Southern African savanna ecosystems and their woody resources are under pressure. Governments in the region need locally calibrated, cost effective, and regularly updated information on these resources in order to satisfy both national and international commitments to manage them. Using LiDAR data as a calibration dataset, this paper sets out to investigate the potential of hypertemporal C-band ASAR SAR data in mapping woody structural related parameters in a savanna environment. Images spanning three years where grouped by years (2007-2009), season (Wet or Dry) and polarization (HH or VV), and relationships were sought for the woody parameter total canopy cover (TCC). Results show that: Dry season combinations of images outperformed wet season images; HH co-polarised images outperformed VV images; temporally filtered images showed marked improvement on unfiltered images. While non-parametric random forest models achieved better validation accuracies than other models did. The single best result was achieved by combining all the temporally filtered images, from all of the various scenarios (R2=0.74; RMSE=8.52; SEP=35.27). The results show promise in delivering regional scale, locally calibrated, baseline products for the management of Southern Africa's woody resources.