This study took place in Perambalur district, Tamil Nadu, from September 2018 to January 2019, aiming to estimate and map soil moisture using Sentinel-1 C-band Synthetic Aperture Radar (SAR) data. Monthly dual-polarized (VV and VH) SAR images were collected along with simultaneous ground measurements using the gravimetric method during satellite passes. SAR data were processed using the SNAP toolbox to extract the backscattering coefficient (σ⁰), which was correlated with local soil moisture and the incidence angle. VV polarization σ⁰ values ranged from -14.28 dB to -2.47 dB and VH values from -21.84 dB to -9.04 dB. Multiple linear regression models were developed to establish empirical relationships between σ⁰, the incidence angle, and soil moisture. The measured soil moisture levels displayed temporal fluctuations. October 2018 exhibited the highest variability (standard deviation ≈7.94) and an outlier value of 29.17%, likely due to uneven rainfall. January 2019 recorded the lowest average soil moisture (mean ≈5.04%) and the least variability, indicating stable, dry conditions. November 2018 had the largest sample size (30 observations) and showed moderate variability, while both September 2018 and January 2019 reflected relatively low moisture levels. A correlation analysis between observed soil moisture and SAR backscatter indicated that VV polarization consistently demonstrated a stronger association with ground measurements than VH. These empirical equations were integrated into a Google Earth Engine (GEE) tool for near real-time soil moisture visualization and monitoring. The GEE tool estimated soil moisture with a coefficient of determination (R²) of 0.65 and delivered instantaneous spatial outputs. This study demonstrates that Sentinel-1 SAR data, particularly VV polarization, combined with cloud-based platforms like GEE, provides a reliable and scalable approach for real-time soil moisture assessment across agricultural landscapes.