Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: August 5, 2010
Publication Date: August 5, 2010
Citation: Kustas, W.P., Anderson, M.C. 2010. Application of thermal remote sensing for multi-scale monitoring of evapotranspiration [abstract]. Workshop on Engineered Barrier Performance Related to Low-Level Radioactive Waste, Decommissioning, and Uranium Mill Tailings Facilities. NRC Workshop Publication p. 122-123. Technical Abstract: Thermal infrared (TIR) remote sensing of land-surface temperature (LST) provides valuable information about the sub-surface moisture status affecting evapotranspiration (ET) (Anderson and Kustas, 2008). There exists a plethora of techniques exploiting LST and vegetation amount (e.g., as quantified by vegetation indices such as the Normalized Difference Vegetation Index; NDVI), many of which are empirical or are based loosely on conceptual/physical processes simulated by more complex soil-vegetation-atmosphere-transfer (SVAT) schemes (Kalma, 2008). However, often such remote sensing-based methods provide ambiguous results since other factors, related to meteorological (radiation, advection, air temperature) and landscape conditions (land use, roughness, fractional cover) are affecting plant water use as well as the relationship between soil and canopy component temperatures on the composite LST (Kustas and Anderson, 2009). A more physically-based interpretation of LST and NDVI and their relationship to surface and sub-surface moisture conditions can be obtained with a simplified SVAT driven by TIR remote sensing. This approach, the Atmosphere-Land Exchange Inverse (ALEXI) model (Anderson et al., 2007a), couples a two-source (soil + canopy) land-surface model with an atmospheric boundary layer model in time-differencing mode to routinely and robustly map soil and vegetation fluxes across the U.S. continent at 5-10 km resolution using thermal band imagery from the Geostationary Operational Environmental Satellites (GOES). The soil moisture condition 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 anomalies in 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 (Anderson et al., 2007bc). Higher resolution ET assessments can be generated through spatial disaggregation scheme called DisALEXI (Norman et al., 2003; Anderson et al, 2007a) using TIR data from polar orbiting instruments such as Landsat (60-120m) and MODIS (1km). This allows monitoring of ET at the subwatershed and field scales, and moreover permits validation of the ET product using tower-based flux observations. The ALEXI algorithm is diagnostic and does not require precipitation or soil texture information, unlike most other physically-based ET models. This is a significant advantage for monitoring many areas where such information is not locally available, particularly precipitation which must be extrapolated from a sparse observation network. It has also prompted the development of assimilation schemes using ALEXI model output of ET and a soil moisture proxy to constrain water balance-SVAT model predictions (Crow et al., 2008; Hain et al., 2009).