Due to their ability to observe the land surface irrespective of weather and lightning conditions, radar satellite constellations are indispensable for monitoring of highly dynamic land surface processes. While in the past only scatterometer missions allowed consistent monitoring at global scale, albeit at very coarse spatial scales, this has changed fundamentally with the Copernicus Sentinel-1 mission that stands out as one of the most successful Synthetic Aperture Radar (SAR) missions. With its novel combination of high spatial and temporal resolution, long-term mission planning, and open data policy it has served as a role model for the conceptualization of future radar missions. With the upcoming launches of the Japanese Advanced Land Observing Satellite-4 (ALOS-4) satellite, the NASA-ISRO SAR Mission (NISAR), ESA’s Biomass mission, and the Copernicus Radar Observing System for Europe in L-band (ROSE-L) satellites, there is now the opportunity to monitor dynamic processes at high spatial resolution (10-20m) with short revisit times (1-3 days) at multiple frequencies (C-, L-, and P-band). In this presentation I will discuss a collaborative effort of the Vienna University of Technology (TU Wien) and the EODC Earth Observation Data Centre to build a global multi-frequency SAR datacube suited for applying hybrid algorithms combining physical models and machine learning. Furthermore, I will show examples of how we use this tailored datacube for the monitoring of soil moisture, floods, vegetation, and soil structural characteristics.