Sensitivity of multi-source SAR backscatter to changes of forest aboveground biomass

Accurate estimates of aboveground biomass (AGB) from forest after disturbance could reduce the uncertainties in carbon budget of terrestrial ecosystem and provide critical information to related carbon policy. Yet the loss of carbon from forest disturbance and the gain from post-disturbance recovery have not been well assessed. In this study, sensitivity analysis was conducted to investigate: (1) influence of factors other than the change of AGB (i.e. distortion caused by incident angle, soil moisture) on SAR backscatter; (2) feasibility of cross-image calibration between multi-temporal and multi-sensor SAR data; and (3) possibility of applying normalized backscatter to detect the post-disturbance AGB recovery. A semi-automatic empirical model was proposed to reduce the incident angle effect. Then, a cross-image normalization procedure was performed in order to remove the radiometric distortions among multi-source SAR data. The results indicate that effect of incident angle and soil moisture on SAR backscatter could be reduced by the proposed procedure, and a detection of biomass changes is possible using multi-temporal and multi-sensor SAR data.