文献类型：期刊文献

英文题名：Construction of Hyperspectral Reflectance and Spectral Exponential Inversion Model for the Water Content of Catalpa Bungei Leaves

作者：Lv, Siyu[1,2] Wang, Junhui[1] Wang, Zhengde[1] Fang, Yang[1] Wang, Shanshan[2] Wang, Fuyu[3] Wang, Xiaoxi[3] Qu, Guanzheng[2] Ma, Wenjun[1]

第一作者：Lv, Siyu

机构：[1] State Key Laboratory of Tree Genetics and Breeding, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091, China; [2] State Key Laboratory of Tree Genetics and Breeding, School of Forestry, Northeast Forestry University, Harbin, 150040, China; [3] Nanyang Forestry Science Research Institute, Nanyang, 473000, China

年份：2023

外文期刊名：SSRN

收录：EI(收录号：20230329767)

语种：英文

外文关键词：Drought - Plants (botany) - Regression analysis

摘要：Real-time monitoring of leaf water content is an important indicator of drought resistance in plants. In this study, hyperspectral reflectance and derived data are used to build an inversion model for leaf water content of Catalpa bungei. Rapid, non-destructive and real-time monitoring of leaf water content provides a high-throughput method for assessing drought resistance in tree seedlings. The hyperspectral reflectance and water content of mature leaves were determined and several models were built to evaluate the optimal combination using different variable selection and model construction methods. The results show that the PLS regression model constructed with reflectance as the input variable is the best for the test series. The MC-UVE method is the best for all models. With the PLS regression method, the model approach is optimal. MC-UVE-PLS model optimal test set regression coefficient (R2) maximum (0.7903) , mean square root error (RMSE) minimum (1.7352). SR (1466nm, 2128nm) is the spectral index with the highest water correlation. First order differencing can effectively improve the correlation between the spectral data and water content, but the model cannot be optimised. Using MC-UVE as a variable screening method, PLS regression was used to build an inversion model for leaf water content, which provides technical support for real-time monitoring of leaf water content of Catalpa bungei. ? 2023, The Authors. All rights reserved.