Combining Energy and Soil Water Balances Based on Remote Sensing Inputs for Monitoring Irrigated and Rainfed Agriculture

The notable advances and the robustness of the remote sensing (RS) methodologies for evapotranspiration (ET) assessment have allowed the development of combined modeling approaches to overcome the possible limitations of individual models resulting in synergist combinations.

RS data analysis for ET retrieval include a limited number of operational models including thermal based surface energy balance models (SEB) and vegetation indices (VI) based water balance approaches (RSWB). While thermal remote sensing based energy balance models account for ET reduction due to plant water stress, VI based approaches are a simple way of determine the potential canopy transpiration. This difference can be explored in a synergist manner and the expected results will depend on the previous knowledge and data availability for the analyzed environments. In this research we present three approaches based on the combination of both methodologies designed to use the knowledge about ET, water stress and irrigation requirements in irrigated and rain-fed agriculture.

In the simplest approach, the results of both models in terms of daily ET can be combined to derive the temporal evolution of the water stress factor, defined as the ratio between actual over potential transpiration. Under rain-fed conditions, both models can be combined to estimate the effective root depth which explains the ET and water stress processes at canopy scale. Finally, the most complex approach considers the assimilation of SEB model into RSWB to infer the irrigation applied during the growing season. All three approaches were analyzed in irrigated and rain-fed maize in eastern Nebraska during the 2012 growing season and the results of the different approaches are discussed and compared to field ET measurements using eddy covariance approaches.