Abstract:
Slated for launch in 2014, the NASA Soil Moisture Active/Passive mission represents a significant advance in our ability to globally observe time and space variations in surface soil moisture fields. The SMAP mission concept is based on the integrated use of L-band active radar and passive radiometry measurements to optimize both the accuracy and resolution of remotely-sensed surface soil moisture estimates. Data assimilation activities represent a critical linkage between SMAP products and eventual science and operational applications. In particular, SMAP mission plans call for the generation of a dedicated data assimilation product to vertically extrapolate near-surface (0 to 5-cm) soil moisture retrievals to produce deeper, root-zone (0 to 1-m) soil moisture estimates required by many data end users. To meet this goal, a global Ensemble Kalman filtering (EnKF) land data assimilation system capable of assimilating SMAP data products into a vertically-discretized land surface model is currently under development at the NASA Global Modeling and Data Assimilation Office (GMAO). This system will be used to generate an official Level 4 SMAP Surface and Root-Zone Soil Moisture (L4_SM) product. The use of data assimilation techniques to generate an official NASA earth science mission data product represents an important milestone in the application of land data assimilation to terrestrial remote sensing.

This paper will describe the SMAP L4_SM surface and root-zone soil moisture system in detail and summarize recent Observing System Simulation Experiment (OSSE) results aimed at quantifying the added value of SMAP soil moisture retrievals for global root-zone soil moisture monitoring activities. Existing applications already possess access to soil moisture estimates derived from land surface water balance models forced by observed rainfall, radiative forcings and micro-meteorology variables. Clarifying the added value of assimilating remotely-sensed surface soil moisture retrievals into such systems (relative to this existing baseline) is important for articulating expected SMAP impacts on key applications.