Model-based Retrieval of Forest Parameters from Sentinel-1 Coherence and Backscatter Time-series

This paper describes a model-based algorithm for estimating tree height and other bio-physical land parameters from time-series of synthetic aperture radar interferometric coherence and backscatter supported by sparse lidar data. The random-motion-over-ground model (RMoG) is extended to timeseries and revisited to capture the short- and long-term temporal coherence variability caused by motion of the scatterers and changes in the soil and canopy backscatter. The proposed retrieval algorithm estimates first the spatially slow-varying RMoG model parameters using sparse lidar data, and subsequently the spatially fast-varying model parameters such as tree height. The recently-published global Sentinel-1 interferometric coherence and backscatter data set and sparse spaceborne GEDI lidar data are used to illustrate the algorithm. Results obtained for a small region over Spain show that the temporal coherence and backscatter time-series have the potential to be used for global, model-based land parameter estimation