{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T12:46:55Z","timestamp":1774615615533,"version":"3.50.1"},"reference-count":65,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,2,3]],"date-time":"2023-02-03T00:00:00Z","timestamp":1675382400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program","doi-asserted-by":"publisher","award":["2019YFE0125300"],"award-info":[{"award-number":["2019YFE0125300"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program","doi-asserted-by":"publisher","award":["KJCX20210433"],"award-info":[{"award-number":["KJCX20210433"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program","doi-asserted-by":"publisher","award":["CARS-03"],"award-info":[{"award-number":["CARS-03"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Special Project for Building Scientific and Technological Innovation Capacity of Beijing Academy of Agricultural and Forestry Sciences","award":["2019YFE0125300"],"award-info":[{"award-number":["2019YFE0125300"]}]},{"name":"Special Project for Building Scientific and Technological Innovation Capacity of Beijing Academy of Agricultural and Forestry Sciences","award":["KJCX20210433"],"award-info":[{"award-number":["KJCX20210433"]}]},{"name":"Special Project for Building Scientific and Technological Innovation Capacity of Beijing Academy of Agricultural and Forestry Sciences","award":["CARS-03"],"award-info":[{"award-number":["CARS-03"]}]},{"name":"National Modern Agricultural Industry Technology System","award":["2019YFE0125300"],"award-info":[{"award-number":["2019YFE0125300"]}]},{"name":"National Modern Agricultural Industry Technology System","award":["KJCX20210433"],"award-info":[{"award-number":["KJCX20210433"]}]},{"name":"National Modern Agricultural Industry Technology System","award":["CARS-03"],"award-info":[{"award-number":["CARS-03"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>LNC (leaf nitrogen content) in crops is significant for diagnosing the crop growth status and guiding fertilization decisions. Currently, UAV (unmanned aerial vehicles) remote sensing has played an important role in estimating the nitrogen nutrition of crops at the field scale. However, many existing methods of evaluating crop nitrogen based on UAV imaging techniques usually have used a single type of imagery such as RGB or multispectral images, seldom considering the usage of information fusion from different types of UAV imagery for assessing the crop nitrogen status. In this study, GS (Gram\u2013Schmidt Pan Sharpening) was utilized to fuse images from two sensors of digital RGB and multispectral cameras mounted on UAV, and the specific bands of the multispectral cameras are blue, green, red, rededge and NIR. The color space transformation method, HSV (Hue-Saturation-Value), was used to separate soil background noise from crops due to the high spatial resolution of UAV images. Two methods of optimizing feature variables, the Successive Projection Algorithm (SPA) and the Competitive Adaptive Reweighted Sampling method (CARS), combined with two regularization regression algorithms, LASSO and RIDGE, were adopted to estimate the LNC, compared to the commonly used Random Forest algorithm. The results showed that: (1) the accuracy of LNC estimation using the fusion image is improved distinctly by a comparison to the original multispectral image; (2) the denoised images performed better than the original multispectral images in evaluating LNC in rice; (3) the RIDGE-SPA combined method, using SPA to select the MCARI, SAVI and OSAVI, had the best performance for LNC in rice, with an R2 of 0.76 and an RMSE of 10.33%. It can be demonstrated that the information fusion of multiple-sensor imagery from UAV coupling with the methods of optimizing feature variables can estimate the rice LNC more effectively, which can also provide a reference for guiding the decision making of fertilization in rice fields.<\/jats:p>","DOI":"10.3390\/rs15030854","type":"journal-article","created":{"date-parts":[[2023,2,3]],"date-time":"2023-02-03T03:36:57Z","timestamp":1675395417000},"page":"854","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":48,"title":["Estimation of Leaf Nitrogen Content in Rice Using Vegetation Indices and Feature Variable Optimization with Information Fusion of Multiple-Sensor Images from UAV"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8669-9726","authenticated-orcid":false,"given":"Sizhe","family":"Xu","sequence":"first","affiliation":[{"name":"School of Agricultural Engineering, Jiangsu 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UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rachel","family":"Gaulton","sequence":"additional","affiliation":[{"name":"School of Natural and Environmental Sciences, Newcastle University, Newcastle Upon Tyne NE1 7RU, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5122-9759","authenticated-orcid":false,"given":"Qingzhen","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meng","family":"Yang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guijun","family":"Yang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianmin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Demonstration Center of The Quality Agricultural Products, Tianjin 301508, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongan","family":"Yang","sequence":"additional","affiliation":[{"name":"Demonstration Center of The Quality Agricultural Products, Tianjin 301508, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Min","family":"Yang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7957-5055","authenticated-orcid":false,"given":"Hanyu","family":"Xue","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaodong","family":"Yang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liping","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China"},{"name":"Key Laboratory of Quantitative Remote Sensing in Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.rse.2012.08.026","article-title":"Diagnostic mapping of canopy nitrogen content in rice based on hyperspectral measurements","volume":"126","author":"Inoue","year":"2012","journal-title":"Remote Sens. 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