This paper is intended to report on the progresses made during the Dragon-4 project Three and Four-Dimensional Topographic Measurement and Validation (ID: 32278), sub-project Multi-baseline SAR Processing for 3D/4D Reconstruction ( ID:32278_2) . The work here reported focuses on two important aspects of SAR remote sensing of tropical forests, namely the retrieval of forest biomass and the assessment of effects due to changing weather conditions. Recent studies have shown that by using SAR tomography the backscattered power at 30 m layer above the ground is linearly correlated to the forest Above Ground Biomass ( AGB) . However, the two parameters that determine this linear relationship might vary for different tropical forest sites. For purpose of solving this problem, we investigate the possibility of using LiDAR derived AGB to help training the two parameters. Experimental results obtained by processing data from the TropiSAR campaign support the feasibility of the proposed concept. This analysis is complemented by an assessment of the impact of changing weather conditions on tomographic imaging, for which we simulate BIOMASS repeat pass tomography using ground-based TropiSCAT data with a revisit time of 3 days and rainy days included. The resulting backscattered power variation at 30 m is within 1. 5 dB. For this forest site, this error is translated into an AGB error of about 50～80 t/hm2 , which is 20％ or less of forest AGB.