|Norman, John - UNIVERSITY OF WISCONSIN|
|Mecikalski, John - UNIV. OF AL-HUNTSVILLE|
Submitted to: Journal of Hydrometeorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: December 1, 2005
Publication Date: December 31, 2005
Citation: Anderson, M.C., Norman, J.M., Kustas, W.P., Li, F., Prueger, J.H., Mecikalski, J.R. 2005. Effects of vegetation clumping on two-source model estimates of surface energy fluxes from an agricultural landscape during SMACEX. Journal of Hydrometeorology. 6:892-909. Interpretive Summary: Thermal infrared remote sensing imagery provides valuable information for mapping evapotranspiration (ET) over a range in spatial scales, from field to continent to global coverage. The key input that can be derived from thermal imagery is land-surface temperature, which provides valuable constraints on several components of the surface energy budget including ET. However, the interpretation of the surface temperature of a given patch of land in terms of local moisture conditions depends on the fractional area of that patch covered by vegetation vs. bare soil, and how strongly the vegetation is clumped within the patch. This is a subtlety that is disregarded by most thermal remote sensing models of ET, leading to possible biases in flux predictions. Here we assess the effects of vegetation clumping on remotely sensed estimates of ET, and suggest a simple means for accommodating clumping effects in thermal-based ET models. When clumping is considered, the model provides excellent estimates of surface energy fluxes over an agricultural landscape in central Iowa, where clumping is prevalent at both the row and field scales.
Technical Abstract: The effects of non-random leaf area distributions on surface flux predictions from a two-source, thermal remote sensing model are investigated. The modeling framework is applied at local and regional scales over the Soil-Moisture-Atmospheric-Coupling Experiment (SMACEX) study area in central Iowa, an agricultural landscape that exhibits foliage organization at a variety of levels. Row-scale clumping in area corn and soybean fields is quantified as a function of view zenith and azimuth angle using ground-based measurements of canopy architecture. The derived clumping indices are used to represent subpixel clumping in Landsat cover estimates at 30-m resolution, which are then aggregated to the 5-km scale of the regional model, reflecting field-to-field variations in vegetation amount. Consideration of vegetation clumping within the thermal model, which affects the relationship between surface temperature and leaf area inputs, significantly improves model estimates of sensible heating at both local and watershed scales in comparison with eddy-covariance data collected by aircraft and with a ground-based tower network. These results suggest that this economical approach to representing subpixel leaf area hetereogeneity at multiple scales within the two-source modeling framework works well over the agricultural landscape studied here.