## 3 Analysis and Discussion
### Measurement and Monitoring of Soil Moisture Dynamics
#### 3.2.1 Remote Sensing: Satellite Dataset for Monitoring Soil Moisture Dynamics  
Satellite imagery serves as a crucial tool for SM measurement, leveraging a diverse array of sensor technologies. These include thermal and optical sensors, alongside active and passive microwave sensors which are widely recognized for their efficacy [44,45]. Thermal infrared (TIR) [46], visible/hyperspectral infrared (VIR/NIR), and radar sensors are among the most utilized technologies for this purpose [47]. [PERSON] et al., (2023) [48] conducted a comprehensive review on the efficacy of empirical, semi-empirical and physical models for SM estimation and found that the relationship between SM and soil reflectance using optical sensors is uncertain due to atmospheric condition limitations. They concluded that currently, there is no single model or data source that provides a universally high-accuracy solution for SM estimation. We identified that radar remote sensing is the most commonly used method to estimate SM in bare soils, utilizing physical, empirical, and semi-empirical approaches [49,50]. Models and Approaches for Monitoring Soil Moisture Dynamics Using Satellite Dataset  
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2. _Empirical models_: leveraging correlations between remote sensing data--such as surface temperature, radar, vegetation index, and ground-measured data, offer simplicity and ease of data access but suffer from limited interpretability and reliance on specific calibration datasets. In our review, we found that the Water Cloud Model (WCM) and the Dubois Model are commonly used to estimate SM using synthetic aperture radar (SAR) backscatter. [51]. [PERSON] et al., (1995) [52] evaluated several SAR data sets by comparing derived SM values with in situ measurements collected in a variety of scenes between 1991 and 1994 in space (SIR-C) and [PERSON] et al., (2019) [53] applied the WCM to the L-band PALSAR/ALOS-2 satellite data for SM estimation. Their findings indicate that increasing the number of polarimetric parameters at the C-band can significantly enhance the robustness of surface SM estimates. 3. _Semi-empirical models_: integrating both statistical correlations and physical principles, account for factors like vegetation and soil texture, enhancing precision over purely empirical models while still necessitating observed data for calibration [54]. This type of model strikes a balance between accuracy and operational complexity, suitable for applications requiring moderate precision without the comprehensive data demands of physical models [55]. Many semi-empirical models estimate SM by considering the relationship between the backscatter coefficient, surface parameters, and radar parameters [50,56]. 4. _Physical models_: grounded in the fundamental laws of physics like the Integral Equation Model (IEM), provide detailed and interpretable SM estimates, especially for bare soil areas, at the cost of increased complexity and computational demands [57]. High potential has been identified by combining multiple models and data sources to refine SM retrieval accuracy and applicability, leveraging multi-source data to enhance spatial-temporal resolution, extend time series analyses, and improve model interpretability and precision [58]. Products for Monitoring Soil Moisture Dynamics Using Satellite Dataset
* _Downscaled products_: where the models stand out SMAP/Sentinel (SPL2 SMAP) product (1 and 3 km spatial resolution--global) [59], European Space Agency--Climate Change Initiative (ESA-CCI) Surface Soil Moisture (SSM) product (1 km spatial resolution--global) [60], European Remote Sensing Satellite-2 (ERS-2) scatterometer (SCAT) (1 km spatial resolution--Europe) [61], and SMAP-HydroBlocks (30 m spatial resolution--CONUS) [62]. * _Microwave remote sensing products_: CGLS Sentinel-1 (1 km spatial resolution--Europe) [63], SMAP Enhanced L3 (36 and 9 km spatial resolution--global) [64], SMOS-IC retrieval data set based on the SMOS L-band observations (1 km spatial resolution--global) [65].