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In this paper, we develop a novel method for forest biomass estimation. The intensity values of Advanced Land Observation Satellite-Advanced Visible and Near Infrared Radiometer type 2 and PRISM images and the texture features of the Japanese Earth Resources Satellite 1 image are used in a multilayer perceptron neural network (MLPNN) that relates them to the forest variable measurements on the ground. A proposed speckle noise model is also applied for modeling and reducing the speckle noise in the synthetic aperture radar (SAR) image. Reducing the speckle would improve the discrimination among different land use types and would make the textual classifiers more efficient in SAR images. Ideally, filters will reduce the speckle without loss of information. In the process of the forest biomass estimation, the filters should preserve the backscattering coefficient values and edges between different areas. We investigate both quantitative and qualitative criteria in speckle reduction and texture preservation to evaluate the performance of the proposed filter in the forest biomass estimation. We will also show that the biomass estimation accuracy is significantly improved in an MLPNN when the radar and the optical data are used in combination compared to estimating the biomass by using a single datum only. The root-mean-square error (rmse) value is decreased when the proposed method is used (rmse = 2.175 ton) compared with that of the classic method (rmse = 5.34 ton).