干旱区生态环境知识资源中心

Accurate information on the distribution of sensible and latent heat fluxes is critical for evaluation of background characteristics. Since these fluxes are subject to rapid changes in time and space, it is nearly impossible to. determine their spatial and temporal distributions over large areas from ground measurements alone. Therefore, prediction from remote sensing data is very attractive as it enables large area coverage and a high repetition rate. In this study, the Surface Energy Balance Algorithm for Land (SEBAL) is selected to estimate sensible and latent heat fluxes at 30 m resolution in the riparian areas of the Middle Rio Grande Basin (New Mexico), San Pedro River (Arizona) and Owens Valley (California). The objective is to compare SEBAL fluxes derived from LandSat TM images with those measured on the ground with eddy covariance towers. The comparison in and heterogeneous riparian areas in the southwestern United States clearly demonstrates that SEBAL can be applied for mapping sensible and latent heat fluxes at high spatial resolutions.