Self-Correction of Soil Moisture Ocean Salinity (SMOS) Soil Moisture Dry Bias. Issue 6 (2nd November 2019)

• Record Type: Journal Article
• Authors: Lee, Ju Hyoung; Cosh, Michael; Starks, Patrick; Toth, Zoltan
• Is Part Of: Canadian journal of remote sensing. Volume 45:Issue 6(2019)
• Page Start: 814
• Page End: 828
• Publication Date: 2019-11-02
• Subjects: Remote sensing -- Periodicals; 621.367805
• DOI: 10.1080/07038992.2019.1700466
• Languages: English
• ISSNs: 0703-8992

Abstract

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 m 3 /m 3 . Dry bias also decreases from −0.055 to −0.020 m 3 /m 3 . As the proposed approach does not rely on local field measurements, it has a potential as a global operational scheme.