Aim: To model and estimate the total above ground biomass (AGB) of forest with the best model out of five different regression models Location: Shreenagar Hill Forest, Tansen Municipality, Nepal Time Period: During the month of July, 2023 Major Taxa Studies: Pinus roxburghii Methods: Sentinel-2 satellite imagery and field-measured AGB at plot level were used. Field data were collected from a total of 26 sample. Randomly chosen 18 sample plots (SPs) (70%) were used to generate the model and remaining 8 SPs (30%) for validation of developed model. Using various bands with 10m spatial resolution, eleven VIs were calculated & correlated with field measured AGB at plot level. Results & Main Conclusions: Evaluating the fit statistics, quadratic regression model using NDVI with correlation coefficient (R) 0.92, coefficient of determination (R^2) 0.86, AIC (161.13) & BIC (164.69) was found as the best model. Predicted value of AGB from best model and observed value of AGB from field were used for model validation. Root mean square error (RMSE), R & R^2 were found as 13.3594 t.ha-1per plot, 0.9597 and 0.9211 respectively during the model validation. Therefore, the quadratic regression model with NDVI as best fit model was used to estimate the total AGB and carbon stock (CS) of study area. The average value of AGB & CS (including no vegetation area) for total study area were found 192.403 & 90.429 t.ha-1 respectively. The value of AGB & CS range from 0 to 233.451 & 0 to 109.722 t.ha-1 per pixel respectively. The benefits, possibilities, and effectiveness of combining Sentinel-2 VIs with field data to forecast biomass are demonstrated by this work. To reduce the estimation error & make wider application of research, very large sample size can be chosen by future researchers.