Soil water content estimation using a remote sensing based hybrid evapotranspiration modeling approach

Remote sensing of evapotranspiration (ET) has evolved over the last 20 years with the development of more robust energy balance approaches and the availability of timely remotely sensed imagery from satellite sensors. This has allowed the use of remote sensing for near-real time water management in irrigated systems in the western United States. In this paper a hybrid ET approach is applied to irrigated and non-irrigated cotton fields at the BEAREX08 experimental site using airborne remote sensing inputs under highly advective conditions, taking advantage of the available root zone soil water content measurements for verification of model output. The modeling approach is based on coupling the Two-Source-Energy Balance (TSEB) and the reflectance-based crop coefficient models. The TSEB model provides estimates of real crop ET while the reflectance-based crop coefficient approach allows for updating the basal crop coefficient and the interpolation and extrapolation of ET between the dates of remote sensing inputs facilitating the maintenance of a soil water balance in the root zone of the crop. Actual ET estimates using the TSEB model were compared with measured ET using eddy covariance systems deployed in four cotton fields during the BEAREX08 experiment. Estimates of soil water content in the soil profile of both irrigated and rain fed cotton fields were compared with measurements at different depths using neutron probe observations. Data assimilation techniques were applied to update soil water content values using estimates based on actual ET from the TSEB model. Results indicate that the hybrid ET modeling approach using data assimilation produced reliable daily ET interpolated between remote sensing observations and significantly improved soil water content estimates throughout the root zone profile compared to applying the crop-coefficient technique in a water balance model without the actual ET inputs.