Nowadays, the increasing demand for energy does not contribute to the protection of natural resources, and forest resources are decreasing worldwide. In addition, forests play an important role in ecosystems, by intercepting and absorbing solar radiation in order to convert it to heat. Estimates of forest biomass were originally developed through the allometric growth equation, but there are other factors in heterogeneous forests that affect forest biomass estimates, such as the distribution of

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... trees and human intervention in the forest environment. In this way, biomass estimation is not easy in terms of accuracy and landscape diversity. The forest biomass inversion algorithm proposed in this study is derived from linear mathematical equations, which are mainly derived from the database training established by the vegetation radar remote sensing model. In this paper, two different models are used to simulate the backscattering coefficients of forest vegetation. The first is the radar coherent scatter model (CORSM), which takes into account the location of the tree scatterers. The second is the radar incoherent model (MIMICS), which considers the influence of the statistical distribution of forest vegetation parameters on the radar backscatter coefficient. The radar backscattering coefficients σHH, σHV and σVV of forest vegetation are obtained by model simulation. Although the σHV value is small, it usually reflects the body scattering information of the forest vegetation scatterer. Therefore, when establishing the forest biomass inversion equation, not only the co-polarized backscattering information but also the cross-polarized scattering information is considered as it has an impact on the inversion model results. Finally, the Sentinel 1 and Alos Palsar image data of the C and L bands were input into the inversion model to extract the forest biomass. The estimated results show that as the forest biomass increases, the backscattering coefficient of the co-polarized rad [...]