The fusion of vegetation indices increases the accuracy of cotton leaf area prediction - Fan, Xianglong; Gao, Pan; Zhang, Mengli; Cang, Hao; Zhang, Lifu; Zhang, Ze; Wang, Jin; Lv, Xin; Zhang, Qiang; Ma, Lulu (Frontiers in Plant Science, 2024)
Rapid and accurate estimation of leaf area index (LAI) is of great significance for the precision agriculture because LAI is an important parameter to evaluate crop canopy structure and growth status. In this study, twenty vegetation indices were constructed by using cotton canopy spectra. Then, cotton LAI estimation models were constructed based on multiple machine learning (ML) methods (Extreme Learning Machine (ELM), Random Forest (RF), Back Propagation (BP), Multivariable Linear Regression (MLR), Support Vector Machine (SVM)), and the optimal modeling strategy (RF) was selected. Finally, the vegetation indices with a high correlation with LAI were fused to construct the VI-fusion RF model, to explore the potential of multi-vegetation index fusion in the estimation of cotton LAI. The results showed that the RF model had the highest estimation accuracy among the LAI estimation models, and the estimation accuracy of models constructed by fusing multiple VIs was higher than that of models constructed based on single VIs. Among the multi-VI fusion models, the RF model constructed based on the fusion of 7 vegetation indices (MNDSI, SRI, GRVI, REP, CIrededge, MSR, NVI) had the highest estimation accuracy, with coefficient of determination (R 2 ), rootmean-square error (RMSE), normalized-root-mean-square error (NRMSE), and mean absolute error (MAE) of 0.90, 0.50, 0.14, and 0.26, respectively. Therefore, appropriate fusion of vegetation indices can include more spectral features in modeling, and significantly improve the cotton LAI estimation accuracy. This study will provide a technical reference for improving the cotton LAI estimation accuracy, and the proposed method has great potential for crop growth monitoring applications.