Bayesian Integration of Multiple Satellite Observations and Model Simulations to Develop a Long-term High-resolution Groundwater Storage Data Record

We develop an algorithm for integrating multiple satellite data products that make indirect observations of groundwater. Specifically, this work fuses high-resolution observations of land subsidence obtained from Interferometric Synthetic Aperture Radar (InSAR) using satellites such as the Sentinel-1 and the Advanced Land Observing Satellite (ALOS), with lower resolution estimates of groundwater storage derived from the Gravity Recovery and Climate Experiment (GRACE/GRACE-FO) pair of satellites. In this study, we develop a long-term high-resolution data product of groundwater storage for the Central Valley in California. The algorithm is tuned and calibrated with in-situ well observations from the United States Geological Survey (USGS) and the California Department of Water Resources (DWR), where and when available. We anticipate the algorithm and the application developed from this study can be expanded to other regions with in-situ data on groundwater, since satellite observations being used are global in nature.