Estimation of Leaf Nitrogen Content in Wheat Using New Hyperspectral Indices and a Random Forest Regression Algorithm

作     者：Liang, Liang Di, Liping Huang, Ting Wang, Jiahui Lin, Li Wang, Lijuan Yang, Minhua 

作者机构：Jiangsu Normal Univ Sch Geog Geomat & Planning Xuzhou 221116 Jiangsu Peoples R China George Mason Univ Ctr Spatial Informat Sci & Syst Fairfax VA 22030 USA Cent South Univ Sch Geosci & Infophys Changsha 410083 Hunan Peoples R China 

出 版 物：《REMOTE SENSING》 (Remote Sens.)

年 卷 期：2018年第10卷第12期

页      面：1940-1940页

核心收录：

学科分类：0830[工学-环境科学与工程（可授工学、理学、农学学位）] 1002[医学-临床医学] 070801[理学-固体地球物理学] 07[理学] 08[工学] 0708[理学-地球物理学] 0816[工学-测绘科学与技术] 

基　　金：Natural Science Foundation of Jiangsu Province [BK20181474] Open Fund of State Key Laboratory of Remote Sensing Science [OFSLRSS201804] National Natural Science Foundation of China [41401473, 31560130] Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) 

主　　题：hyperspectral remote sensing crop parameter inversion spectral index design derivative algorithm optimization 

摘      要：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 mNDVI(705), mSR, and NDVI705, which was indicated by higher R-2 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-R-2 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 R-2 = 0.721 and RMSE = 0.540 for FD-NDNI and R-2 = 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.