Soil moisture is a crucial parameter that reflects the water content in the soil, impacting agriculture, hydrology, and climate studies. Soil moisture defines the amount of water content present on the surface and is estimated for precise agriculture and crop monitoring. Traditional methods, such as gravimetric sampling and time-domain reflectometry (TDR), offer accurate estimates but are limited by their labour-intensive, localized, and costly nature. Remote Sensing, particularly Synthetic Aperture Radar (SAR), has emerged as a valuable tool for soil moisture estimation, offering advantages like wide-area coverage, non-invasiveness, and efficiency. Synthetic Aperture Radar (SAR), especially data from Sentinel-1A, uses microwave signals to penetrate the soil surface and is sensitive to soil moisture content and surface roughness. This paper focuses on estimating soil moisture in the Madurai region using Sentinel-1A SAR data, employing polarization ratio to derive moisture estimates. The Madurai region data captured by Sentinel-1A undergoes radiometric calibration, speckle filtering using a Lee 7 × 7 filter, and terrain correction using Range Doppler, ensuring accurate and refined imagery. The methodology offers a reliable, non-invasive, and cost-effective approach for large-scale soil moisture monitoring.