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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #370961
Research Project: Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems

Location: Hydrology and Remote Sensing Laboratory

Title: Self-correction of Soil Moisture Ocean Salinity (SMOS) soil moisture dry bias

Author
item LEE, J.H. - Konkuk University
item Cosh, Michael
item Starks, Patrick
item TOTH, Z. - National Oceanic & Atmospheric Administration (NOAA)

Submitted to: Canadian Journal of Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/28/2019
Publication Date: 12/30/2019
Citation: Lee, J., Cosh, M.H., Starks, P.J., Toth, Z. 2019. Self-correction of Soil Moisture Ocean Salinity (SMOS) soil moisture dry bias. Canadian Journal of Remote Sensing. 45(6):814-828. https://doi.org/10.1080/07038992.2019.1700466.
DOI: https://doi.org/10.1080/07038992.2019.1700466

Interpretive Summary: Calibration and validation of satellite data can be difficult because of the differences in sensing scale between the satellite and the in situ resources commonly used for ground truth. A statistical analysis is introduced to validate satellite data which is able to compensate for the deficiencies of the in situ approach. Testing this approach on the Soil Moisture Ocean Salinity Missions soil moisture product was able to significantly improve the errors and bias of the retrieval of soil moisture. This will provide a valuable alternative to current operational quality controls.

Technical Abstract: Satellites produce global monitoring data, while field measurements are made at a local station over the land. Due to difference in scale, it has been a challenge how to define and correct the satellite retrieval biases. Although the relative approach of cumulative distribution functions (CDF) matching compares a long-term climatology of reference data with that of satellite data, it does not mitigate the retrieval biases generated from Instantaneous Field of View (IFOV) measurements over short timescales. As an alternative, we suggest stochastic retrievals (using probabilistic distribution function) to reduce the dry bias in soil moisture retrievals from the satellite SMOS (Soil Moisture and Ocean Salinity) that occurs at the time scale of several days. Rank Probability Skill Score (RPSS) is also proposed as non-local Root Mean Square Errors (RMSEs) of a probabilistic version to optimize stochastic retrievals. With this approach, the time-averaged RMSEs of retrieved SMOS soil moisture is reduced from 0.072 to 0.035'm3/m3. Dry bias also decreases from -0.055 to -0.020'm3/m3. As the proposed approach does not rely on local field measurements, it has a potential as a global operational scheme.