Linking changes in dynamic cotton canopy to passive microwave remote sensing.

摘要

Soil moisture is one of the most important variables in land-atmosphere processes. It determines how precipitation partitions into infiltration, surface runoff, and groundwater recharge. Additionally, soil moisture is important in partitioning the available energy into the latent and sensible heat fluxes at the land surface. The control of soil moisture is the key mechanism for the feedback mechanisms between land and atmospheric fluxes.;Accurate estimates of these land surface fluxes are essential for understanding and quantifying the global, regional, and local hydrological cycles. Even though the biophysics of moisture and energy transport is captured in most current Soil-Vegetation-Atmosphere-Transfer (SVAT) models that provide estimates of soil moisture, the computational errors accumulate over time and the model estimates diverge from reality. One promising way to significantly improve model estimates of soil moisture is by assimilating remotely sensed data that are sensitive to soil moisture, for example, microwave brightness temperatures, and updating the model state variables.;The microwave brightness at low frequencies is very sensitive to soil moisture in the top few centimeters in most vegetated surfaces. Most of the passive microwave brightness experiments for soil moisture retrieval conducted in agricultural terrains have been short-term experiments that captured only parts of the growing season. Knowledge for the interactions between microwave brightness signatures and changes in soil moisture and temperatures for a dynamic agricultural canopy, such as cotton, is very important during the whole growing season. Microwave brightness (MB) models simulating the terrain emission provide the opportunity to relate microwave signatures to soil moisture information. An integrated SVAT and MB model provides the opportunity to direct assimilate microwave remote sensing observations.;The goal of this dissertation is to develop a MB model that can be used to simulate microwave brightness temperature (TB) for the entire growing season of cotton. This MB model can be linked with existing SVAT models such as the Land Surface Process (LSP) model for the cotton growing season to allow assimilation of passive microwave observations.