Above Ground Biomass (AGB) is a vital factor in the forest ecosystem, closely linked to the carbon cycle and global climate change. Synthetic Aperture Radar (SAR) remote sensing is a potent tool for AGB quantification, due to its ability to penetrate vegetation canopies and its reliability for all-weather forest mapping and monitoring. The study used HH/HV dual-polarization SAR data from EOS-04 (C) and ALOS-2 PALSAR-2 (L) satellites to estimate AGB. Multiple linear regression-based statistics model was developed for AGB prediction by considering the best suited frequency and polarisation data for different forest density classes in the study area. The results revealed a strong correlation between AGB and HV backscatter from both the frequencies. The combined HV backscatter from both the sensors showed improvement in the goodness-of-fit (R 2  > 0.5) with reduced error for all the forest density classes. The model estimated AGB was validated with the ground estimated AGB over 80 number of forest inventory plots (0.1 ha), and the overall root-mean-squared error corresponding to the estimated AGB was 32.02 Mg/ha. The model predicted versus ground estimated AGB showed a high correlation upto AGB density of 120 Mg/ha, beyond which underestimation was observed due to saturation of SAR backscatter at higher AGB density values. The AGB in the study ranged from about 10 to 200 Mg/ha. From the results, it was observed that the use of multi-frequency SAR data can be helpful in reducing error with consideration of forest categorisation in the AGB prediction model.