Soil Moisture Retrieval Over Agricultural Fields Using Synthetic Aperture Radar (SAR) Data

Soil moisture is vital for agricultural fields as it determines water availability for crops, directly affecting plant growth and productivity. It regulates nutrient uptake, root development, and microbial activity, ensuring efficient use of fertilizers and soil resources. Proper soil moisture levels prevent water stress, reduce crop failure risks, and optimize water irrigation efficiency. Accurate soil moisture monitoring supports sustainable farming practices, helps mitigate drought impacts, and enhances climate resilience. By maintaining optimal soil moisture, farmers can improve resource use, boost crop yields, and promote long-term agricultural sustainability. This study aims to develop an approach for retrieving soil moisture from Sentinel-1 A Synthetic Aperture Radar (SAR) data. The SAR data were processed for the 2024 dry season using a triangle-based approach in the Mekong Delta, Vietnam, following three main steps: (1) data preprocessing to convert raw radar backscatter values into the sigma naught (σ₀) backscatter coefficient in decibels (dB). This involves radiometric calibration, noise removal, and logarithmic scaling to enhances data interpretability, allowing for better comparisons across different radar acquisitions and improved analysis accuracy, (2) soil moisture retrieval by means of a triangle-based method developed based on the dual-polarization modes of the vertical transmit and vertical receive polarization (VV) and vertical transmit and horizontal receive polarization (VH). This method employs the triangular feature space created by using change in VV backscatter coefficients and the radar vegetation index (RVI), in which RVI helps distinguish vegetation effects while VV backscatter provides information on soil moisture. Combining both parameters thus allows for more precise moisture estimation even in complex environments, and (3) error verification. The results of soil moisture retrieval compared with the reference data showed moderate positive correlation, with the values of correlation coefficient (r) greater than 0.5 and the root mean square error (RMSE) smaller than 0.05, respectively. The lower soil moisture levels were especially observed in coastal areas, where higher evaporation rates, saline intrusion, and limited rainfall contribute to drier soils. These conditions create challenges for agriculture in coastal regions, as crops are more susceptible to drought stress and water shortages. Consequently, managing soil moisture becomes crucial for maintaining crop productivity and ensuring sustainable farming in coastal provinces. Eventually, soil moisture data was spatially aggregated with cropping areas to improve management practices in the region, allowing precise monitoring of soil conditions relative to specific crops and enabling tailored irrigation and water management strategies. This approach, leveraging dual-polarization SAR data with aid of the triangle-based method, could enhance soil moisture monitoring in agriculture and is completely transferable to other regions across the globe for soil moisture monitoring.