Abstract: Hyperspectral technique has been an effective method to monitor the vegetation biomass as a rapid and nondestructive approach. However, the accuracy of biomass estimation is always limited by the influence of soil background. The purpose of this study aimed to alleviate the effects of soil on spectra and improve the accuracy of wheat biomass estimation based on the extracted vegetation spectra by blind source separation (BSS) method. In this study, with the application of fast independent component analysis (FastICA), pure vegetation spectra were extracted from the 110 groups of original fieldobserved canopy spectra, and the wheat biomass estimation accuracy were compared before and after FastICA with the partial least squares regression (PLSR). The results showed that the FastICA method could separate the soil spectra and vegetation spectra effectively, and the accuracy of wheat biomass estimation was significantly improved based on the extracted vegetation spectra, as compared with the original spectral, with the improvement of the ratio of performance to deviation of the calibration (RPD_c) and the ratio of performance to deviation of the cross calibration (RPD_cv) from 1.83 and 1.64 to 2.77 and 2.09, respectively. These results indicated that FastICA method could be applied as an effective spectral preprocessing method to significantly improve the accuracy of biomass estimation, thus providing guidance for accurate regional monitoring of wheat biomass by hyperspectral technology.

Key words: soil carbon mineralization, precipitation reduction, soil aggregate.