**5. Conclusions**

It is vital to establish landscape irrigation water conservation strategies while determining its impact on the cooling effect of irrigated landscape in the US southwest. Our two-year field study focused on ground-based remote sensing of hybrid bermudagrass and tall fescue under varying irrigation scenarios autonomously imposed by an ET-based smart irrigation controller in central California. When the NDVI range of variation was high, it was well correlated to VR values for both species. Overall, the NDVI showed less variability between replications of the same treatments for both species when compared to VR. This finding suggests NDVI as a consistent and objective proxy of overall turfgrass quality in response to varying irrigation regimes. Hybrid bermudagrass was a superior species to tall fescue when water conservation was concerned. However, it showed 1.6 ◦C higher canopy temperature than tall fescue when it received the same amount of water. Our results suggested the NDVI values of 0.6–0.65 for tall fescue and 0.5 for hybrid bermudagrass to maintain acceptable quality (VR = 6) in the central California region. Further investigation is needed to verify the thresholds obtained in this study, particularly for hybrid bermudagrass, since the recommendation is only based on 2019 data. No CWSI minimum threshold could be identified to maintain the quality of the selected species due to its high variability and low correlation with VR. Given their ease of use for small plot data collection, we selected handheld sensors in this study to measure canopy temperature and NDVI. However, collecting data from larger irrigated areas in practice using handheld sensors might be time-consuming and challenging. Further studies are needed to explore the utility of unmanned aerial vehicles and advanced multispectral and thermal cameras and compare their readings with handheld sensors used in this study.

**Author Contributions:** Conceptualization, A.H.; methodology, A.H.; software, A.H., A.S. (Amninder Singh), and A.S. (Anish Sapkota); validation, A.H., A.S. (Amninder Singh), and A.S. (Anish Sapkota); formal analysis, A.H., M.R., A.S. (Amninder Singh) and A.S. (Anish Sapkota); investigation, A.H., M.R., A.S. (Amninder Singh) and A.S. (Anish Sapkota); resources, A.H. and M.R; data curation, A.H., M.R., A.S. (Amninder Singh) and A.S. (Anish Sapkota); writing—original draft preparation, A.H.; writing—review and editing, A.H., M.R., A.S. (Amninder Singh) and A.S. (Anish Sapkota); visualization, A.H.; supervision, A.H.; project administration, A.H. and M.R; funding acquisition, A.H. and M.R. All authors have read and agreed to the published version of the manuscript.

**Funding:** This study was supported by the University of California Division of Agriculture and Natural Resources competitive grant (ID#: 17-5021) and by the United States Geological Survey (ID#: 2017CA371B).

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.

### **References**

