Assimilation of SMOS Observations for science development of the SMAP Level 4 Surface and Root-Zone Soil Moisture Product

The Soil Moisture Active and Passive (SMAP) mission is being implemented by NASA for launch in October 2014. The SMAP mission makes simultaneous active (radar) and passive (radiometer) measurements in the 1.26-1.43 GHz range (L-band). The primary science objectives of SMAP are to enhance understanding of land surface controls on the water, energy and carbon cycles, and to determine their linkages. Moreover, the high-resolution soil moisture mapping provided by SMAP has practical applications in weather and seasonal climate prediction, agriculture, human health, drought and flood decision support. The Soil Moisture and Ocean Salinity (SMOS) mission was launched by ESA in November 2009 and has since been observing L-band upwelling passive microwaves. In this presentation we describe our use of SMOS brightness temperature observations for science development of the planned SMAP Level 4 Surface and Root-zone Soil Moisture (L4_SM) product. SMAP and SMOS directly observe only surface soil moisture (in the top 5 cm of the soil column). Several of the key applications targeted by SMAP, however, require knowledge of root zone soil moisture (~top 1 m of the soil column), which is not directly measured by SMAP. The foremost objective of the SMAP L4_SM product is to fill this gap and provide estimates of root zone soil moisture that are informed by and consistent with SMAP observations. Such estimates are obtained by merging SMAP observations with estimates from a land surface model in a soil moisture data assimilation system. The L4_SM soil moisture analysis, which merges land model forecast soil moisture and soil temperature (at 9 km resolution) with horizontally and vertically polarized L-band brightness temperature observations (at 9 km and 36 km resolution, see below). The land surface model component of the assimilation system, the NASA Catchment land surface model, is driven with observations-based surface meteorological forcing data, including precipitation, which is the most important driver for soil moisture. The model also encapsulates knowledge of key land surface processes, including the vertical transfer of soil moisture between the surface and root zone reservoirs. For the SMAP L4_SM data product, downscaled TBH and TBV observations at 9 km resolution from the SMAP Level 2 Soil Moisture Active Passive (L2_SM_AP) product will be used whenever available. These downscaled brightness temperatures are a merger of the 36 km SMAP Level 1C Brightness Temperature (L1C_TB) product and SMAP high-resolution radar observations, which are only available for the morning overpasses. For evening overpasses, the SMAP L4_SM product will assimilate SMAP L1C_TB brightness temperatures (at 36 km resolution). In the prototype L4_SM system discussed here, SMOS brightness temperature observations are regridded to the 36 km SMAP grid and assimilated. Residual biases between modeled and observed brightness temperatures are addressed through the use of Z-scores (standard-normal deviates). The horizontally distributed ensemble Kalman filter (“3d EnKF”) update step considers the respective uncertainties of the model estimates and the observations, resulting in a soil moisture and soil temperature analysis at 9 km resolution that is superior to satellite or model estimates alone. Moreover, error estimates for the L4_SM product are generated as a by-product of the data assimilation system. In addition to the soil moisture analysis, the L4_SM product also includes a freeze-thaw analysis. The L4_SM product thus provides a comprehensive and consistent picture of land surface hydrological conditions based on satellite observations and complementary information from a variety of sources. The presentation will focus on the use of SMOS brightness temperature observations for the generation of a prototype SMAP L4_SM product. The quality of the prototype product is assessed against in situ observations from operational watershed sites and sparse networks, including USDA/SCAN stations and stations in the Murrumbidgee basin in Australia. The assessment of the prototype product provides important information on the accuracy of the SMAP L4_SM product relative to its RMSE requirement of 0.04 m3 m-3.