The Enhanced SMAP Global Simulation Testbed (GloSim2)

NASA's Soil Moisture Active Passive (SMAP) mission, planned for launch in late 2014, has the objective of frequent, global mapping of near-surface soil moisture and its freeze-thaw state. SMAP measurements would enable significantly improved estimates of water, energy and carbon fluxes at the land surface. SMAP measurements are important for weather and climate prediction, flood and drought monitoring, and agricultural yield forecasting, among other applications. The SMAP measurement concept utilizes an L-band radar and radiometer sharing a rotating 6-meter mesh reflector antenna. The radar and radiometer instruments are planned to operate in a 680 km polar orbit, viewing the surface at a constant 40-degree incidence angle with a 1000-km swath width, providing 3-day global coverage. Data from the instruments would yield global maps of soil moisture and freeze/thaw state at 10 km and 3 km resolutions respectively, every two to three days. The algorithms and data products for SMAP are being developed in the SMAP Science Data System (SDS) Testbed. In the Testbed algorithms are developed and evaluated using simulated SMAP observations as well as observational data from current airborne and spaceborne L-band sensors. In this presentation we report on the development status of the SMAP data product algorithms using the SDS Testbed simulation environment. The simulations are based on a land surface model (LSM) and available ancillary data. A forward microwave model is used to translate the simulated geophysical fields and ancillary data to computed L-band brightness temperature (Tb) and backscatter cross-section (σ^o) fields. These fields are then fed into an orbit simulation which applies sampling to the geophysical fields and simulates the generation of SMAP Level 1 products. These products are subsequently fed into retrieval processing algorithms to generate higher-level SMAP data products. The Testbed is designed to capture various sources of errors in the products including environmental effects, instrument effects (non-ideal aspects of the measurement system), and retrieval algorithm errors.