## 4 Conclusions and Recommendations  
### Conclusions  
* In our review, we identified a significant gap in the application of SM as a performance indicator to assess the impact of ecological restoration in areas affected by disturbances in semi-arid zones. Although we identified great technological development to measure SM, this gap highlights the need to focus research and develop methodologies that incorporate SM measurements to evaluate and improve restoration results in semi-arid ecosystems; e.g., [PERSON] et al., (2022) [104] highlighted the importance of SM instead of precipitation for crops yields and other purposes. * The integration of DL and ML algorithms has significantly enhanced SM analytics, facilitating detailed spatiotemporal monitoring; additionally, the ISMN plays a pivotal role in harmonizing in situ data, essential for validating satellite observations. * ML techniques, such as RF, SVM, and ANN, are increasingly applied due to their robustness in handling complex, non-linear relationships between SM and various environmental covariates; the integration of these algorithms enhances the resolution and accuracy of SM estimates. * Our review underscores the substantial potential of ML algorithms in the geospatial monitoring of SM across regional and local scales, integrating in situ and satellite remote sensing data, notably through the widespread use of the RF algorithm in conjunction with the SMAP satellite product. * There is also a promising trend towards developing algorithms that reduce reliance on auxiliary data, or utilize readily available remote sensing observations to improve SM monitoring capabilities. * Ground-based soil moisture (SM) measurements are indispensable for the calibration and validation of remote sensing data and models, providing precise and localized information critical for ecological restoration and sustainable land management in semi-arid ecosystems. Our analysis underscores the extensive yet varied global distribution of in situ SM measurement networks, highlighting their pivotal role in enhancing the reliability of satellite-derived SM estimates and supporting climate model reanalysis. Notable examples, such as the ISMN and NCSMMN, demonstrate the integration of large-scale networks that offer real-time, high-quality data essential for comprehensive SM monitoring. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Measurement Method** & **Influence of Soil Surface Characteristics** & **Relevant Soil Properties** \\ \hline Satellite Remote Sensing & Captures surface SM, influenced by soil & Soil texture, surface roughness, \\  & texture, roughness, and vegetation & vegetation cover [64]. \\ UAV-Based Data Collection & High-resolution imagery sensitive to surface conditions, vegetation, and topography & Surface roughness, vegetation density, \\ In situ Measurements & Direct measurements that can be calibrated & Soil porosity, texture, organic matter \\  & against soil structure and SM content & content [21]. \\ Empirical Models & Relies on statistical relationships, influenced by surface reflectance and vegetation & Reflectance properties, vegetation indices \\ Semi-Empirical Models & Combines physical principles and statistical & Soil texture, surface roughness, \\  & relationships, considers surface conditions & vegetation cover [50,56]. \\ Physical Models & Based on fundamental physics, requires & Soil dielectric properties, surface \\  & detailed surface properties for accuracy & roughness, moisture [57]. \\ Microwave Remote Sensing & Sensitive to surface SM content and roughness, & SM content, vegetation cover, \\  & can penetrate vegetation to some extent & roughness [83]. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Soil moisture modeling techniques and their relation to soil and vegetation characteristics. * The use of empirical, semi-empirical, and physical models significantly enhances the accuracy and utility of soil moisture (SM) estimates.