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

Ju Lee,Jungho Im

doi:10.3390/rs71215824

Abstract

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.

Highlights

Data assimilation for merging satellite observations with model states is widely applied to provide the boundary conditions with high quality for Numerical Weather Prediction (NWP) model predictions, as well as to improve the spatial and temporal resolutions of satellite data [1,2]
The objectives of this study are to provide a rescaling function with retrieval ensembles and to mitigate Soil Moisture and Ocean Salinity (SMOS) errors over West Africa, as this region is exposed to several complicated retrieval errors such as a high vertical gradient of soil moisture during the West African Monsoon (WAM) period and the presence of vegetation, and as it is difficult to simulate dry soils with models [22]
The error distributions examined include normal, random, and lognormal functions with different variances at 10%, 20%, 30%, 40%, and 60% and various ensemble sizes (i.e., m in Equation (2)) at 12, 24, 50, and 100. Such ranges of variances were chosen for including the SMOS brightness temperature uncertainties at 20–40 K shown in the literature [1,14,31,32,33]. This magnitude of brightness temperature variances takes into account the effects of calibration errors, a high vertical gradient condition of soil moisture or the water film arising from WAM rainfall events over West Africa, Radio-Frequency Interference (RFI), soil dryness, soil texture, and vegetation attenuation

Summary

Introduction

Data assimilation for merging satellite observations with model states is widely applied to provide the boundary conditions with high quality for Numerical Weather Prediction (NWP) model predictions, as well as to improve the spatial and temporal resolutions of satellite data [1,2] This method is preferred to model forecasting because models always rely on various assumptions to solve related equations and require a calibration for various input parameters. Gruhier et al [4] reported that L-band passive microwave satellite-retrieved soil moisture data produced large errors, especially in dry and sandy soils such as those in West Africa Under such conditions, data assimilation reproduces large errors while converging towards the erroneous satellite observations. It is essential to acquire high quality observations prior to data assimilation [6,7]

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Methods

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Conclusion