The Research Of Fully Polarized ALOS-2 Data For Estimating Forest Above-Ground Biomass

Accurate estimation of AGB is significant for carbon neutralization.Regional and global biomass estimates have important implications for understanding and monitoring ecosystems.SAR data have a longer wavelength and stronger penetrating power compared with traditional optical remote sensing.Therefore,SAR data are more suitable for the estimation of the above-ground biomass(AGB)of forests.This study was aimed at evaluating the sensitivity of L-band full polarization data to AGB.We processed the L-band data to obtain parameters related to AGB.We used these parameters to combine 58 samples to build a model,and were found to be suitable for estimating a wide range of AGB.This study extracted backscattering coeffificients,polarization decomposition variables,and terrain factors.New parameters were constructed from these variables,and their performance in predicting AGB was evaluated.Signifificant variables found with AGB were added to the multivariate linear model.A statistical analysis showed the presence of multicollinearity between the variables.Therefore,ridge regression,random forest method(RF),and principal component analysis(PCA)were introduced to solve the problem of collinearity.(1)There was a strong correlation between backscattering coefficient and AGB,but the univariate and multivariate models of backscattering coefficient can not accurately estimate AGB.The combination of backscatter coefficients does not significantly improved the ability to estimate AGB.Meanwhile,topographic factors were not strongly sensitive to AGB in this study.(2)Three polarization decomposition methods were selected by screening,and the multivariate linear model established after screening the parameters had strong collinearity among variables,and the model was not suitable for estimating AGB on a large regional scale.The constructed three indices show that the SAR parameters contain certain complementary information.(3)In all the three methods,the saturation of the ridge regression model was low,reaching it at 150 t/ha.Better accuracy was obtained with the RF model.No obvious saturation incident was detected in the model established using the principal component analysis.This could be attributed to the low biomass levels observed in our study area.This model provided accurate results(adjusted r~2=0.62 rmse=15.24 t/ha).Indicating that L-band data have the potential to estimate AGB.Additionally,suitable variables and models were selected in this study,with the principal component analysis being more helpful in combining various SAR parameters.The achievement of these accurate results could be attributed to the synergy among variables.