Sensitivity of L-Band Radar Backscatter to Forest Biomass in Semiarid Environments: A Comparative Analysis of Parametric and Nonparametric Models

This paper investigated the effectiveness of frequently used parametric and nonparametric models for biomass retrieval from L-band radar backscatter. Two areas, one in Spain and one in Australia, characterized by different tree species, forest structure, and field sampling designs were selected to demonstrate that retrieval error metrics are similar for different local conditions and sampling characteristics. A mixed-model retrieval strategy was proposed to reduce the overall (i.e., across the entire biomass range) as well as by-biomass-interval errors. Significant relationships were found between aboveground biomass and radar backscatter with most of the backscatter dynamic range being limited to a fairly low range of biomass values ( t/ha) in both study areas. Biomass retrieval errors were largely similar for all parametric and nonparametric models tested. However, some parametric models consistently provided lower correlation between the observed and the predicted biomass while nonparametric models generally provided an unbiased estimation. A mixed-model retrieval strategy was shown to reduce biomass estimation errors by up to 15%. Biomass retrieval errors were highly variable within the L-band sensitivity interval, suggesting that overall accuracy estimates should be used with care, particularly for low biomass intervals ( t/ha) where surface scattering could dominate the total backscatter. Despite exhibiting the highest dynamic range, low biomass areas were characterized by the highest estimation errors (in excess of 80%). Conversely, relative estimation errors were as low as 20%-35% for the 30-75 t/ha biomass intervals, while at higher biomass levels, the estimation error increased due to signal saturation.