Coupled Estimation of Evapotranspiration and Recharge from Remotely Sensed Soil Moisture and Land Surface Temperature

Evapotranspiration and recharge are fluxes at the land-atmosphere interface. Evaporative flux links the surface and atmospheric systems and the recharge flux links the surface and subsurface systems. These are two critical fluxes in the water cycle that play a pivotal role in (1) global water, energy and biogeochemical cycles; (2) crop productivity; (3) sustainability of aquifers; (4) ecosystem health; and (5) climate. These water fluxes are intimately relatedto the distribution and functioning of vegetation cover and type and therefore are sensitive to human alteration of the landscape. As a result, these fluxes have already changed dramatically over time and by orders of magnitude.In addition, evapotranspiration and recharge flux amplify changes in precipitation and radiative forcing which makes the future of these two critical fluxes even more uncertain under changing atmospheric composition (climate change). Despite the importance of these fluxes and their historical change, there are no direct measurements – in situ or by remote sensing – that can allow any mapping or any global or regional estimation. Long-term and spatially explicit (mapped) monitoring of evapotranspiration and recharge flux have been elusive goals and a grand challenge for hydrologists.

The goal of this study is to quantify/map the patterns and dynamics of evapotranspiration and recharge flux using land surface state observations that are widely available across a range of spatial and temporal scales, landscapes and climates. In order to achieve this objective, we develop and integrate state-of-the-art computational and data driven techniques to yield first order accurate estimates of key state and parameters (e.g., estimation control variables) of evapotranspiration and recharge flux from implicit information contained in the multi-platform remotely sensed Land Surface state observations of Temperature (LST) and Soil Moisture (SM). The developed approach is based on the variational data assimilation (VDA) scheme that assimilates state observations of LST and SM into a coupled system of surface energy balance and surface water balance. The cost function consists of the LST and SM misfit terms and deviations of parameter estimates from prior values. Parsimonious heat and moisture diffusion equation are adjoined to the cost function as strong constraints. Efficient solution procedures (Euler-Lagrange) are available for such systems. The Hessian of the cost function, which yields a measure of uncertainty in the estimation, will be derived and used in this study to guide the formulation of a well-posed estimation problem. We test the accuracy of this method at point scale using synthetic and field site measurements of turbulent heat fluxes and soil moisture profiles. The method is applied to the U.S. Southern Great Plains region (with computational grid size of 0.05 degree) by assimilating Soil Moisture Active Passive (SMAP) soil moisture data and Geostationary Operational Environmental Satellite (GOES) land surface temperature data into the developed VDA model during summer 2016. Categorical land classifications will be used to test the consistency of the retrieved variables. The retrievals will be verified against tower-flux and soil moisture profile field site measurements, and physiographic characteristics of the region.