Here, the backscatter model was used to analyze the relation between the backscatter and various VBPs. The results show that the backscatter model can be used to simulate the backscatter for wetland vegetation and to generate the training data for the ANN because wetland vegetation has the same structure as rice, for which the coefficients of determination are 0.93, 0.94, and 0.95 for HH, VV, and HV polarizations, respectively. However, SAR backscatter from vegetation-covered fields is a strong function of dielectric properties of the vegetation, vegetation canopy structure, vegetation volume along with moisture, and surface roughness of the underlying soil.46,51,52 Sometimes SAR sensors may receive strong backscatter because of the double-bounce between the water surface and the vegetation stem.53 Here, the soil properties were not considered in this study, because most of the sample locations were either flooded or oversaturated during image acquisition. Therefore, when the backscatter model is used to simulate the backscatter from vegetation, the backscatter will be underestimated because of the absence of the soil surface. The biomass will be wrongly estimated because of the assumption that the ground is covered by water in the backscatter model. If the ground soil was not flooded or oversaturated, the soil properties should be taken into account. If soil properties had been considered in the modeling process, the accuracy of the backscatter simulation and biomass inversion would have been expected to be improved. When the backscatter model is used, another factor that must be taken into consideration is the geometry (shape, size, geometric distribution, etc.) of the leaf. Here, the probability of geometric distribution function used in the model was adopted from Liu et al.,54 where the vegetation and study area are the same types as in this study.