Seamless finer-resolution soil moisture from the synergistic merging of the FengYun-3 satellite series

Area of Science:

• Earth Science
• Remote Sensing
• Hydrology

Background:

• Increasing satellite observations offer opportunities for global soil moisture estimation.
• China's FengYun (FY) satellites (FY-3B, C, D) provide passive microwave data crucial for filling global soil moisture gaps.

Purpose of the Study:

• To develop a merged soil moisture dataset from FY satellite observations.
• To enhance the spatial resolution and temporal coverage of global soil moisture data.

Main Methods:

• A merging technique minimizing mean square error (MSE) using signal-to-noise ratio was employed.
• Ascending and descending FY satellite observations were combined for sub-daily estimates.
• Deep learning interpolation was used for gap-filling daily merged data.

Main Results:

• A merged soil moisture dataset with 0.15° spatial resolution was created from 2011 to 2020.
• Sub-daily and daily soil moisture estimates were generated.
• Gap-filling successfully reconstructed missing data while preserving dataset characteristics.

Conclusions:

• The developed merged FY soil moisture dataset addresses challenges in utilizing global satellite observations.
• This dataset offers improved soil moisture monitoring for scientific and practical applications.