|Kustas, William - Bill|
|GAO, FENG - National Aeronautics And Space Administration (NASA)|
|SUMMER, DAVID - Us Geological Survey (USGS)|
Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 9/1/2010
Publication Date: 9/27/2010
Citation: Anderson, M.C., Kustas, W.P., Dulaney, W.P., Gao, F., Summer, D. 2010. Integration of multi-scale thermal satellite imagery for evaluation of daily evapotranspiration at the sub-field scale [abstract]. Remote Sensing and Hydrology 2010 Symposium. p. 62.
Technical Abstract: Development of robust algorithms for routine monitoring of evapotranspiration (ET) over large areas at spatial resolutions that discriminate individual agricultural fields (<100 m resolution) and small hydrologic features will benefit an array of water resource management applications. Land-surface temperature (LST) derived from thermal infrared (TIR) remote sensing has proven to be a valuable input to surface energy balance algorithms for estimating ET and serves as an effective proxy for spatially distributed surface moisture and precipitation measurements. Routine monitoring with the current suite of TIR sensors provides broad coverage of a range of spatiotemporal sampling scales. Geostationary satellites such as GOES provide 15 minute LST fields at 5-10 km spatial resolution, polar orbiting systems such as MODIS generate TIR data at 1km resolution every one-two days, while the Landsat TIR systems can produce relatively high resolution (60-120 m) maps every 8-16 days depending on the number of satellites in orbit at a given time. The MODIS and Landsat sensors sample shortwave bands that allow for vegetation cover and amount to be mapped at 250 m and 30 m resolution, respectively. This paper will discuss a strategy for integrating GOES, MODIS and Landsat TIR and shortwave imagery to map daily ET at 30 m resolution. The methodology is based on the continental-scale Atmosphere-Land Exchange Inverse (ALEXI) surface energy balance algorithm, which maps hourly ET using GOES data. Associated flux disaggregation techniques (DisALEXI) downscales ALEXI fluxes using MODIS and Landsat TIR imagery, which can be improved to the spatial resolution of the shortwave bands using thermal sharpening. Finally, a new spatial data fusion algorithm, STARFM, is used to merge the MODIS and Landsat-scale ET evaluations, generating daily predicted fields at the Landsat scale. In this way, we make full use of all available TIR data in interpolating surface moisture conditions between infrequent Landsat overpasses. The methodology has been tested over sites in southern Florida, which represents an area where high-resolution time-continuous ET data are urgently needed for decision making by water management agencies.