| Summary: | Efficient water management in agriculture requires a precise estimate of evapotranspiration (<inline-formula> <math display="inline"> <semantics> <mrow> <mi>E</mi> <mi>T</mi> </mrow> </semantics> </math> </inline-formula>). 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 <inline-formula> <math display="inline"> <semantics> <mrow> <mi>E</mi> <mi>T</mi> </mrow> </semantics> </math> </inline-formula>. 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 (<inline-formula> <math display="inline"> <semantics> <mrow> <mi>N</mi> <mi>D</mi> <mi>V</mi> <mi>I</mi> </mrow> </semantics> </math> </inline-formula>) obtained from Landsat-8 (<inline-formula> <math display="inline"> <semantics> <mrow> <mi>L</mi> <mn>8</mn> </mrow> </semantics> </math> </inline-formula>) and Sentinel-2 (<inline-formula> <math display="inline"> <semantics> <mrow> <mi>S</mi> <mn>2</mn> </mrow> </semantics> </math> </inline-formula>) sensors in order to obtain an <inline-formula> <math display="inline"> <semantics> <mrow> <mi>N</mi> <mi>D</mi> <mi>V</mi> <mi>I</mi> </mrow> </semantics> </math> </inline-formula> time series used to estimate <inline-formula> <math display="inline"> <semantics> <mrow> <mi>E</mi> <mi>T</mi> </mrow> </semantics> </math> </inline-formula> 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 <inline-formula> <math display="inline"> <semantics> <mrow> <mi>E</mi> <mi>T</mi> </mrow> </semantics> </math> </inline-formula> using an <inline-formula> <math display="inline"> <semantics> <mrow> <mi>N</mi> <mi>D</mi> <mi>V</mi> <mi>I</mi> </mrow> </semantics> </math> </inline-formula> time series by integrating data from both sensors <inline-formula> <math display="inline"> <semantics> <mrow> <mi>L</mi> <mn>8</mn> </mrow> </semantics> </math> </inline-formula> and <inline-formula> <math display="inline"> <semantics> <mrow> <mi>S</mi> <mn>2</mn> </mrow> </semantics> </math> </inline-formula>, 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.
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