Developing a dual assimilation approach for thermal infrared and passive microwave soil moisture retrievals [INVITED]
TIR and PM retrievals of soil moisture are compared to soil moisture estimates provided by a retrospective Land Information System (LIS) simulation using the NOAH LSM during the time period of 2003 - 2009. The TIR-based soil moisture product is provided by a retrieval of soil moisture associated with surface flux estimates from the Atmosphere-Land-Exchange-Inversion (ALEXI) model (Anderson et al., 1997; Mecikalski et al., 1999; Hain et al., 2009). The PM soil moisture retrieval is provided by the Vrijie Universiteit Amsterdam(VUA)-NASA surface soil moisture product. The VUA retrieval is based on the findings of Owe et al. (2001; 2008) using the Land Surface Parameter model (LPRM), which uses one dual polarized channel (6.925 or 10.65 GHz) for a dual-retrieval of surface soil moisture and vegetation water content.
In addition, retrievals of ALEXI (TIR) and AMSR-E (PM) soil moisture are assimilated with the LIS and the NOAH LSM. A series of data assimilation experiments are completed with the following configuration, (a) no assimilation, (b) only ALEXI soil moisture, (c) only AMSR-E soil moisture, and (d) ALEXI and AMSR-E soil moisture. The relative skill of each assimilation configuration is quantified through a data-denial experimental design, where the LSM is forced with an inferior precipitation dataset (in this case, the TRMM 3B42RT precipitation dataset). The ability of each assimilation configuration to correct for precipitation errors is quantified through the comparison of the results with a single simulation over the same domain with a high-quality (NLDAS) precipitation dataset. Additionally, future applications of ALEXI surface flux estimates and soil moisture will be addressed.