Remotely sensed data can be valuable for effective groundwater resource supply and demand monitoring

This study illustrated how remotely sensed data can be valuable for effective groundwater resource supply and demand monitoring, specifically for intensively irrigated areas reliant on groundwater, without the need for extensive skills and expensive <em>in situ</em> data.
The datasets used to create the charts for each research chapter are included. In Chapter 4, <em>in situ</em> precipitation observations are compared to the remotely sensed precipitation product selected for use, namely, CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) at the Secondary Catchment A2 spatial resolution, and monthly, seasonal and annual temporal resolutions. In Chapter 5, the remotely sensed groundwater storage anomalies were downscaled to a higher spatial resolution using the Random Forest Machine Learning classifier, and CHIPRS precipitation and MODIS actual evapotranspiration as independent variables. In Chapter 6, active cultivation was identified from monthly Sentinel-2 composites and divided into monthly rainfed and irrigated areas using an existing product. Crop and irrigation water use were estimated using remotely sensed data and compared to the downscaled GWS changes from Chapter 6. Chapter 7 explored the implications and outcomes of incorporating a range of different specific yield (Sy) values into the downscaled GWS product created.
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No field data was collected, and all products generated relied on open source, published datasets. Google Earth Engine codes are included, and made available with the publications.