Biomass estimation in dense tropical forest using multiple information from single-baseline P-band PolInSAR data

Abstract With the upcoming BIOMASS mission, P-band PolInSAR is expected to provide new perspectives on global forest aboveground biomass (AGB). However, its performance has not yet been fully evaluated for dense tropical forests with complex structure and very high biomass. Based on the TropiSAR campaign in French Guiana, we explored the challenges of the three most commonly used PolInSAR measures to capture AGB in tropical forests; coherence magnitude, interferometric phase, and backscatter. An improved AGB estimation approach was developed by integrating multiple information derived from single-baseline PolInSAR data. The approach involves ground-volume backscatter decomposition and combines volume backscatter with the retrieved forest height. Volume backscatter from the forest canopy was the best predictor of AGB for tropical forests, whereas the ground backscatter contribution was affected by the complex underlying surface and terrain slope. Both LiDAR- and PolInSAR-derived forest heights showed limited correlation with high AGB due to the varying forest basal area. The linear combination of PolInSAR-derived forest height and volume backscatter complemented each other and produced improved AGB estimates. Comparing three different PolInSAR data pairs, the proposed method produced an AGB map with an average R2 of 0.7 and RMSE of 34 tons/ha (relative RMSE of 9.4%) at a spatial resolution of 125 × 125 m2 for biomass between 250–500 tons/ha.