A Calibration/Disaggregation Coupling Scheme for Retrieving Soil Moisture at High Spatio-Temporal Resolution: Synergy...

Area of Science:

• Earth Observation
• Remote Sensing Science
• Agricultural Science

Background:

• High-resolution soil moisture (SM) data are crucial for agriculture and hydrology.
• Existing remote sensing methods have limitations in spatio-temporal resolution and sensitivity to environmental factors.
• A synergistic approach combining multiple remote sensing data sources is lacking.

Purpose of the Study:

• To develop a novel algorithm for field-scale soil moisture retrieval.
• To integrate data from SMAP (passive microwave), MODIS/Landsat (optical/thermal), and Sentinel-1 (radar).
• To achieve high spatio-temporal resolution SM data at Sentinel-1's observation frequency.

Main Methods:

• A three-step procedure: disaggregation of SMAP SM data using optical/thermal data, calibration of a radar-based SM model, and application of the calibrated model to Sentinel-1 data.
• Utilized vegetation descriptors (NDVI, PR, CO) derived from optical (Sentinel-2) or radar (Sentinel-1) data for calibration.
• Tested two radar models (linear regression and semi-empirical) over wheat crop sites in Morocco.

Main Results:

• Achieved field-scale temporal correlation between predicted and in situ SM ranging from 0.66 to 0.81.
• The linear radar model using polarization ratio (PR) as a vegetation descriptor demonstrated a good balance of precision and robustness.
• The synergistic approach successfully provided SM data without requiring in situ measurements for calibration.

Conclusions:

• The developed synergistic algorithm effectively combines multi-resolution and multi-sensor data for accurate soil moisture estimation.
• This approach offers a robust and calibration-free method for obtaining field-scale soil moisture data.
• The findings have significant implications for agricultural and hydrological monitoring and management.