Natural and anthropogenic disturbances have affected the carbon sink capacity in tropical forests. Tree mortality decreases the number of trees to capture carbon in the atmosphere, while the growth of secondary forests can enhance carbon assimilation. Studies have demonstrated the destructive effects of strong wind disturbances on northern and western Amazonian forests. However, few studies focus on quantifying the carbon loss due to strong wind events. In this study, we use satellite-retrieval products of vegetation optical depth from the Soil Moisture and Ocean Salinity (SMOS) mission to identify biomass loss signals in the Amazon. Empirical functions will be applied to quantify carbon loss. PyFLEXTRKR MCS Tracking Dataset and ERA5 reanalysis data are used to characterize strong wind events. To predict biomass loss associated with these storms, such as intensity, occurrence, and duration, we will construct a generalized linear model and a machine-learning-based model. Case studies from the SMOS retrieval products are consistent with previous studies. With increasing convective available potential energy and forest degradation in the future changing climate, the relations between storm activities and tropical forest biomass loss are worth further investigation.