Abstract:
The inversion of leaf area index (LAI) for optical data is susceptible to cloud occlusion and saturation of optical remote sensing information. Based on the radar scattering mechanism and Yamaguchi decomposition, this paper proposes a polarization decomposition vegetation index, and uses the optical vegetation index and the polar decomposition vegetation index to form an optical and microwave polarization decomposition fusion vegetation index, which, combined with the measured data, is used to establish a regression model so as to invert the leaf area index, and evaluate the accuracy of the model. Experiments show that the optical and microwave polarization decomposition fusion vegetation index and measured data establish a regression model that inverts the accuracy of leaf area index better than does the regression model established by polarization decomposition vegetation index and optical vegetation index and measured data, among which the regression model formed by MRVI and LAI is optimal, with
R2 reaching 0.726 2 and RMSE reaching 0.354 8. In summary, the optical and microwave polarization decomposition fusion vegetation index are able to fully utilize the characteristics of radar capable of penetrating dense plants; the inversion sensitivity of the fusion optical data to the leaf area index is capable of inverting the leaf area index more accurately.