Conference Agenda

NASA/JPL’s Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) has been deployed to collect multi-baseline interferometric SAR observations across a diverse range of forest biomes, including tropical forests in Africa and Central America, temperate forests in California and Maine, and boreal forests in Alaska and Saskatchewan, Canada. For each site, the multi-baseline data acquisition was designed and optimized based on expected canopy height, radar frequency (L- or P-band), and the number of interferometric tracks achievable within a single flight mission. The acquired multi-baseline data are processed, polarimetrically calibrated, and co-registered into stacks of single-look complex (SLC) images. To remove residual phase screens among SLCs, we apply the phase center double localization (PCDL) method for inter-track phase calibration. The resulting calibrated SLC stacks serve as input for subsequent polarimetric interferometric SAR (PolInSAR) and tomographic SAR (TomoSAR) processing. PolInSAR processing is performed using Kapok, an open-source software developed at JPL, to compute the full PolInSAR covariance matrix from the calibrated SLC stack. For TomoSAR processing, we use the Capon beamforming algorithm to reconstruct 3-D radar backscatter voxels (tomographic cubes). To retrieve canopy height and ground elevation beneath vegetation, we developed a SAR-Lidar data fusion workflow capable of ingesting a variety of PolInSAR and/or TomoSAR input, or a combination of both. In this presentation, we summarize the data processing and machine learning framework used to generate PolInSAR/TomoSAR-based canopy height and bare surface topography retrievals, and evaluate the performance and generalizability of data fusion models trained with different number of baselines, radar frequencies, and forest types.