## 4 Conclusions and Recommendations
### Recommendations for Future Research
* Future efforts should focus on standardized protocols and guidelines for the integration of SM measurements into ecological restoration projects with a particular emphasis on semi-arid landscapes. These protocols should include the development of comprehensive methodologies that not only incorporate advanced remote sensing technologies but also ensure the consistency and comparability of data across different scales and regions. Establishing such protocols will enhance the reliability of SM as a performance indicator, enabling more accurate assessment and improvement of restoration outcomes in semi-arid ecosystems.
* Upscaling applications (from the field measurements to the UAV to the satellite) should be strengthened so that SM monitoring can be implemented at different scales. While UAV estimations of SM provide high-resolution data, their limited scope is insufficient for covering the vast landscapes characteristic of the western USA. Future research should focus on integrating UAV-derived SM data with satellite observations to enhance spatial coverage and data reliability. Additionally, the potential of using cost-effective RGB or multispectral imagery as substitutes for expensive SWIR sensors in UAVs should be investigated to ensure economic feasibility without compromising data quality.
* There are various national and regional land use and cover mapping efforts for the Western US that provide estimates of canopy cover useful for assessing ecological restoration performance. However, it is crucial to identify and demonstrate which of these products offer the highest accuracy.
* Assessing the spatiotemporal dynamics of soil moisture (SM) as a performance indicator of ecological restoration is a significant future research challenge and opportunity. For instance, it is relevant to identify and measure which specific types of restoration practices effectively improve SM in particular landscapes.  
Conceptualization, E.D. and A.H.; methodology, E.D.; investigation, E.D.; writing--original draft preparation, E.D.; writing--review and editing, E.D. and A.H.; supervision, A.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by US Department of Agriculture (USDA) Agricultural Research Service's SCINet Program and AI Center of Excellence, ARS project numbers 0201-88888-003-000D and 0201-88888-002-000D.  
Data Availability Statement: Not applicable.  
**Acknowledgments:** Special thanks to the USDA Agricultural Research Service SCINet Program and AI Center of Excellence. [PERSON] was supported by a postdoctoral fellowship funded by the USDA Agricultural Research Service's SCINet Program and AI Center of Excellence, ARS project numbers 0201-88888-003-000D and 0201-88888-002-000D. This research was supported in part by an appointment to the Agricultural Research Service (ARS) Research Participation Program administered by the Oak Ridge Institute for Science and Education (ORISE) through an interagency agreement between the US Department of Energy (DOE) and the US Department of Agriculture (USDA). ORISE is managed by ORAU under DOE contract number DE-SC0014664. All opinions expressed in this paper are the author's and do not necessarily reflect the policies and views of USDA, DOE, or ORAU/ORISE.  
The authors declare no conflict of interest.