The goal of SBL-S1-PR is to make high-resolution profile data of space-borne LiDAR (SBL) useful for Alpine forest management. Sloped terrain limits the achievable accuracy of SBL derived canopy heights. Therefore, SBL-S1-PR will explore corrections within SBL processing (Gaussian waveform decomposition, etc.) based on terrain height from detailed ALS DTMs. To derive spatially continuous canopy height maps with high temporal resolution, SBL will be used to calibrate machine learning based forest canopy models based on Sentinel-1 time series data including different polarizations, temporal decorrelation, and forest phenology information. Demonstrating the feasibility of such a novel combined approach will promote the exploitation of Sentinel-1 and SBL data in an operational context in a challenging environment.