{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T15:52:19Z","timestamp":1784821939083,"version":"3.55.0"},"reference-count":58,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2018,12,3]],"date-time":"2018-12-03T00:00:00Z","timestamp":1543795200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["no. 41401473 and no. 31560130"],"award-info":[{"award-number":["no. 41401473 and no. 31560130"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"publisher","award":["BK20181474"],"award-info":[{"award-number":["BK20181474"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Open Fund of State Key Laboratory of Remote Sensing Science","award":["OFSLRSS201804"],"award-info":[{"award-number":["OFSLRSS201804"]}]},{"name":"project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions","award":["PAPD"],"award-info":[{"award-number":["PAPD"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Novel hyperspectral indices, which are the first derivative normalized difference nitrogen index (FD-NDNI) and the first derivative ratio nitrogen vegetation index (FD-SRNI), were developed to estimate the leaf nitrogen content (LNC) of wheat. The field stress experiments were conducted with different nitrogen and water application rates across the growing season of wheat and 190 measurements were collected on canopy spectra and LNC under various treatments. The inversion models were constructed based on the dataset to evaluate the ability of various spectral indices to estimate LNC. A comparative analysis showed that the model accuracies of FD-NDNI and FD-SRNI were higher than those of other commonly used hyperspectral indices including mNDVI705, mSR, and NDVI705, which was indicated by higher R2 and lower root mean square error (RMSE) values. The least squares support vector regression (LS-SVR) and random forest regression (RFR) algorithms were then used to optimize the models constructed by FD-NDNI and FD-SRNI. The p-R2 values of the FD-NDNI_RFR and FD-SRNI_RFR models reached 0.874 and 0.872, respectively, which were higher than those of the exponential and SVR model and indicated that the RFR model was accurate. Using the RFR inversion model, remote sensing mapping for the Operative Modular Imaging Spectrometer (OMIS) image was accomplished. The remote sensing mapping of the OMIS image yielded an accuracy of R2 = 0.721 and RMSE = 0.540 for FD-NDNI and R2 = 0.720 and RMSE = 0.495 for FD-SRNI, which indicates that the similarity between the inversion value and the measured value was high. The results show that the new hyperspectral indices, i.e., FD-NDNI and FD-SRNI, are the optimal hyperspectral indices for estimating LNC and that the RFR algorithm is the preferred modeling method.<\/jats:p>","DOI":"10.3390\/rs10121940","type":"journal-article","created":{"date-parts":[[2018,12,3]],"date-time":"2018-12-03T06:02:09Z","timestamp":1543816929000},"page":"1940","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":141,"title":["Estimation of Leaf Nitrogen Content in Wheat Using New Hyperspectral Indices and a Random Forest Regression Algorithm"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0331-3312","authenticated-orcid":false,"given":"Liang","family":"Liang","sequence":"first","affiliation":[{"name":"School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, China"},{"name":"Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA 22030, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liping","family":"Di","sequence":"additional","affiliation":[{"name":"Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA 22030, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ting","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiahui","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Lin","sequence":"additional","affiliation":[{"name":"Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA 22030, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lijuan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minhua","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,12,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.isprsjprs.2016.09.002","article-title":"Spatial variability of chlorophyll and nitrogen content of rice from hyperspectral imagery","volume":"122","author":"Moharana","year":"2016","journal-title":"ISPRS J. 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