Cloud-based agricultural crop water use monitoring across Saudi Arabia

Monitoring agricultural water use is essential to ensure water security, especially in regions facing water scarcity. Satellite-acquired multi-spectral images of the Earth’s surface provide crucial data to enable frequent estimations of crop water use. In large data-scarce regions, these estimations represent a key source of information for water management. The emergence of cloud-based platforms, such as Google Earth Engine (GEE), has made it feasible, accessible, and cost-effective to automate crop water use monitoring pipelines. Here, we demonstrate the potential benefits of a cloud-based crop water use estimation and monitoring framework by estimating a decade's worth of agricultural water use over Saudi Arabia. In Saudi Arabia, large-scale agricultural activities account for the majority  (>80%) of water use, water which is sourced primarily from non-renewable groundwater resources from the Arabian Shelf. Saudi Arabia’s large land area (> 2 million  km²) and the long study period (+10 years) forms the basis of a case-study for our cloud-based model. Previous mapping efforts provided annual maps of individual field boundary delineations at, identifying more than 30,000 fields covering a total of more than 10,000 km² of croplands that are distributed across several large-scale agricultural clusters within the Kingdom. As a preprocessing step, we developed an approach to generate large-scale delineations of irrigated agricultural regions over arid areas. This approach helped reduce processing efforts for field delineations, and at the same time reducing the water use estimation computations. Our GEE cloud-based model implements a two-source energy balance model (TSEB) and automatically incorporates all available Landsat collection 2 surface reflectance and surface temperature products from Landsat 7, 8, and 9, along with climate reanalysis data from the ECMWF ERA5-Land hourly product. The model can be readily applied elsewhere by defining just the date range and study geometry, while allowing the flexibility for more advanced users to control parameters within the TSEB model. Total crop water use (evapotranspiration term only; not accounting for irrigation efficiency) was estimated at between 7 and 12 BCM per year of study, with the highest use in 2016 and the lowest in 2020. Both Riyadh and al Jawf administrative regions collectively shared more than half of the total study cropland area, and a similar contribution of water use. This study represents the convergence of a number of efforts towards developing operational crop water use monitoring, while motivating further applications in other regions and providing a rich dataset for further food and water security related studies.