The Role of Time-Series L-Band SAR and GEDI in Mapping Sub-Tropical Above-Ground Biomass - Khati, Unmesh; Lavalle, Marco; Singh, Gulab (Frontiers in Earth Science, 2021)
Physics-based algorithms estimating large-scale forest above-ground biomass (AGB) from SAR data generally use airborne lidar scanning (ALS) or grid of national forest inventory (NFI) to reduce uncertainties in the model calibration. This study assesses the potential of multi-temporal L-band ALOS-2/PALSAR-2 data to improve forest AGB estimation using 3-parameter water cloud model (WCM) trained with field data from relatively small (0.1-ha) plots. The major objective is to assess the impact of the high uncertainties in field inventory data due to relatively smaller plot size and temporal gap between acquisitions and ground truth on the AGB estimation. This study analyzes a time-series of twenty-three ALOS-2 dual-polarized images spanning 5 years acquired under different weather and soil moisture conditions over a sub-tropical forest test site in India. The WCM model is trained and validated on individual acquisitions to retrieve forest AGB. The accuracy of the generated AGB products is quantified using the root mean square error (RMSE). Further, we use a multi-temporal AGB retrieval approach to improve the accuracy of the estimated AGB. Changes in precipitation and soil moisture affect the AGB retrieval accuracy from individual acquisitions, however using multi-temporal data, these effects are mitigated. Using a multi-temporal AGB retrieval strategy, the accuracy improves by 15% (55 Mg/ha RMSE) for all field plots and by 21% (39 Mg/ha RMSE) for forests with AGB less than 100 Mg/ha. The analysis shows that any ten multi-temporal acquisitions spanning five years are sufficient for improving AGB retrieval accuracy over the considered test site. We use allometry from co-located field plots and GEDI L2A height metrics to produce GEDI-derived AGB estimates. Preliminary analysis shows potential of using jointly GEDI-derived AGB and multi-temporal ALOS-2 data for large-scale AGB retrieval, as confirmed by the AGB root mean square deviation (RMSD) between 55 Mg/ha and 85 Mg/ha.