International Journal of Applied Earth Observations and Geoinformation (Dec 2022)
Improved forest biomass estimation based on P-band repeat-pass PolInSAR data across different forest sites
Abstract
The upcoming BIOMASS mission will provide P-band repeat-pass PolInSAR data from space for the improved mapping of global biomass. PolInSAR technique has been widely validated with the potential to invert forest height and estimate forest aboveground biomass (AGB). However, the robustness of PolInSAR-based AGB estimation across different sites still lacks full evaluation, especially for those with a varied forest type, heterogeneity (varied growth ratio between cover and height), and topographic relief. In this study, we concentrated on backscatter decomposition and forest height inversion, and developed a robust AGB estimation method that can be applied to different sites. Two dense and closed tropical forest sites (Paracou and Nouragues) and one open and heterogeneous boreal forest site (Krycklan) were selected as the study areas, and the corresponding airborne PolInSAR, LiDAR, and ground measured AGB data were used for validation and analysis. Results show that ground backscatter has the strongest correlation with AGB in boreal forests, but this correlation cannot be transferred to the tropical forests. Only canopy volume backscatter is almost free from topographic influence, and its relationship with AGB across three sites can be formulated using one exponential equation, producing the best estimation accuracy, with R2 of 0.79 and RMSE of 61.5 tons/ha (relative RMSE of 20.0 %). Multi-baseline PolInSAR retrieved forest height with little bias in spite of the presence of temporal decorrelation. One power equation can be used to correlate PolInSAR forest height with AGB across three different sites, and LOO (leave-one-out) validation shows the R2 of 0.85 and RMSE of 51.8 tons/ha (relative RMSE of 16.9 %). However, the RVoG-inverted PolInSAR FH was found to mainly represent the top forest height for open and heterogeneous forests, which means PolInSAR FH (forest height) lacks consideration for forest horizontal structure (e.g. forest density). In contrast, volume backscatter better captured forest density, and the proposed AGB model that combines PolInSAR FH and volume backscatter further improved the AGB estimation accuracy, especially for open forests: the plot-scale validation from all three sites shows R2 was improved from 0.79 (volume backscatter) and 0.85 (PolInSAR FH) to 0.89, and RMSE decreased from 61.5 and 51.8 to 45.2 (relative RMSE of 14.7 %) tons/ha; for region-scale validation, R2 was improved from 0.77 and 0.83 to 0.89, and RMSE decreased from 64.2 (relative RMSE of 39.0 %) and 54.5 (34.5 %) to 48.1 (29.4 %) tons/ha.
