The Western United States, including Utah is currently experiencing a "mega drought". Therefore, the need to limit water use in an efficient way has become essential. Turfgrass, a major vegetation type in urban areas, is the largest irrigated crop in the United States. It performs important ecosystem services such as cooling through evapotranspiration, fixing carbon from the atmosphere, and reducing wild-fire risk. Most residential turfgrass is irrigated using uniform protocols: for example, 20-30 minutes of irrigation every other day. However, more than 50% of irrigated water used on turfgrass is wasted by temporal and spatial misapplications. There are some solutions to this waste of water. Smart sprinklers reduce temporal misapplication by considering the weather of a particular area. Valve-in-head sprinklers can reduce wasted water through reducing spatial misapplications. In addition, sensors can help determine exactly how much water to apply in a certain area. The drawback to sensors is that they are expensive, and one needs ways to extrapolate rates to apply to zones. While spatial zones can be determined in several ways, some are more labor intensive than others. Traditionally, a ground survey would be performed using theta probes, NDVI readers, and infrared thermometers. However, we propose that using the EM38 takes less time and resources to get the similar information. Furthermore, this research works to link sensor measurements from four locations in two fields on the Brigham Young University campus using ground survey, drone survey, and EM38 maps, to determine how irrigation amounts should be varied between existing zones, or zones determined for valve-in-head sprinklers.