Dynamic Harvest Index Estimation of Winter Wheat Based on UAV Hyperspectral Remote Sensing Considering Crop Aboveground Biomass Change and the Grain Filling Process

The crop harvest index (HI) is of great significance for research on the application of crop variety breeding, crop growth simulation, crop management in precision agriculture and crop yield estimation, among other topics. To obtain spatial information on the crop dynamic HI (D-HI), taking winter wheat as the research object and fully considering the changes in crop biomass and the grain filling process from the flowering period to the maturity period, the dynamic f_G (D-f_G) parameter was estimated as the ratio between the aboveground biomass accumulated in different growth periods, from the flowering stage to the maturity stage, and the aboveground biomass in the corresponding periods. Based on the D-f_G parameter estimation using unmanned aerial vehicle (UAV) hyperspectral remote sensing data, a technical method for obtaining spatial information on the winter wheat D-HI was proposed and the accuracy of the proposed method was verified. A correlation analysis was performed between the normalized difference spectral index (NDSI), which was calculated using pairs of any two bands of the UAV hyperspectral spectrum, and the measured D-f_G. Based on this correlation analysis, the center of gravity of the local maximum region of R² was used to determine the sensitive band center to accurately estimate D-f_G. On this basis, remote sensing estimation of the D-f_G was realized by using the NDSI constructed by the sensitive hyperspectral band centers. Finally, based on the D-f_G remote sensing parameters and the D-HI estimation model, spatial information on the D-HI of winter wheat was accurately obtained. The results revealed five pairs of sensitive hyperspectral band centers (i.e., λ(476 nm, 508 nm), λ(444 nm, 644 nm), λ(608 nm, 788 nm), λ(724 nm, 784 nm) and λ(816 nm, 908 nm)) for D-f_G estimation, and the results of the D-f_G remote sensing estimation showed high precision. The root mean square error (RMSE) was between 0.0436 and 0.0604, the normalized RMSE (NRMSE) was between 10.31% and 14.27% and the mean relative error (MRE) was between 8.28% and 12.55%. In addition, the D-f_G parameter estimation, using the NDSI constructed by the above five sensitive remote sensing band centers, yielded highly accurate spatial D-HI information with an RMSE between 0.0429 and 0.0546, an NRMSE between 9.87% and 12.57% and an MRE between 8.33% and 10.90%. The D-HI estimation results based on the hyperspectral sensitive band centers λ(724 nm, 784 nm) had the highest accuracy, with RMSE, NRMSE and MRE values of 0.0429, 9.87% and 8.33%, respectively. The proposed method of acquiring spatial information on the winter wheat D-HI in this study was shown to be feasible, and it might provide a technical reference toward developing satellite-based indices to monitor large-scale crop HI information.