The Western United States is currently experiencing a “Mega Drought”. This makes efficient water use more important than ever. Turfgrass is a major vegetation type in urban areas and performs many ecosystem services such as cooling through evapotranspiration, fixing carbon from the atmosphere and reducing wild-fire risk. There are now more acres of irrigated turfgrass (>40 million) in the USA than irrigated corn, wheat and fruit trees combined (Milesi et al., 2005). It has been estimated that about 60% of household water is used to irrigate lawns and that as much as 50% of this is wasted (EPA, 2017). Wasted water results from temporal and spatial mis-applications. Smart sprinkler systems often address temporal mis-application issues tailoring watering schedules to local weather conditions and this can reduce irrigation water use by 15% (EPA, 2017). However, effectively modifying spatial application of irrigation water requires the use of new valve-in-head sprinkler technology and the identification of application zones based on spatial variation in grass health, soil properties and topographic attributes. This research uses data from ground and drone surveys of large sports fields under irrigated and non-irrigated conditions to determine spatial irrigation zones using principal components analysis and k-means classification. Zones are developed using all field measurements, soil moisture measurements and NDVI measurements and assessed. The errors and potential wasted water associated with uniform irrigation and different configurations of spatial zones are assessed to determine potential improvements in irrigation efficiency afforded by spatial irrigation zones. A determination is also made as to whether the spatial zones can be temporally static or need to be re-determined periodically or before each irrigation event. Analysis suggests that zones based on spatial soil moisture surveys are better than those based on NDVI. Also, ideally zones should be re-evaluated before each irrigation. However, a less labor-intensive solution is to determine zones based on the similarities in patterns of soil moisture from several surveys.