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

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 m 3 /m 3 for CDF matching to 0.0678 m 3 /m 3 for the proposed ensemble approach. In Niger, the RMSEs decreased from 0.14 m 3 /m 3 for CDF matching to 0.045 m 3 /m 3 for the ensemble approach.

Stochastic Bias Correction and Uncertainty Estimation of Satellite-Retrieved Soil Moisture Products

“…However, recent studies showed that a small ensemble size at 12-20 is sufficient to provide the optimal estimates [52]. If perturbing different error sources including land surface heterogeneity, meteorological event, satellite measurement, and parameter inversion errors with the same ensemble size, the resultant ensembles indicate a different quantitative measure of retrieval errors.…”

“…On the other hand, CDF matching in Figure 5 increased the RMSEs of the original SMOS soil moisture, as CDF matching shifted the original SMOS soil moisture towards reference data exposed to their own intrinsic errors (i.e., the lowest limit of soil moisture is set at wilting point for calculating bare soil evaporation so that model did not appropriately simulate soils in extremely dry conditions drier than the wilting point [106]. Figure 5 shows that the retrieval ensemble mean corrected footprint scale wet biases of the SMOS soil moisture in semi-arid region [52]. Ensembles were generated from a random perturbation of brightness temperature with an ensemble size of 12.…”

“…The CDF matching has been widely used for bias correction of satellite soil moisture data [52]. Fundamentally, it matches the cumulative distribution function of satellite data with a long record of climatology from reference data.…”

“…There are two different approaches in retrieval ensembles: (1) the deterministic approach to assemble multiple retrieval models [63]; and (2) the probabilistic approach to process various retrieval input data with the same retrieval algorithm, where the input errors are stochastically defined in a form of PDF (Probability Density Function) [48,52,64]. These are also called PDF methods.…”

“…As it is effective in rescaling satellite data towards the model estimates, it is employed as a bias correction prior to data assimilation. However, there are some limitations as a bias correction [52]. For example, an instantaneously or seasonally dynamic variation of rescaling parameters or retrieval errors is ignored [53].…”