Satellite-based soil moisture data accuracies are of important concerns by hydrologists because they could significantly influence hydrological modelling uncertainty. Without proper quantification of their uncertainties, it is difficult to optimise the hydrological modelling system and make robust decisions. Currently, the satellite soil moisture data uncertainty has been limited to summary statistics with the validations mainly from the in-situ measurements. This study attempts to build the first error distribution model with additional higher order uncertainty modelling for satellite soil moisture observations. The methodology is demonstrated by a case study using SMOS (the Soil Moisture and Ocean Salinity) satellite soil moisture observations. The validation is based on soil moisture estimates from hydrological modelling which is more relevant to the intended data use than the in-situ measurements. Four probability distributions have been explored to find suitable error distribution curves using the statistical tests and bootstrapping resampling technique. General Extreme Value is identified as the most suitable one among all the curves. The error distribution model is still in its infant stage which ignores spatial and temporal correlations, and nonstationarity. Further improvements should be carried out by the hydrological community by expanding the methodology to a wide range of satellite soil moisture data using different hydrological models.
- error distribution model
- uncertainty modelling
- satellite soil moisture;hydrological modelling
- Soil Moisture and Ocean Salinity (SMOS)
- Xinanjiang (XAJ)