Get reliable rangeland science

With increasing interest in airborne and satellite-based sensors for mapping regional and global energy balance, there is a need to determine the uncertainty involved in aggregating remotely-sensed variables [surface temperature (Tk) and reflectance ([pi])] and surface energy fluxes [sensible (H) and latent ([lambda]E) heat flux] over large areas. This uncertainty is directly related to two factors: (1) the nonlinearity of the relation between the sensor signal and Tk, [pi], H or[lambda]E; and (2) the heterogeneity of the site. In this study, we compiled several remotely-sensed data sets acquired at different locations within a semi-arid rangeland in Arizona, at a variety of spatial and temporal resolutions. These data sets provided the range of data heterogeneities necessary for an extensive analysis of data aggregation. The general technique to evaluate uncertainty was to compare remotely-sensed variables and energy balance components calculated in two ways: first, calculated at the pixel resolution and averaged to the coarser resolution; and second, calculated directly at the coarse resolution by aggregating the fine-resolution data to the coarse scale. Results showed that the error in the aggregation of Tk and [pi] was negligible for a wide range of conditions. However, the error in aggregation of H and [lambda]E was highly influenced by the heterogeneity of the site. Errors in H larger than 50% were possible under certain conditions. The conditions associated with the largest aggregation errors in H were: - sites which are composed of a mix of stable and unstable conditions;- sites which have considerable variations in aerodynamic roughness, especially for highly unstable conditions where the difference between surface and air temperature is large; and- sites which are characterized by patch vegetation, where the pixel resolution is less than or nearly-equal to the diameter of the vegetation [`]element' (in most cases, the diameter of the dominant vegetation type or vegetation patch). Thus, knowledge of the surface heterogeneity is essential for minimizing error in aggregation of H and [lambda]E. Two schemes are presented for quantifying surface heterogeneity as a first step in data aggregation. These results emphasized the need for caution in aggregation of energy balance components over heterogeneous landscapes with sparse or mixed vegetation types.