Mapping time-series evapotranspiration for agricultural applications

Abstract: Fresh water provides a range of essential services and is often used for irrigation purposes. Decreasing precipitation and increasing temperatures caused by climate change together with increased usage by humans has put these resources under stress, especially in relatively dry areas. This project takes a closer look at the irrigation of agricultural areas in the Guadalquivir river basin in southern Spain. An indication of irrigation intensity is attained by estimating the evapotranspiration using the S-SEBI method. This method is based on the surface energy balance and uses Landsat satellite images as its main input. Secondarily, a random forest classifier is trained to differentiate between irrigated and non-irrigated agricultural areas. Evaluation of these implementations produced a Root Mean Squared Difference of 0.8mm/day for daily actual evapotranspiration and an overall accuracy close to 80% for classification of irrigated areas. The results indicate that both the level of evapotranspiration and the irrigated agricultural surface area were stable over the period 2000-2020. This should not be taken to indicate that current freshwater management is therefore sustainable. This project shows the value ofcloud-computing services such as Google Earth Engine for remote sensing research. With this tool evapotranspiration estimation and irrigation classification was performed on an unprecedented temporal and spatial scale.