The estimation and scaling of land-surface fluxes of latent and sensible heat with remotely sensed data over a grassland site.

Abstract

The overall topic of the research described 10 this dissertation was the partitioning of available energy at the Earth's surface into sensible and latent heat flux, with an emphasis on the development of techniques which utilize remotely sensed data. One of the major objectives was; to investigate the modification of existing techniques, developed over agricultural surfaces, to "natural" ecosystems (i.e., non-agricultural vegetation types with variable and incomplete canopy cover). Ground-based measurements of surface fluxes, vegetation cover, and surface and root-zone soil moisture from the First ISLSCP (International Land Surface Climatology Program) Field Experiment (FIFE) were used to examine the factors controlling the partitioning of energy at ground stations with contrasting surface characteristics. Utilizing helicopter-based and satellite-based data acquired directly over ground-based flux stations at the FIFE experimental area, relatively simple algorithms were developed for estimating the soil heat flux and sensible heat flux from remotely sensed data. The root mean square error (RMSE) between the sensible heat flux computed with the remotely sensed data and the sensible heat flux measured at the ground stations was 33 Wm⁻². These algorithms were then applied on a pixel-by-pixel basis to data from a Landsat-TM (Thematic Mapper) scene acquired over the FIFE site on August 15, 1987 to produce spatially distributed surface energy-balance components for the FIFE site. A methodology for quantifying the effect of spatial scaling on parameters derived from remotely sensed data was presented. As an example of the utility of this approach, NDVI values for the FIFE experimental area were computed with input data of variable spatial resolution. The differences in the values of NDVI computed at different spatial resolutions were accurately predicted by an equation which quantified those differences in terms of variability in input observations.