Academy of Sciences, Nanjing 210008, China"},{"name":"College of Advanced Agricultural Sciences, University of Chinese Academy of Sciences, Beijing 100049, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1126\/science.1070721","article-title":"The Rice Genome: The Cereal of the World\u2019s Poor Takes Center Stage","volume":"296","author":"Cantrell","year":"2002","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"10646","DOI":"10.3390\/rs70810646","article-title":"Satellite Remote Sensing-Based In-Season Diagnosis of Rice Nitrogen Status in Northeast China","volume":"7","author":"Huang","year":"2015","journal-title":"Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zheng, H., Cheng, T., Li, D., Yao, X., Tian, Y., Cao, W., and Zhu, Y. 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