Efficient water management in agriculture requires a precise estimate of evapotranspiration ( $ET$ ). Although local measurements can be used to estimate surface energy balance components, these values cannot be extrapolated to large areas due to the heterogeneity and complexity of agriculture environment. This extrapolation can be done using satellite images that provide information in visible and thermal infrared region of the electromagnetic spectrum; however, most current satellite sensors do not provide this end, but they do include a set of spectral bands that allow the radiometric behavior of vegetation that is highly correlated with the $ET$ . In this context, our working hypothesis states that it is possible to generate a strategy of integration and harmonization of the Normalized Difference Vegetation Index ( $NDVI$ ) obtained from Landsat-8 ( $L8$ ) and Sentinel-2 ( $S2$ ) sensors in order to obtain an $NDVI$ time series used to estimate $ET$ through fit equations specific to each crop type during an agricultural season (December 2017–March 2018). Based on the obtained results it was concluded that it is possible to estimate $ET$ using an $NDVI$ time series by integrating data from both sensors $L8$ and $S2$ , which allowed to carry out an updated seasonal water balance over study site, improving the irrigation water management both at plot and water distribution system scale.

In this work, we present a new methodology integrating data from multiple sources, such as observations from the Landsat-8 (L8) and Sentinel-2 (S2) satellites, with information gathered in field campaigns and information derived from different public databases, in order to characterize the water demand of crops (potential and estimated) in a spatially and temporally distributed manner. This methodology is applied to a case study corresponding to the basin of the Longaví River, located in south-central Chile. Potential and estimated demands, aggregated at different spatio-temporal scales, are compared to the streamflow of the Longaví River, as well as extractions from the groundwater system. The results obtained allow us to conclude that the availability of spatio-temporal information on the water availability and demand pairing allows us to close the water gap—i.e., the difference between supply and demand—allowing for better management of water resources in a watershed.