Seasonal Crop Water Balance Using Harmonized Landsat-8 and Sentinel-2 Time Series Data

Abstract

Efficient water management in agriculture requires a precise estimate of evapotranspiration ( E T ). 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 E T . 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 ( N D V I ) obtained from Landsat-8 ( L 8 ) and Sentinel-2 ( S 2 ) sensors in order to obtain an N D V I time series used to estimate E T 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 E T using an N D V I time series by integrating data from both sensors L 8 and S 2 , 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.

Characterization of Low-Cost Capacitive Soil Moisture Sensors for IoT Networks

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Landsat Hourly Evapotranspiration Flux Assessment using Lysimeters for the Texas High Plains

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Editorial—Managing and Planning Water Resources for Irrigation: Smart-Irrigation Systems for Providing Sustainable Agriculture and Maintaining Ecosystem Services

“…Aiming at contributing to the efficient use and management of irrigation water, Gavilán et al (2019) [1] tackle the difficult and important task of estimating evapotranspiration in agricultural irrigated areas. The work explores the feasibility of using satellite-based data to carry out updated seasonal water balances over large areas-an approach that would overcome difficulties in using local measurements of surface energy balance components to extrapolate the conditions over large agricultural areas.…”