Monitoring carbon stock has emerged as a critical environmental problem among several worldwide organizations and collaborations in the context of global warming and climate change. This study seeks to provide a remote sensing solution based on three types of data, to explore the feasibility and reliability of estimating aboveground biomass (AGB) in order to improve the efficiency of monitoring carbon stock. The study attempted to investigate the potential of using Google Earth Engine (GEE), and the combinations of different datasets from Sentinel-1 (SAR), Sentinel-2 multispectral imagery, and LiDAR data to estimate AGB, by using the random forest algorithm (RF). Two models were proposed: the first one (Model 1) detected the AGB temporal changes from 2016 to 2021 in Southern Sweden; while the second one (Model 2) focused on Hultsfred municipality and studied the influence of different variables including the canopy height. Besides, six experimental groups of variables were tested to determine the performance of using different types of remote sensing data. We validated these two models with the observed AGB, and the findings showed that the combination of SAR polarization, multisprectral bands, vegetation indices able to estimate AGB for Model 1. In addition, Model 2 showed that further using the canopy height data can further improve the estimation. 

We also found out that the spectral bands from Sentinel-2 contributed the most to AGB estimation for Model 1 in terms of: bands B3 (Green), B4 (Red), B5 (Red edge), B11 (SWIR), B12 (SWIR); and, vegetation indices of RVI, DVI, and EVI. On the other hand, for Model 2, B1(Ultra blue), B4 (Red), EVI, SAVI, and the canopy height are the most crucial variables for estimating AGB. Besides, the radar backscatter values using VV and VH modes from Sentienl-1 were both important for Models 1 and 2. For Model 1, the experimental group with the best accuracy was the group that used all variable combinations from Sentinel-1 and 2, and its   was 0.33~0.74. For Model 2, the group that used all the variables, in addition to the canopy height performed the best, where its   is 0.91. These therefore showed the benefit of integrating different remote sensing data sources. 

In conclusion, this study showed the potential of using RF and GEE to estimate AGB in Southern Sweden. Furthermore, this study also shows the possibility of handling large dataset for a large scale area, at the resolution of 10 m, and producing time series AGB maps from 2016 to 2021. This can help enhance our understanding of AGB temporal changes and carbon stock detection in Southern Sweden, that can provide valuable insights for forest management and carbon monitoring.