## 3 Materials and Methods  
### Data Acquisition  
Sentinel-1 Single Look Complex (SLC) products are distributed by the European Space Agency (ESA). These products are acquired by a pair of sun-synchronous, polar-orbiting satellites that operate in the same orbital plane but are positioned 180\({}^{\circ}\) apart, ensuring continuous coverage. The satellites conduct all-weather, day-and-night observations using C-band synthetic aperture radar (SAR). Sentinel-1 operates in four exclusive imaging modes--Stripmap, Interferometric Wide Swath, Extra Wide Swath, and Wave--offering spatial resolutions as fine as 5 m and swath widths up to 400 km (Table 1). The mission supports dual polarization, delivers frequent revisit times, and ensures rapid product availability. Each acquisition includes high-precision measurements of the satellite's position and altitude. A pre-programmed observation strategy ensures data consistency over time, making Sentinel-1 especially well-suited for long-term applications such as land subsidence monitoring. Table 2 presents a summary of the multi-domain datasets employed to model groundwater dynamics, integrating geomorphological (e.g., DEM, slope), hydroclimatic (e.g., terrestrial water storage anomaly (TWSA) from GRACE and precipitation), biophysical (e.g., NDVI and LAI), and geophysical (e.g., InSAR-derived deformation) variables [46, 47, 48]. These datasets were obtained from various sources, including NASA (MOD11A2 and MODIS), the USGS (SRTM), ESA Copernicus, and in situ monitoring networks. Spatial resolutions range from 30 m (e.g., DEM) to 0.1\({}^{\circ}\) (e.g., GRACE), while temporal resolu \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Specifications** \\ \hline Resolution & 5 \(\times\) 20 m \\ Band type & C-band \\ Orbit height & 693 km \\ Orbit inclination & 98.18\({}^{\circ}\) \\ Temporal coverage & 12 days (12/2014 to 12/2022)/6 days (12/2014 to 12/2022) \\ Spectral range & 3.75–7.5 cm \\ Mode & Interferometric Wide (IW) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Sentinel-1 specifications for InSAR time series land subsidence. tions span from daily (e.g., precipitation) to monthly (e.g., deformation) intervals [49]. All variables were harmonized in terms of units and data formats to support cross-domain machine learning workflows. Key proxies such as GRACE-TWSA and soil moisture, along with InSAR-derived deformation time series, provide direct insights into subsurface hydrological processes [50; 51; 52]. To derive surface deformation time series, imagery from the Sentinel-1 constellation was utilized. Moreover, we also utilize information from the Open Land Map, including soil types (clay, sand, bulk density, and soil organic carbon). Dataset Spatial and Temporal Correlation  
Figure 3 presents the spatial (a) and temporal (b) correlations between hydrone-teorological covariates, terrain forms, and auxiliary datasets using Pearson correlation coefficients. The matrix reveals significant positive correlations between soil moisture, NDWI, precipitation, and deformation data, highlighting their robust potential for capturing groundwater fluctuations [53; 54]. Notably, soil moisture and NDWI are particularly effective in reflecting changes in surface and subsurface water availability, indicating their importance in groundwater level predictions [55].