文献类型：期刊文献

英文题名：Analyzing the performance of statistical models for estimating leaf nitrogen concentration of Phragmites australis based on leaf spectral reflectance

作者：Zhang, Manyin[1,2,3] Li, Mengjie[1,2,3] Liu, Weiwei[1,2,3] Cui, Lijuan[1,2,4] Li, Wei[1,2,4] Wang, Henian[1,2,3] Wei, Yuanyun[1,2,3] Guo, Ziliang[1,2,3] Wang, Daan[1,2,3] Hu, Yukun[1,2,3] Xu, Weigang[1,2,3] Yang, Si[1,2,3] Xiao, Hongye[1,2,3] Long, Songyuan[1,2,3]

第一作者：Zhang, Manyin;张曼胤

通信作者：Cui, LJ[1]

机构：[1]Chinese Acad Forestry, Inst Wetland Res, 1 Dongxiaofu, Beijing 100091, Peoples R China;[2]Beijing Key Lab Wetland Serv & Restorat, Beijing, Peoples R China;[3]Hengshuihu Natl Wetland Ecosyst Res Stn, Hengshui, Peoples R China;[4]Hanshiqiao Natl Wetland Ecosyst Res Stn, Beijing, Peoples R China

年份：2019

卷号：52

期号：9

起止页码：483-491

外文期刊名：SPECTROSCOPY LETTERS

收录：;EI(收录号：20242616306633);Scopus(收录号：2-s2.0-85074376300);WOS:【SCI-EXPANDED(收录号:WOS:000491002600001)】；

基金：This study was funded by China's Special Fund for Basic Scientific Research Business of Central Public Research Institutes [CAFINT2014K05].

语种：英文

外文关键词：Constructed wetland; hyperspectral; models; nitrogen

摘要：Nitrogen is an essential nutrient for plant growth and development. Rapid and nondestructive monitoring of nitrogen nutrition in plants using hyperspectral remote sensing is important for accurate diagnosis and quality evaluation of plant growth status. The sensitive bands of leaf nitrogen concentration varied in different plants. However, most of the current studies are concentrated on crops, and only a few studies focused on wetland plants. This study investigated the accuracy of the most common univariate, stepwise multiple linear regression, and partial least squares regression models for predicting leaf nitrogen content in a wetland plant reed (Phragmites australis) by testing the accuracy of all the models through leave-one-out cross validation coefficient of determination, root mean square error and relative error. It found that: (i) sensitive bands responding to leaf nitrogen concentration were concentrated in the green and red regions of visible light; (ii) for univariate regression models, the quadratic polynomial model based on the difference spectral index composed of 665?nm and 680?nm had the highest predictive accuracy (the validation coefficient of determination was 0.7535); (iii) for multivariate regression models, the stepwise multiple linear regression models had superior predictive accuracy to the partial least squares regression models, and the stepwise multiple linear regression model with first derivative reflectance was optimal for predicting leaf nitrogen concentration (the validation coefficient of determination was 0.7746, the validation root mean square error was 0.2925, and the validation relative error was 0.0804). The findings provide a scientific basis for rapid estimation and monitoring of leaf nitrogen concentration in P. australis in a nondestructive manner.