Authors:

Emiko A Sano

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About Paper:

Effective farm management for optimal crop yield requires continuous monitoring of key indicators of crop and soil health, with soil moisture being one of the most critical parameters. While in-situ soil moisture sensors provide accurate point measurements, their spatial coverage is often insufficient to capture the heterogeneity across agricultural fields. Synthetic Aperture Radar (SAR) offers a promising alternative by indirectly estimating soil moisture through analysis of radar backscatter. Its sensitivity to surface dielectric properties and increased spatial coverage enables detection of the heterogeneity of agricultural fields. In this study, we developed a time series model for soil moisture retrieval using high-resolution S-band UAV-based SAR imagery. Data collection occurred biweekly from March to October during the 2024 and 2025 growing seasons. Ground truth soil moisture data were acquired through buried sensors, handheld probes, and soil sampling. The SAR backscatter was calibrated, converted to decibel values, and analyzed over time. An empirical model linking SAR backscatter to soil moisture was developed using linear regression techniques. This model will be validated against in-situ measurements across varying soil textures, roughness conditions, and vegetation cover types. Successful validation would demonstrate the model's potential for scalable, high-resolution soil moisture monitoring in precision agriculture. Future work will investigate its generalizability to broader land covers and under different viewing geometries. Keywords: Synthetic Aperture Radar (SAR); Unmanned Aerial Vehicle (UAV); Soil Moisture; Time Series; Drone

Source:

Purdue University / 2025

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Co-authors:

Emiko A Sano