Estimating carbon stock is important for understanding ecosystem dynamics and mitigating climate change. However, biomass mapping in boreal forests faces challenges due to harsh conditions and limited ground truth data for large scale studies. This study presents a parametric model for accurate biomass estimation in the Acadia and Taiga Forest using GEDI Level 4A, Sentinel-2, and SRTM DEM data. We integrated these datasets, and developed the parametric model consisting of spectral bands, vegetation indices, and topographic information with regression techniques, Random Forest and K-nearest neighbour. Results showcase performance of the parametric model with relative weights of variables for accurate Aboveground Biomass Density (AGBD) predictions for the two forest sites. With an average RMSE ranging between 9 Mg/ha to 31 Mg/ha and <em>R</em><sup>2</sup> values of 0.54 to 0.60, the study reveals the importance of variables like slope, aspect and specific vegetation indices along with raw bands of Sentinel-2 data. Results also demonstrate potential and accuracy limitations of the proposed model with for biomass estimation with high resolution open-source satellite data without ground control. Further research include assessing the model robustness across diverse ecosystems and geographical settings, contributing to sustainable resource management practices.