Field test of the surface soil moisture mapping using Sentinel-1 radar data

Area of Science:

• Hydrology
• Remote Sensing
• Precision Agriculture

Background:

• Soil surface moisture is vital for hydrological assessment and irrigation.
• High-resolution soil moisture mapping aids precision irrigation, erosion control, and water resource management.
• Sentinel-1 radar data offers potential for field-scale soil moisture monitoring.

Purpose of the Study:

• To develop and validate a novel method for high-resolution soil surface moisture mapping using Sentinel-1 data.
• To assess the accuracy of radar-derived soil moisture estimates against direct measurements.
• To establish a methodological basis for advanced soil moisture monitoring in precision irrigation.

Main Methods:

• Direct soil moisture measurements at a 5-cm depth were conducted.
• Soil moisture was retrieved from Sentinel-1 radar observations.
• A permittivity model integrated Sentinel-1 reflectivity (via neural networks) and UAV-derived surface roughness to estimate soil moisture.
• An inverse problem approach was used to generate soil moisture geodata.

Main Results:

• A high determination coefficient (0.948) and low standard deviation (2.04%) were achieved when comparing direct measurements with modelled soil moisture.
• A satisfactory linear correlation and reproduction were confirmed between the two datasets.
• The developed method provides a reliable basis for soil surface moisture monitoring.

Conclusions:

• The developed radar-based methodology offers a scientifically sound approach for soil surface moisture monitoring.
• This technology supports precision irrigation management by providing accurate, high-resolution soil moisture data.
• The findings contribute to improved irrigation efficiency and reduced environmental impact.