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United States Department of Agriculture

Agricultural Research Service

Research Project: USING REMOTE SENSING & MODELING FOR EVALUATING HYDROLOGIC FLUXES, STATES, & CONSTITUENT TRANSPORT PROCESSES WITHIN AGRICULTURAL LANDSCAPES Title: Correcting for Atmospheric Spatial Variability When Estimating Surface Fluxes from Remotely Sensed Land Surface Data

item Albertson, John - DUKE UNIVERSITY
item Bertoldi, Giacomo - INST FOR ALPINE ENVIRON
item Kustas, William

Submitted to: American Geophysical Union
Publication Type: Abstract Only
Publication Acceptance Date: March 24, 2008
Publication Date: March 30, 2008
Citation: Albertson, J.D., Bertoldi, G., Kustas, W.P. 2008. Correcting for atmospheric spatial variability when estimating surface fluxes from remotely sensed land surface data [abstract]. EOS Transactions, American Geophysical Union, 89(23):H43D-06.

Technical Abstract: Efforts to monitor the terrestrial water cycle require accurate estimates of evapotranspiration over the global land area. Flux towers provide valuable site-level data, but their collective footprints cover only a very small fraction of the land surface. Satellite remote sensing instruments, on the other hand, cover the entire globe, but provide at best a view of the land surface states (i.e. not the fluxes). Algorithms that provide spatially distributed estimates of land surface fluxes from the land surface state data require information about the states of the lower atmosphere. However, the lower atmosphere states are typically taken from regional meteorological stations and consequently do not possess the same level of spatial detail as the remote sensing data. In this talk we examine Large Eddy Simulation (LES) results over remotely sensed land surface fields to demonstrate the errors injected from the use of regional scale atmospheric data. We also present an analysis of the effect of the length scales of the land surface heterogeneity on the magnitude of these errors. And, finally, we present a simple means to downscale the regional atmospheric data for more accurate use with the remotely sensed land surface state data to compute land surface fluxes.

Last Modified: 11/30/2015
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