An intercomparison of available soil moisture estimates from thermal infrared and passive microwave remote sensing and land surface modeling

Remotely sensed soil moisture studies have mainly focused on retrievals using active and passive microwave (MW) sensors, which provide measurements that are directly related to soil moisture (SM). MW sensors have obvious advantages such as the ability to retrieve through nonprecipitating cloud cover which provides shorter repeat cycles. However, MW sensors offer coarse spatial resolution and suffer from reduced retrieval skill over moderate to dense vegetation. A unique avenue for filling these information gaps is to exploit the retrieval of SM from thermal infrared (TIR) observations, which can provide SM information under vegetation cover and at significantly higher resolutions than MW. Previously, an intercomparison of TIR-based and MW-based SM has not been investigated in the literature. Here a series of analyses are proposed to study relationships between SM products during a multiyear period (2003-2008) from a passive MW retrieval (AMSR-E), a TIR based model (ALEXI), and a land surface model (Noah) over the continental United States. The three analyses used in this study include (1) a spatial anomaly correlation analysis, (2) a temporal correlation analysis, and (3) a triple collocation error estimation technique. In general, the intercomparison shows that the TIR and MW methods provide complementary information about the current SM state. TIR can provide SM information over moderate to dense vegetation, a large information gap in current MW methods, while serving as an additional independent source of SM information over low to moderate vegetation. The complementary nature of SM information from MW and TIR sensors implies a potential for integration within an advanced SM data assimilation system.