A global 400-m high-resolution soil moisture dataset derived from multi-sensor remote sensing observations

Area of Science:

• Earth Science
• Remote Sensing
• Hydrology

Background:

• Satellite remote sensing has monitored Soil Moisture (SM) for decades.
• The Soil Moisture Active Passive (SMAP) mission provides global SM data but with limited spatial resolution (tens of kilometers).
• Existing SMAP products have limitations for local and regional hydrological studies due to coarse resolution.

Purpose of the Study:

• To downscale SMAP SM data to a higher spatial resolution (400m).
• To improve the accuracy and utility of SM data for hydrological applications.
• To validate the downscaled SM product against in situ observations.

Main Methods:

• Developed an algorithm to downscale SMAP enhanced Level-2 radiometer SM product from 9km to 400m resolution.
• Incorporated land surface temperature (LST) and leaf area index (LAI) products from the Visible Infrared Imaging Radiometer Suite (VIIRS).
• Validated the 400m downscaled SM product against International Soil Moisture Network (ISMN) in situ data.

Main Results:

• The 400m downscaled SM product demonstrated superior performance compared to 1km and 9km SMAP products.
• The downscaled product showed lower Root Mean Square Error (RMSE) and Mean Absolute Error (MAE).
• Finer-scale spatial patterns of SM were revealed, supporting detailed hydrological analysis.

Conclusions:

• The downscaling algorithm effectively enhances SMAP SM data resolution.
• The 400m SM product offers improved accuracy and spatial detail for hydrological applications.
• This high-resolution SM data supports localized and regional water resource management and research.