The S&T objectives of the Advanced_SAR project are: 1) to develop advanced Earth Observation methods by combining 3D data derived from various Remote Sensing systems in an novel way and 2) to show their improved performances in forest biomass estimation and biomass change detection with respect to present GMES services. The methodology is based on object-based, multi-date analysis of Sentinel-1 (C-band), TerraSAR/TanDEM-X (X-band), ALOS-2 PALSAR-2 (L-band) SAR data utilizing radargrammetry and Interferometric Synthetic Aperture Radar (InSAR). Understanding of 3D forest responses will be deepened by comparing results to other modern 3D methods: optical satellite stereo-photogrammetry, simulated space-borne LiDAR (laser ranging), and Airborne Laser Scanning (ALS). It will be shown that high-quality estimation and change detection can be done at different scales (thus improving estimation accuracy at national level). We develop methods to derive the best possible cost-efficiency out of the given SAR data with an aim to significantly advance current GMES services. The methodological quality will be verified by comparing the relationship between SAR canopy height estimates with those of two probing systems: TomoRadar (profiling radar) and ALS. ALS gives more information of canopy gaps whereas TomoRadar will give information of canopy penetration at radar frequencies. A physical model is created between the SAR response and the ground truth. Deep physical understanding of where the radar signals originate in the vertical dimension is created for SAR scenes with using ALS and TomoRadar data as a high-quality reference. Moreover, Mobile and Terrestrial Laser Scanning methods for field inventory are tested in real-life scenario. Two super test sites 1) boreal test site Evo (Finland) and 2) hemi-boreal test site Remningstorp (Sweden) are used to verify and demonstrate SAR-based 3D methods. For demonstration purposes, we create SAR-based biomass and change maps covering a large region of Sweden for Swedish National Forest Inventory.
