Kim, J, Guo, Q, Baldocchi, DD, Leclerc, M, Xu, L, Schmid, HP (2006). Upscaling fluxes from tower to landscape: Overlaying flux footprints on high-resolution (IKONOS) images of vegetation cover. AGRICULTURAL AND FOREST METEOROLOGY, 136(4-Mar), 132-146.

In this paper, we describe the process of assessing tower footprint climatology, spatial variability of site vegetation density based on satellite image analysis, and sensor location bias in scaling up to 1 km x 1 km patch. Three flat sites with different vegetation cover and surface heterogeneity were selected from AmeriFlux tower sites: the oak/grass site and the annual grassland site in a savannah ecosystem in northern California and a slash pine forest site in Florida, USA. The site vegetation density was expressed in terms of normalized difference vegetation index (NDVI) and crown closure (CC) by analyzing the high-resolution IKONOS satellite image. At each site, the spatial structure of vegetation density was characterized using semivariogram and window size analyses. Footprint maps were produced by a simple model based on the analytical solution of the Eulerian advection-diffusion equation. The resulting horizontal arrays of footprint functions were then superimposed with those of NDVI and CC. Annual sensor location biases for the oak/grass and the pine forest sites were < 4% for both NDVI and CC, requiring no flux corrections in scaling from tower to landscape of 1 km(2). Although the annual grassland site displayed much larger location biases (28% for NDVI, 94% for CC), their temporal changes associated with averaging time showed a real potential to develop algorithms aimed at upscaling tower fluxes to the landscape in an effort to provide validation data for MODIS products. (c) 2005 Elsevier B.V. All rights reserved.