Satellite-based soil moisture mapping tool for precision agriculture

Overview

Soil moisture is a critical factor in food production, yet obtaining reliable moisture data at meaningful temporal and spatial resolutions remains a major challenge for the agricultural sector. Digital Field Ltd has developed a model and tool, built in collaboration with the Surrey Space Centre, that retrieves and maps soil moisture from RADAR satellite imagery. The solution combines cutting-edge soil science, image processing, and machine learning techniques to convert satellite signals into actionable soil moisture information for farmers and land managers.

The service is designed to support a range of agronomic decisions, from irrigation scheduling to crop planning, while also helping reduce the risks associated with extreme weather events such as drought and flooding. By delivering moisture data at the farm scale, the tool bridges the gap between coarse global datasets and the fine-grained information farmers need on the ground.

Technical specifications

Key features:

• Processes RADAR satellite imagery to retrieve soil moisture values
• Built on a theoretical framework developed by a leading soil moisture scientist
• Integrates soil science models with image processing and machine learning techniques
• Developed in collaboration with the University of Hertfordshire using ERDF-funded research
• Produces farm-scale soil moisture maps suitable for operational agronomic use
• Designed to work across different geographies, soil types, and climatic conditions

Validation approach:

• Field trials conducted with multiple farmers across varied geographies and soil types
• Detailed soil and crop information collected for each participating farm
• Soil moisture probes deployed to provide frequent ground-truth measurements
• Cross-comparison of probe data against tool outputs to validate accuracy under different climatic conditions

Technology readiness level

The underlying theoretical framework and processing model have been established, with preliminary results available from previous PhD research projects. The tool itself has been built through a collaboration with the University of Hertfordshire, supported by ERDF funding. The current stage focuses on farm-scale validation and commercial demonstration, with field trials planned across multiple farms to confirm accuracy and commercial value before broader market deployment.