Sequential Ensembles Tolerant to Synthetic Aperture Radar (SAR) Soil Moisture Retrieval Errors

Abstract

Due to complicated and undefined systematic errors in satellite observation, data assimilation integrating model states with satellite observations is more complicated than field measurements-based data assimilation at a local scale. In the case of Synthetic Aperture Radar (SAR) soil moisture, the systematic errors arising from uncertainties in roughness conditions are significant and unavoidable, but current satellite bias correction methods do not resolve the problems very well. Thus, apart from the bias correction process of satellite observation, it is important to assess the inherent capability of satellite data assimilation in such sub-optimal but more realistic observational error conditions. To this end, time-evolving sequential ensembles of the Ensemble Kalman Filter (EnKF) is compared with stationary ensemble of the Ensemble Optimal Interpolation (EnOI) scheme that does not evolve the ensembles over time. As the sensitivity analysis demonstrated that the surface roughness is more sensitive to the SAR retrievals than measurement errors, it is a scope of this study to monitor how data assimilation alters the effects of roughness on SAR soil moisture retrievals. In results, two data assimilation schemes all provided intermediate values between SAR overestimation, and model underestimation. However, under the same SAR observational error conditions, the sequential ensembles approached a calibrated model showing the lowest Root Mean Square Error (RMSE), while the stationary ensemble converged towards the SAR observations exhibiting the highest RMSE. As compared to stationary ensembles, sequential ensembles have a better tolerance to SAR retrieval errors. Such inherent nature of EnKF suggests an operational merit as a satellite data assimilation system, due to the limitation of bias correction methods currently available.

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

“…This error range is fundementally different from globally averaged climatology errors estimated over nominal pixels. For backscattering errors, Mattia et al [70] and Lee [61] previously suggested 0.5 to 2 dB.…”

“…We consider it as the ensembles, which are defined as multiple idealizations of "virtual" Figure 3. Nonlinear error propagation of roughness to SAR soil moisture [61]: ASAR backscattering differently retrieved soil moisture products under four roughness conditions indicated in Table. Only scheme #4 is outside of an optimal roughness range.…”

Vadose Zone Modeling in a Small Forested Catchment: Impact of Water Pressure Head Sampling Frequency on 1D-Model Calibration

“…The effectiveness of the calibrations was evaluated through the objective functions value and information indicators such as the Akaike information criterion (AIC) and Bayesian information criterion (BIC) [59,60]. The AIC and BIC provide a measure of the relative quality of a model for a given set of data, which considers the number of data, number of model parameters, and the value of the objective function: (12) where N p is the amount of parameters, N inv is the amount of data used in the inverse problem, and O is the objective function value after minimization. The smaller the criterion value, the better the calibration.…”

“…), is soil moisture [9]. Different methods may be used to estimate the soil moisture, which mainly include in situ methods [10], satellite-based data retrieval [11][12][13], and hydrological model based results [14]. The various available methods are generally combined to take advantage of their respective assets [15][16][17], as temporal-spatial scale issues and human-economic means dedicated to monitoring are important limiting factors.…”