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Article

A Novel Bias Correction Method for Soil Moisture and Ocean Salinity (SMOS) Soil Moisture: Retrieval Ensembles

by and *
School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), UNIST-gil 50, Eonyang-eup, Ulju-gun, Ulsan 44919, Korea
*
Author to whom correspondence should be addressed.
Current address: Department of Environmental Engineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, Milan 20133, Italy
Academic Editors: Nicolas Baghdadi and Prasad S. Thenkabail
Remote Sens. 2015, 7(12), 16045-16061; https://doi.org/10.3390/rs71215824
Received: 27 September 2015 / Revised: 23 November 2015 / Accepted: 25 November 2015 / Published: 2 December 2015
Bias correction is a very important pre-processing step in satellite data assimilation analysis, as data assimilation itself cannot circumvent satellite biases. We introduce a retrieval algorithm-specific and spatially heterogeneous Instantaneous Field of View (IFOV) bias correction method for Soil Moisture and Ocean Salinity (SMOS) soil moisture. To the best of our knowledge, this is the first paper to present the probabilistic presentation of SMOS soil moisture using retrieval ensembles. We illustrate that retrieval ensembles effectively mitigated the overestimation problem of SMOS soil moisture arising from brightness temperature errors over West Africa in a computationally efficient way (ensemble size: 12, no time-integration). In contrast, the existing method of Cumulative Distribution Function (CDF) matching considerably increased the SMOS biases, due to the limitations of relying on the imperfect reference data. From the validation at two semi-arid sites, Benin (moderately wet and vegetated area) and Niger (dry and sandy bare soils), it was shown that the SMOS errors arising from rain and vegetation attenuation were appropriately corrected by ensemble approaches. In Benin, the Root Mean Square Errors (RMSEs) decreased from 0.1248 m3/m3 for CDF matching to 0.0678 m3/m3 for the proposed ensemble approach. In Niger, the RMSEs decreased from 0.14 m3/m3 for CDF matching to 0.045 m3/m3 for the ensemble approach. View Full-Text
Keywords: bias correction; SMOS soil moisture data assimilation; brightness temperature (TB) ensembles; West Africa bias correction; SMOS soil moisture data assimilation; brightness temperature (TB) ensembles; West Africa
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MDPI and ACS Style

Lee, J.H.; Im, J. A Novel Bias Correction Method for Soil Moisture and Ocean Salinity (SMOS) Soil Moisture: Retrieval Ensembles. Remote Sens. 2015, 7, 16045-16061. https://doi.org/10.3390/rs71215824

AMA Style

Lee JH, Im J. A Novel Bias Correction Method for Soil Moisture and Ocean Salinity (SMOS) Soil Moisture: Retrieval Ensembles. Remote Sensing. 2015; 7(12):16045-16061. https://doi.org/10.3390/rs71215824

Chicago/Turabian Style

Lee, Ju H., and Jungho Im. 2015. "A Novel Bias Correction Method for Soil Moisture and Ocean Salinity (SMOS) Soil Moisture: Retrieval Ensembles" Remote Sensing 7, no. 12: 16045-16061. https://doi.org/10.3390/rs71215824

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