Conference Agenda

The structure of forests directly indicates the amount of carbon stored in the ecosystem. This total mass of living vegetation above the soil, Above-Ground Biomass (AGB), impacts how the ecosystem cycles carbon, nutrients, and water. With the launch of BIOMASS (P-band) and NISAR (L-band and S-band) missions this year, we are now better equipped to make comprehensive, accurate measurements of Earth's surface features, owing to the longer wavelength of P-band, which can penetrate deeper into the forest canopy, and S-band, which is sensitive to light vegetation.

This study focuses on modeling the forest AGB using full polarimetric L-band SAR data from the NISAR Simulated UAVSAR campaign. These datasets simulate NISAR satellite acquisitions and include geocoded covariance products in HDF5 format with associated metadata. The CX products used in this study provide HH and HV backscatter, which are further modelled according to the NISAR's Algorithm Theoretical Basis Document. The validation is carried out using the CHM-AGB relationship, which provides the biomass in Mg/ha based on LiDAR data. For parameter optimization, the differential evolution method is employed. As the NISAR ATBD is a nonlinear implicit function that cannot be inverted mathematically, multiple inversion techniques were tested, including both parametric and non-parametric methods. The preliminary results are produced using individual and cumulative acquisitions from multiple sites in North America and Africa for polarisations HH and HV. The model has been tested on binned and unbinned data. We see a decrease in the RMSE for binned data when cumulative acquisitions are used. It is observed that the highest RMSE is 8.61±0.05 Mg/Ha, and the lowest is 7.91±0.03 Mg/Ha, for the LENO site with HV polarization. The temporal invariance of D, 𝛂, and γ is also tested over multiple sites. The soil moisture content and seasonality exhibit significant changes in the backscatter values; however, the S parameter should account for this variation, thereby improving model performance.