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United States Department of Agriculture

Agricultural Research Service

Title: A Multi-Scale Remote Sensing Model for Disaggregating Regional Fluxes to Micrometeorological Scales

Authors
item Anderson, Martha - UNIVERSITY OF WI
item Norman, John - UNIVERSITY OF WI
item Mecikalski, John - UNIVERSITY OF WI
item Torn, Ryan - UNIVERSITY OF WA
item Kustas, William
item Basara, Jeffrey - OKLAHOMA CLIMATE SURVEY

Submitted to: Journal of Hydrometeorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: December 15, 2003
Publication Date: April 5, 2004
Citation: Anderson, M.C., Norman, J.N., Mecikalski, J.R., Torn, R.D., Kustas, W.P., Basara, J.B. 2004. A multi-scale remote sensing model for disaggregating regional fluxes to micrometeorological scales. Journal of Hydrometeorology. 5:343-363.

Interpretive Summary: A procedure for disaggregating regional evapotranspiration (ET) estimates from an operational remote sensing satellite platform and modeling system to the field scale was evaluated in Oklahoma during 2000-2001 growing season. A technique that sharpens surface temperature to higher pixel resolutions was also incorporated providing ET estimates at the same resolution as remotely sensed vegetation cover. The accuracy and utility of this combined multi-scale modeling system is evaluated quantitatively in comparison with measurements made with ET towers in the Oklahoma Mesonet, and qualitatively, in terms of enhanced information content that emerges at high resolution where flux patterns can be identified with recognizable surface phenomena. Disaggregated ET fields at high resolution were reaggregated over an area representative of the tower ET measurements, and agreement was to within 10%. In contrast, regional ET predictions from the operational system showed a higher relative error of nearly 20% due to the gross mismatch in scale between model and measurement, highlighting the efficacy of disaggregation as a means for validating regional-scale ET predictions over heterogeneous landscapes. Sharpening the surface temperature inputs significantly improved output in terms of visual information content and model convergence rate.

Technical Abstract:

Last Modified: 7/31/2014
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