Forest stand biomass serves as an effective indicator for monitoring REDD (reducing emissions fromdeforestation and forest degradation). Optical remote sensing data have been widely used to derive forestbiophysical parameters inspite of their poor sensitivity towards the forest properties. Microwave remotesensing provides a better alternative owing to its inherent ability to penetrate the forest vegetation.This study aims at developing optimal regression models for retrieving forest above-ground bole biomass(AGBB) utilising optical data from Landsat TM and microwave data from L-band of ALOS PALSARdata over Indian subcontinental tropical deciduous mixed forests located in Munger (Bihar, India). Spatialbiomass models were developed. The results using Landsat TM showed poor correlation (R^2 =0.295and RMSE=35 t/ha) when compared to HH polarized L-band SAR (R^2 =0.868 and RMSE=16.06 t/ha).However, the prediction model performed even better when both the optical and SAR were used simultaneously(R^2 =0.892 and RMSE=14.08 t/ha). The addition of TM metrics has positively contributed inimproving PALSAR estimates of forest biomass. Hence, the study recommends the combined use of bothoptical and SAR sensors for better assessment of stand biomass with significant contribution towardsoperational forestry.