Evapotranspiration (ET) is key to assess crop water balance and optimize water-use efficiency. To attain sustainability in cropping systems, especially in semi-arid ecosystems, it is necessary to improve methodologies of ET estimation. A method to predict ET is by using land surface temperature (LST) from remote sensing data and applying the Operational Simplified Surface Energy Balance Model (SSEBop). However, to date, LST information from Landsat-8 Thermal Infrared Sensor (TIRS) has a coarser resolution (100 m) and longer revisit time than Sentinel-2, which does not have a thermal infrared sensor, which compromises its use in ET models as SSEBop. Therefore, in the present study we set out to use Sentinel-2 data at a higher spatial-temporal resolution (10 m) to predict ET. Three models were trained using TIRS’ images as training data (100 m) and later used to predict LST at 10 m in the western section of the Copiapó Valley (Chile). The models were built on cubist (Cub) and random forest (RF) algorithms, and a sinusoidal model (Sin). The predicted LSTs were compared with three meteorological stations located in olives, vineyards, and pomegranate orchards. RMSE values for the prediction of LST at 10 m were 7.09 K, 3.91 K, and 3.4 K in Cub, RF, and Sin, respectively. ET estimation from LST in spatial-temporal relation showed that RF was the best overall performance (R2 = 0.710) when contrasted with Landsat, followed by the Sin model (R2 = 0.707). Nonetheless, the Sin model had the lowest RMSE (0.45 mm d−1) and showed the best performance at predicting orchards’ ET. In our discussion, we argue that a simplistic sinusoidal model built on NDVI presents advantages over RF and Cub, which are constrained to the spatial relation of predictors at different study areas. Our study shows how it is possible to downscale Landsat-8 TIRS’ images from 100 m to 10 m to predict ET.

Chile has defined an energy development policy in which hydropower is an important part of the energy grid. This energy source has not yet been accepted by many people in local communities. For future hydroelectric development to be more widely accepted, the Chilean Ministry of Energy developed a methodological framework called Objects of Valuation. This framework is aimed at identifying the main community interests that may condition hydroelectric development. The objective of this paper is to analyze the scope of the framework based on a review of the scientific literature and information generated through participatory activities in three basins that have high hydropower potential. Analyzing the results obtained from the application of the framework, four complementary intangible factors not represented by the framework are identified: the lack of validation of a formal participatory process, under-recognition of different worldviews, distrust regarding the development of hydroelectricity, and a sense of self-determination in the community. These factors could potentially condition community acceptance of hydroelectricity, thereby limiting the framework as a decision-making tool. We recommend that this methodological framework should be complemented by the incorporation of intangible elements in the decision-making process, using a systematic tool applicable to spatial planning and strategic environmental-assessment processes.