Thermal infrared (TIR) remote sensing of land-surface temperature (LST) provides valuable information about the sub-surface moisture status required for estimating evapotranspiration (ET) and detecting the onset and severity of drought. While empirical indices measuring anomalies in LST and vegetation amount (e.g., as quantified by the Normalized Difference Vegetation Index; NDVI) have demonstrated utility in monitoring ET and drought conditions over large areas, they may provide ambiguous results when other factors (e.g., air temperature, advection) are affecting plant functioning. A more physically based interpretation of LST and NDVI and their relationship to sub-surface moisture conditions can be obtained with a surface energy balance model driven by TIR remote sensing. The Atmosphere-Land Exchange Inverse (ALEXI) model is a multi-sensor TIR approach to ET mapping, coupling a two-source (soil + canopy) land-surface model with an atmospheric boundary layer model in time-differencing mode to routinely and robustly map daily fluxes at continental scales and 5 to 10-km resolution using thermal band imagery and insolation estimates from geostationary satellites. A related algorithm (DisALEXI) spatially disaggregates ALEXI fluxes down to finer spatial scales using moderate resolution TIR imagery from polar orbiting satellites. An overview of this modeling approach is presented, along with strategies for fusing information from multiple satellite platforms and wavebands to map daily ET down to resolutions on the order of 10 m. The ALEXI/DisALEXI model has potential for global applications by integrating data from multiple geostationary meteorological satellite systems, such as the US Geostationary Operational Environmental Satellites, the European Meteosat satellites, the Chinese Fen-yung 2B series, and the Japanese Geostationary Meteorological Satellites. Work is underway to further evaluate multi-scale ALEXI implementations over the US, Europe, Africa and other continents with geostationary satellite coverage.

Surface roughness is a crucial input for radar backscatter models. Roughness measurements of root mean-squared height (h_rms) of the same surface can vary depending on the measuring instrument and how the data are processed. This letter addresses the error in h_rms associated with instrument bias and instrument deployment issues such as number and length of measurement transects. It was found that at least 20 transect measurements, 3 m in length, for study sites ranging from 3.5 to 1225 m² in size were necessary to get a consistent h_rms measurement. Also, roughness heights of longer transect lengths were highly dependent on the method of detrending the transects. Finally, soil moisture was predicted by inverting the integral equation model using roughness heights taking into account instrument bias, number of measurements, and the detrending method. For common configurations of the Radarsat sensor and reasonable h_rms values, error associated with measurement of h_rms generally exceeded plusmn20% of soil-moisture prediction