Estimates of aboveground biomass (AGB) in forests have been made in the context of climate change mitigation. There were limited studies about Falcata aboveground biomass and carbon stock estimation using the traditional method; however, that is time-intensive and expensive. Hence, this study was executed to utilize remote sensing and assess the potential of the synergistic use of Sentinel-1 and Sentinel-2 images in estimating the AGB and carbon stock of Falcata. The methodology consists of boundary demarcation of Falcata plantations in Butuan City, development of aboveground biomass models, and estimation and mapping of aboveground biomass and carbon stock. Among the developed models, the model which is the combination of Sentinel-1 and Sentinel-2 has the highest coefficient of determination (R<sup>2</sup>) of 0.632 and lowest Root Mean Square Error (RMSE) of 1.94 ton/pixel and was found to perform best in predicting the AGB and carbon stock of 4-year-old Falcata and performed poorly in 1-year-old Falcata. Nevertheless, its overall R<sup>2</sup> and RMSE have proven that the model is moderately good and acceptable in predicting AGB of all stand ages of Falcata and, indirectly, the carbon stock. This study demonstrates that combining satellites generates a robust and more accurate AGB and Carbon Stock model than the models derived from the individual satellites.