外文摘要：For rapid estimation of soybean chlorophyll by hyperspectral technology, 76 soybean varieties were investigated by simulation of Landsat-8 satellite bands through spectral resampling technology and vegetation indices. Four different modeling methods viz. Support Vector Machine (SVM), Multiple Linear Regression (MLR), Partial Least Squares Regression (PLSR), and Back Propagation Neural Network (BPNN), were combined to analyze the response characteristics of resampled spectra to predict soybean chlorophyll and a prediction model based on vegetation indices was constructed. The results revealed that chlorophyll concentration and the corresponding spectral reflectance were inversely related. Also, Normalized Differential Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Transformed Vegetation Index (TVI) and Ratio Vegetation Index (RVI) were significantly correlated with chlorophyll and the GNDVI exhibited significant correlation with the whole growth period (r = 0.70). Furthermore, the BPNN model was the best in predicting the chlorophyll contents and vegetation index of the soybean at seed-filling stage (Rv2 = 0.846, RMSEv = 0.384, RPDv = 2.413). Based on the results, we propose that after spectral resampling, the soybean leaf chlorophyll content can be effectively predicted by BPNN with a combination of vegetation indices.
外文关键词：vegetation index;soybean;chlorophyll;hyperspectral reflectance;Spectral resampling
作者：Feng, Meichen;Wang, Chao;Yang, Wude;Xiao, Lujie;Song, Xiaoyan;Qiao, Xingxing;Shafiq, Fahad;Zhang, Xin;Li, Hao;Zhao, Yu;Gao, Chunrui;Wang, Jiachen;Huang, Kunming;Li, Fangzhou
作者单位：Shanxi Agr Univ;Govt Coll Univ
期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE
期刊影响因子：0.0
出版年份：2024
出版刊次：218
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台