文献类型：期刊文献

英文题名：Hyperspectral Inversion ofPhragmites CommunisCarbon, Nitrogen, and Phosphorus Stoichiometry Using Three Models

作者：Cui, Lijuan[1,2] Dou, Zhiguo[1,2] Liu, Zhijun[1,2] Zuo, Xueyan[1,2] Lei, Yinru[1,2] Li, Jing[1,2] Zhao, Xinsheng[1,2] Zhai, Xiajie[1,2] Pan, Xu[1,2] Li, Wei[1,2]

第一作者：Cui, Lijuan

通信作者：Li, W[1];Li, W[2]

机构：[1]Chinese Acad Forestry, Inst Wetland Res, Beijing Key Lab Wetland Serv & Restorat, Beijing 100091, Peoples R China;[2]Beijing Hanshiqiao Natl Wetland Ecosyst Res Stn, Beijing 101399, Peoples R China

年份：2020

卷号：12

期号：12

外文期刊名：REMOTE SENSING

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000553468700001)】；

基金：This research was funded by the China's special fund for basic scientific research business of central public research institutes (grant no. CAFYBB2017MA028).

语种：英文

外文关键词：wetland plant; stoichiometric characteristics; random forest; support vector machine; BP neural network

摘要：Studying the stoichiometric characteristics of plant C, N, and P is an effective way of understanding plant survival and adaptation strategies. In this study, 60 fixed plots and 120 random plots were set up in a reed-swamp wetland, and the canopy spectral data were collected in order to analyze the stoichiometric characteristics of C, N, and P across all four seasons. Three machine models (random forest, RF; support vector machine, SVM; and back propagation neural network, BPNN) were used to study the stoichiometric characteristics of these elements via hyperspectral inversion. The results showed significant differences in these characteristics across seasons. The RF model had the highest prediction accuracy concerning the stoichiometric properties of C, N, and P. The R(2)of the four-season models was greater than 0.88, 0.95, 0.97, and 0.92, respectively. According to the root mean square error (RMSE) results, the model error of total C (TC) inversion is the smallest, and that of C/N inversion is the largest. The SVM yielded poor predictive results for the stoichiometric properties of C, N, and P. The R(2)of the four-season models was greater than 0.82, 0.81, 0.81, and 0.70, respectively. According to RMSE results, the model error of TC inversion is the smallest, and that of C/P inversion is the largest. The BPNN yielded high stoichiometric prediction accuracy. The R(2)of the four-season models was greater than 0.87, 0.96, 0.84, and 0.90, respectively. According to RMSE results, the model error of TC inversion is the smallest, and that of C/P inversion is the largest. The accuracy and stability of the results were verified by comprehensive analysis. The RF model showed the greatest prediction stability, followed by the BPNN and then the SVM models. The results indicate that the accuracy and stability of the RF model were the highest. Hyperspectral data can be used to accurately invert the stoichiometric characteristics of C, N, and P in wetland plants. It provides a scientific basis for the long-term dynamic monitoring of plant stoichiometry through hyperspectral data in the future.