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Updated: Feb 11, 2026
08:20
In Situ Soil Moisture Sensors in Undisturbed Soils
Published on: November 18, 2022
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Joint Sentinel-1 and SMAP data assimilation to improve soil moisture estimates
H Lievens1,2, R H Reichle2, Q Liu2,3
1Laboratory of Hydrology and Water Management, Ghent University, Ghent, Belgium.
Geophysical Research Letters
|April 17, 2018
Summary
Adding Sentinel-1 radar data to Soil Moisture Active and Passive (SMAP) satellite data assimilation improves soil moisture accuracy. Joint assimilation of both radar and radiometer observations yielded the best results for surface and root-zone soil moisture estimates.
Area of Science:
- Earth Science
- Remote Sensing
- Hydrology
Background:
- Soil Moisture Active and Passive (SMAP) radiometer data is assimilated into the NASA Catchment Land Surface Model.
- This process generates a 9-km SMAP Level-4 Soil Moisture product.
Purpose of the Study:
- To assess the impact of incorporating high-resolution Sentinel-1 radar data into SMAP data assimilation.
- To evaluate the spatio-temporal accuracy of soil moisture estimates using combined radar and radiometer observations.
Main Methods:
- Sentinel-1 radar observations were assimilated either separately or simultaneously with SMAP radiometer observations.
- Simulations were compared against in situ measurements from SMAP validation sites and sparse networks (May 2015 - December 2016).
Main Results:
- Sentinel-1 assimilation consistently improved surface soil moisture accuracy.
- Root-zone soil moisture showed mostly neutral impacts, with larger improvements from SMAP assimilation.
- Joint assimilation of SMAP and Sentinel-1 data performed best.
Conclusions:
- High-resolution radar data from Sentinel-1 enhances the accuracy of soil moisture estimates.
- The complementary nature of radar and radiometer observations is crucial for optimal soil moisture retrieval.
- Combined assimilation strategies offer the most significant improvements in soil moisture monitoring.
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