Variation in Scintillometer and Eddy Tower Footprints and Implications for the Validation of Satellite Derived Energy Fluxes in Heterogeneous Terrain
Abstract
The large aperture scintillometer (LAS) has emerged as one of the best tools for quantifying areal averaged fluxes over heterogeneous land surfaces. This is particularly useful as a validation of surface energy fluxes derived from satellite sources. We examine how changes in surface source area contributing to the scintillometer and eddy covariance measurements relates to satellite derived estimates of sensible and latent heat flux. Field data was collected on the Konza Prairie in Northeastern Kansas, includes data from two eddy covariance towers: one located on an upland, relatively flat homogeneous area, and the second located in a lowland area with generally higher biomass and moisture conditions. The large aperture scintillometer spans both the upland and lowland areas and operates with a path length of approximately 1 km specifically to compare to MODIS derived estimates of surface fluxes. Data from the MODIS sensor was used on a daily basis to compute fluxes using the 'triangle method' which combines the remotely sensed data with a soil-vegetation-atmosphere-transfer scheme and a fully developed atmospheric boundary layer model. As wind direction varies, the relative contribution of upland and lowland sources contributing to the LAS measurements varies while the MODIS pixel contribution remains relatively constant. Via examining relative contributions of upland and lowland areas to the total LAS measured fluxes we are able to evaluate the relationship between the LAS observations and the remotely sensed estimates of the surface energy balance. General implications for validation of remotely sensed data in heterogeneous terrain will also be discussed.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2007
- Bibcode:
- 2007AGUFM.H21H0831B
- Keywords:
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- 1640 Remote sensing (1855);
- 1814 Energy budgets;
- 1843 Land/atmosphere interactions (1218;
- 1631;
- 3322);
- 1855 Remote sensing (1640)