Combined use of spectral resampling and machine learning algorithms to estimate soybean leaf chlorophyll

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.