During the 1997 Southern Great Plains Hydrology Experiment (SGP97), passive microwave observations using the L-band electronically scanned thinned array radiometer (ESTAR) were used to extend surface soil moisture retrieval algorithms to coarser resolutions and larger regions with more diverse conditions. This near-surface soil moisture product (W) at 800 m pixel resolution together with land use and fractional vegetation cover (f(c)) estimated from normalized difference vegetation index (NDVI) was used for computing spatially distributed sensible (H) and latent (LE) heat fluxes over the SGP97 domain (an area similar to 40 x 260 km) using a remote sensing model (called the two-source energy Balance-soil moisture, TSEBSM, model). With regional maps of W and the heat fluxes, spatial correlations were computed to evaluate the influence of W on H and LE. For the whole SGP97 domain and full range in f(c), correlations (R) between W and LE varied from 0.4 to 0.6 (R similar to 0.5 on average), while correlations between W and H varied from -0.3 to -0.7 (R similar to -0.6 on average). The W-LE and W-H correlations were dramatically higher when variability due to f(c) was considered by using NDVI as a surrogate for f, and computing R between heat fluxes and corresponding W values under similar fractional vegetation cover conditions. The results showed a steady decline in correlation with increasing NDVI or f(c). Typically, /R/ greater than or similar to 0.9 for data sorted by NDVI having values less than or similar to 0.5 or f(c) less than or similar to 0.5, while /R/ less than or similar to 0.5 for the data sorted under high canopy cover where NDVI greater than or similar to 0.6 or f(c) less than or similar to 0.7. The correlations also varied with environmental/moisture conditions, but even more significantly depending on the area selected within the SGP97 domain. Output of the spatially distributed heat fluxes over the SGP97 domain for one day were compared to the atmosphere-land exchange inversion (ALEXI) model, which uses the 5-km resolution geostationary operational environmental satellite (GOES) surface temperature data (T-surf) and 10-km scale atmospheric forcing. The spatial variability in heat fluxes predicted by TSEBSM was significantly greater than ALEXI, but the domain average H and LE differed by less than 5% and 15%, respectively. High resolution (similar to 10 m pixel) remotely sensed T-surf from an airborne sensor collected over one of the main study sites were aggregated to 800-m resolution and compared to the TSEBSM simulated values as a function of remotely sensed f(c). There was good agreement between the model-derived and remotely sensed T-surf with both indicating a linear relationship with f(c). The ability of the TSEBSM model to simulate T-surf values in reasonable agreement with remotely sensed observations suggests the model is capable of properly partitioning the convective-energy and radiative balance of the surface between soil and vegetation components. The spatial correlation results have implications regarding the influence of near-surface soil moisture on atmospheric dynamics and the scale at which these relationships are considered.
