Estimating rootzone soil moisture by assimilating both microwave based surface soil moisture and thermal based soil moisture proxy observations

Remote sensing can be used to retrieve soil moisture through either microwave- or thermal-based satellite observations. Microwave satellite data can be used to retrieve top 5-cm soil moisture (surface soil moisture), whereas thermal observations are indirectly related to root-zone soil moisture through the thermal response of the vegetation canopy to soil water stress. Microwave-derived soil moisture data does not depend on weather conditions and can provide almost daily coverage. However, the resolution is relatively low (>10 km). In contrast, thermal satellite data has high resolution (100 m to 1 km), but low temporal frequency, since retrieval is not possible in the presence of clouds. The assimilation of microwave-derived surface soil moisture into land surface models has been an active area of research for nearly a decade. Likewise, a number of past studies have inputted thermal-based land surface temperature retrievals into a data assimilation system. However, relatively little work has focused on the simultaneous assimilation of both observations into a land surface model. In this paper, a number of synthetic data assimilation experiments are carried out at the USDA Economic and Environmental Enhancement (OPE3) site in Beltsville, Maryland. As a first case, only surface soil moisture retrievals are assimilated into a land surface model using the Ensemble Kalman filter (EnKF). This case is compared to dual EnKF assimilation results where both surface and root-zone soil moisture observations (derived from thermal remote sensing observations) are simultaneously assimilated. The ability of the dual assimilation case to enhance results (relative to the single surface soil moisture assimilation case) is examined for a range of root-zone soil moisture accuracies and frequencies to determine how valuable root-zone moisture retrievals - degraded to realistic levels of frequency and accuracy - are for the accurate constraint of land surface model predictions. Problems associated with the low temporal frequency of thermal remote sensing data, the accurate specification of modeling uncertainties within the EnKF, and the nonlinearity of the land surface model with respect to soil moisture will be discussed. Preliminary results for the assimilation of real observations at the OPE3 site will also be presented.