With the foreseen increase in population and the reliance on water as a key input for agricultural production, greater demand will be placed on freshwater supplies. The objective of this work was to present the newly developed Android smartphone application to calculate crop evapotranspiration in real-time to support field-scale irrigation management. As part of the answer to water shortage, we embraced technology by developing AgSAT, a Google Earth Engine-based application that optimizes water use for food production. AgSAT uses meteorological data to calculate daily water requirements using the ASCE-Penman–Monteith method (ETref) and vegetation indices from satellite imagery to derive the basal crop growth coefficient, Kcb. The performance of AgSAT to estimate ETref was assessed using climatic data from 18 meteorological stations distributed over several climatic zones worldwide. ETref estimation through the app showed acceptable results with values of 1.27, 0.9, 0.79, 0.95, and 0.5 for root mean square error (RMSE), correlation coefficient (r), modeling efficiency (NSE), concordance index (d), and percentage bias (Pbias), respectively. AgSAT guides gross irrigation requirements for crops and rationalizes water quantities used in agricultural production. AgSAT has been released, is currently in use by research scientists, agricultural producers, and irrigation managers, and is freely accessible from the Google Play and IOS Store and also at agsat.app. Our work is geared towards the development of remote sensing-based technologies that transfer significant benefits to farmers and water-saving efforts.

Quantifying Evapotranspiration (ET), a major component of the hydrologic cycle, is critical for monitoring agricultural water use at the field scale and enabling better water management. Despite the importance of a global field-scale ET product, there is limited information available on the inter- and intra-annual variability in recent years. Here, we generate a monthly 100-m resolution ET dataset from 1990 to 2021, using a validated single-source energy balance algorithm and more than four million thermal Landsat satellite imagery scenes. We find that global ET has significantly accelerated over the last two decades at an annual rate of 1.33 mm yr− 1 (0.2%), despite regional disparities. This rate has intensified to 0.47% and 1.97–2.15% in the most recent twelve and seven years of the study, respectively, primarily due to increases in summer ET over North America, African tropics, and Indochina. Our machine learning analysis indicates that air temperature, rainfall, and atmospheric moisture are the primary drivers of these ET trends. This publicly available ET datasets has the potential to advance our understanding of global and local water use dynamics and can inform water policy and management decisions in a warming climate.