Satellite Remote Sensing and Hydrological Modeling for Estimating Daily Actual Evapotranspiration in a Semi-Arid Mediterranean Ecosystem

Evapotranspiration (ET) is the process by which water is lost from the Earth's surface through the combined mechanisms of evaporation from surfaces and transpiration from plants. It is an important factor in the soil-plant-atmosphere (SPA) system and plays a key role in the functioning of ecosystems. In semi-arid regions such as the Mediterranean, ET is a major contributor to water loss. An accurate understanding of the spatiotemporal dynamics of ET is crucial for effective water resource management and conservation, particularly in the face of increasing water resource pressure and potential climate change. Remote sensing (RS) can provide long-term data with relatively high spatial and temporal resolution, which can be valuable for sustainable ecosystem management. Surface energy balance (SEB) techniques based on satellite RS data have proven useful for quantifying actual evapotranspiration (ET_{a eb}) at various temporal and spatial scales. However, limitations such as the temporal resolution of satellite data and gaps in image acquisition due to cloud cover can limit the usefulness of RS. This study proposes a model-based approach for constructing daily crop actual evapotranspiration (ET_{c act}) between Landsat 8 acquisition days. The modeling approach aims to simulate the dynamics in the SPA system that occur between two Landsat acquisitions in order to estimate the daily time series of ET_{c act}. The model integrates ET_{a eb} estimates by SEBAL model on Landsat-8 acquisition days, RS-derived vegetative biomass dynamics, field measurements of potential evapotranspiration, and a hydrological modeling approach using the transient flow Richards equation to estimate soil moisture in the root zone. The results show that the proposed approach is well suited for modeling the dynamics in the soil-plant-atmosphere system that occurs between two Landsat acquisitions to estimate the daily time series of ET_{c act}. This approach can provide valuable information for water resource management, drought monitoring, and climate change research, moreover accurate ET_{c act} estimates can make significant contributions to near real time irrigation modeling and scheduling.