## 3 Analysis and Discussion
### Modeling  
#### 3.3.1 Algorithms  
Artificial Intelligence (AI) models are increasingly applied in soil science to predict local-scale near-surface SM data. These models use coarse-scale predictors such as topographic features, evapotranspiration, land surface temperature, and vegetation indices from remote sensing products to enhance the resolution and accuracy of SM estimates [87]. The use of ML techniques can significantly improve SM estimation through various applications such as those described below:  
* _Pedotransfer Functions_: ML is particularly effective in predicting soil hydraulic properties [88; 89]. * _Predictive Models for SM Estimation_: These models are crucial for meteorological studies, runoff and soil erosion estimates, and predicting droughts and floods [6; 90; 91]. * _Remote Sensing for SM Retrieval_: Utilizes optical, infrared, and primarily microwave wavelengths to estimate SM content without physical soil contact [20; 21]. * _Downscaling Satellite-Derived SM Products_: Improves the resolution of satellite-based SM data, enhancing usability for detailed local applications [60; 62]. Figure 5: Sensors carried by UAVs: (**a**) SWIR, (**b**) multispectral camera, (**c**) low-resolution multispectral sensor, (**d**) dual multispectral with panchromatic, (**e**) thermal camera, (**f**) hyperspectral + LiDAR sensors, (**g**) multispectral + thermal + panchromatic, and (**h**) RGB camera. The use of ML algorithms to downscale passive microwave surface soil moisture (SSM) products, like SMAP and SMOS, is increasing due to their typically low resolution, which limits their application and validation at regional and local scales. [PERSON] et al., (2021) [62] employed RF to enhance these SM products to 30 m of spatial resolution and [PERSON] et al., (2023) [19] provided SSM (0-5 cm) at 1 km spatial and daily temporal resolution using the RF algorithm and demonstrating the potential of ML to improve the usability of coarse-resolution data for more precise regional studies. In modeling the complex, non-linear relationships impacting SM, we found the most prominent algorithms include RF, support vector machine (SVM), and artificial neural network (ANN). These methods are favored for their robustness in handling variable interactions and their effectiveness in deriving accurate SM predictions, e.g., in semi-arid areas, the multiscale extrapolative learning algorithm (MELA) was applied using hydroclimatic data obtained from remote sensors to extrapolate local monthly SM measurements to multiple depths from 2015-2021 [91]. [PERSON] et al., (2020) [90] used optical and thermal sensors from Landsat 8 in Iran's semi-arid restoration areas, and they identified that the RF method outperformed others, achieving the highest Nash-Sutcliffe efficiency value [92]. [PERSON] et al., (2023) [6] evaluated eight ML algorithms for estimating SSM, and found that the ensemble model with K-neighbors regressor (KNR), RF, and extreme gradient boosting (XB) had the best performance. [PERSON] et al., (2020) [93] observed an overall increase in the use of ML models in soil science yet noted a relative decline in the usage of certain models like SVM, multivariate adaptive regression splines (MARS), and classification and regression trees (CART). This shift reflects a trend towards adopting models such as RF, which are perceived as offering enhanced capabilities in handling complex soil science data. The RF algorithm is used in ML due to its capability to handle low interpretability scenarios [94]. However, its popularity also stems from its ability to facilitate easier interpretation through the identification of important variables, enhancing its utility in various applications [95]. The application of interpolation methods for SM measurement and monitoring is infrequent due to the scarcity of in situ observations and the significant spatial variability of hydrometeorological conditions and soil characteristics.