Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 6/10/2010
Publication Date: 7/19/2010
Citation: Kustas, W.P., Anderson, M.C. 2010. Application of thermal remote sensing for multi-scale estimation of evapotranspiration and drought [abstract]. The CUASHI 2nd Biennial Colloquim. 2010 CDROM. Interpretive Summary:
Technical Abstract: Thermal infrared (TIR) remote sensing of land-surface temperature (LST) provides valuable information about the sub-surface moisture status affecting evapotranspiration 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 (radiation, advection, air temperature) are affecting plant stress. 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. This approach, the Atmosphere-Land Exchange Inverse (ALEXI) model, couples a two-source (soil + canopy) land-surface model with an atmospheric boundary layer model in time-differencing mode to routinely and robustly map fluxes across the U.S. continent at 5-10km resolution using thermal band imagery from the Geostationary Operational Environmental Satellites (GOES). The moisture stress is quantified in terms of the reduction of evapotranspiration (ET) from the potential rate (PET) expected under non-moisture limiting conditions. A derived Evaporative Stress Index (ESI), given by 1-ET/PET, shows good correspondence with standard drought metrics and with patterns of antecedent precipitation, but at significantly higher spatial resolution due to limited reliance on ground observations. Higher resolution drought and ET assessments can be generated through spatial disaggregation scheme (DisALEXI) using TIR data from polar orbiting instruments such as Landsat (60-120m) and MODIS (1km). The ALEXI algorithm is diagnostic and does not require precipitation or soil texture information, unlike most other ET and drought models. In addition, DisALEXI scheme permits validation of the ET product using tower-based flux observations using higher resolution TIR data as well as means of scaling up to watershed and regional scales to compare with aircraft-based fluxes and output from operational models. Work is underway to further evaluate multi-scale ALEXI/DisALEXI implementations over the U.S. and other continents with geostationary satellite coverage.