Semiempirical Calibration of the WCM for Estimating Maize Biomass in Northeast China

The water cloud model (WCM) has been widely used to retrieve vegetation parameters, such as biomass and soil moisture. To address difficulties in the measurement of soil moisture data and inaccurate estimation of soil scattering in the original model, a semiempirical calibration of the WCM for maize biomass retrieval is proposed in this letter. In it, a change detection approach is used to estimate soil moisture and the effect of surface roughness, calculated from the polarization difference, is added to the soil layer scattering. By combining the two polarization retrieval results and setting the weight coefficient of the VV polarization to a value greater than that of the VH polarization, the optimal retrieval results for maize biomass are obtained for the study area. The accuracy of the calibration of the WCM is verified using the backscatter coefficients from Sentinel-1 data and ground-based maize biomass measurements. It is found that the root-mean-square error (RMSE) and the coefficient of determination ( R2) are 1.642 kg/m2and 0.803, respectively, between the calibration of the WCM results and the measurements. These results demonstrate the application potential of the C-band synthetic aperture radar data using the semiempirical calibration of the WCM for retrieving maize biomass over large-scale areas.