What is the Optimal Mixture of Space-borne Sensors for Remote Sensing of Terrestrial Freshwater?: A Comparative Analysis of Passive Optical, Passive Microwave, Active Microwave, and LiDAR Retrievals

An observing system simulation experiment (OSSE) is used to evaluate different observing strategies of terrestrial freshwater and to help create a mission planning tool with a particular focus on the coupled snow-soil moisture-vegetation system at the land surface. The NASA Land Information System (LIS) is used to model the spatiotemporal dynamics of the hydrologic cycle. LIS is then linked to the Trade-space Analysis Tool for Constellations (TAT-C) in order to provide a realistic view of land surface conditions as seen by a given space-borne sensor that is then used to generate synthetic, space-borne retrievals of snow, soil moisture, and vegetation. The merger of LIS with TAT-C enables the simulation of different mixtures of space-borne sensors designed to explore the trade-off in scientific utility related to passive microwave radiometery, microwave RADAR, optical radiometery, and LiDAR. An ensemble-based data assimilation framework is employed to systematically merge (in a Bayesian sense) the land surface model with the space-borne retrievals of snow, soil moisture, and/or vegetation. The results from each OSSE enable systematic comparisons across a wide range of different observing system strategies and can help program managers decide what is the optimal mixture of sensor types, orbital configurations, senor resolutions, instrument errors, feasible cost options, and risk assessments in order to achieve a predefined scientific benchmark. The end result of this project will be a framework to help decide how to harness the information content of Earth science mission data in order to best characterize the spatiotemporal dynamics of freshwater in the natural environment.