# Performance of Satellite-Based Evapotranspiration
## 1 Introduction  
The mid-latitude westerly storm tracks in southern South America are shifting poleward with anthropogenic climate change [1], exposing the temperate region to progressively drier summer conditions that are prevalent at lower latitudes under a drier Mediterranean-like climate regime. This ongoing regional drying is part of a much larger scale drying trend across the southeastern Pacific and is expected to continue into the future [2]. The intensification of summer droughts [3; 4; 5; 6], particularly during 2015 and 2016, has produced severe drops in pasture productivity (see Figure 1b) and large losses to the dairy industry. Water management programs for pastures have been implemented for the first time in the region to maintain productivity levels during the critical dry period of the austral summer. These traditionally rainfed pastures cover about 1.32 million hectares in the lowlands [7] and sustain major agricultural and dairy production in the country [8]. Accurate evapotranspirationestimates and timely monitoring of pasture water requirements are necessary for the implementation of efficient irrigation programs, although this information is difficult to obtain at regional scale because of the scarcity of in-situ measurements for evapotranspiration retrievals over this large region. Quantifying evapotranspiration is crucial for determining irrigation needs and the water footprint of pasture landscape systems [9; 10; 11; 12; 13]. A widely used approach to determine crop water consumption for irrigation management is through the estimation of ET\({}_{a}\) from the surface energy balance (SEB) based on high-resolution satellite observations of the land surface and meteorological data [14; 15; 16; 17]. The SEB models can be categorized as one-source, two-layer, two-patch, dual-source, multi-patch, and multi-layer models. One-source models avoid the distinction in the aerodynamic resistance (r\({}_{ah}\)) contribution between soil and vegetation [18]. The two-layer models consider the individual contribution of soil and vegetation to the total heat flux [19]. In contrast, the two-patch models estimate heat fluxes on the soil and vegetation component independently [20]. Hybrid models of dual source originate from the combination of the patch and layer approaches, resulting in a hybrid dual-source model [21]. The multi-patch models consider a greater spatial heterogeneity within each cell by dividing the surface into multiple patches, while the multi-layer models consider the vertical heterogeneity of surface conditions [22; 23]. One-source models such as SEBAL (Surface Energy Balance Algorithm for Land), METRIC (Mapping EvapoTranspiration at high Resolution with Internalized Calibration), SEBS (Surface Energy Balance System) and SSEBop (Operational Simplified Surface Energy Balance) have been widely used to estimate ET\({}_{a}\). These models have shown good performance over terrestrial ecosystems and crops [24; 25; 26; 27]. The ET\({}_{a}\) estimated by SEB models at basin or regional scales can be spatialized by remote sensing techniques, which allow to monitor different crops using currently available satellite data  
Figure 1: Study region and view of pastures at Oromo calibration site during the drought of the austral summer 2014–2015. (**a**) Temperate pastures in southern Chile (green shading), location of the agrometeorological monitoring stations (circles) and the Oromo calibration site (red triangle). (**b**) View of the monitoring station in Oromo and surrounding pastures during the middle and later part of the summer drought of 2014–2015. from platforms such as the Copernicus Program, Earth Resources Observation System (EROS), and the recently launched ECOSTRESS [28; 29]. These products provide a range of frequent and spatially continuous images of surface biophysical variables that influence \(\mathrm{ET_{a}}\)[30].