USING REMOTE SENSING & MODELING FOR EVALUATING HYDROLOGIC FLUXES, STATES, & CONSTITUENT TRANSPORT PROCESSES WITHIN AGRICULTURAL LANDSCAPES
Title: Mapping Daily Evapotranspiration at Field to Global Scales using Geostationary and Polar Orbiting Satellite Imagery
| Norman, John - |
| Hain, Christopher - |
| Mecikalski, John - |
| Schultz, Lori - |
| Gonzalez-Dugo, M - |
| Cammalleri, Carmello - |
| D'Urso, Guido - |
| Pimstein, Agustin - |
| Gao, Feng - |
Submitted to: Hydrology and Earth System Sciences
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: January 4, 2011
Publication Date: January 21, 2011
Citation: Anderson, M.C., Kustas, W.P., Norman, J., Hain, C., Mecikalski, J., Schultz, L., Gonzalez-Dugo, M.P., Cammalleri, C., D'Urso, G., Pimstein, A., Gao, F. 2011. Mapping daily evapotranspiration at field to global scales using geostationary and polar orbiting satellite imagery. Hydrology and Earth System Sciences. 15:223-239.
We describe a satellite-based methodology for mapping evapotranspiration (water lost to the atmosphere through evaporation and plant transpiration) at scales ranging from individual fields to entire continents using remote sensing imagery collected by multiple satellite platforms. Individual satellites focus on specific spatiotemporal scales, but in combination, scalable maps of water use can be constructed following the example of Google maps. This paper describes this modeling effort, giving examples of applications in drought monitoring and irrigation and water management both in the U.S. and in Europe and Africa. We further explore integration of satellite information in both thermal and microwave wavebands, to provide improved spatial and temporal coverage over a range in cloud and vegetation cover conditions.
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 (soil moisture, 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. 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 5-10 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 of 30 m. The ALEXI/DisALEXI model has potential for global applications by integrating data from multiple geostationary meteorological satellite systems, such as the U.S. 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 U.S., Europe and, Africa and other continents with geostationary satellite coverage.