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The performance of the advanced synthetic aperture radar (ASAR) global mode (GM) surface soil moisture (SSM) data was studied over Australia by means of two widely used bivariate measures, the root-mean-square error (RMSE) and the Pearson correlation coefficient (R). By computing RMSE and R at multiple spatial scales and for different data combinations, we assessed how, and at which scales, the spatial sampling error, noise, and the choice of the reference data impact on RMSE and R. The results reveal large changes in RMSE and R with continental average values of 8% and 18% for the RMSE of relative soil moisture saturation and between 0.4 and 0.7 for R depending on the spatial scale of aggregation and the choice of reference data. The combined effect of noise and spatial sampling error accounted for a 79% RMSE increase at 1 km and predominated over the error due to the choise of the reference data also at 5 km scale. The effect of noise on RMSE strongly diminished at spatial scales ≥ km. By contrast, the impact of uncertainties in the reference data was larger on than on RMSE. This highlights the better potential of to estimate the benefit of observations prior to data assimilation. Based on our results, it is further suggested that a potential way for an improved ASAR GM SSM error assessment is to: 1) aggregate the data to ≥ km resolution to minimize the noise; 2) subtract the spatial sampling error within the coarse resolution footprint; and 3) remove the reference uncertainty using advanced techniques such as triple collocation.

The Water Observation and Information System (WOIS) is an open source software tool for monitoring, assessing and inventorying water resources in a cost-effective manner using Earth Observation (EO) data. The WOIS has been developed by, among others, the authors of this paper under the TIGER-NET project, which is a major component of the TIGER initiative of the European Space Agency (ESA) and whose main goal is to support the African Earth Observation Capacity for Water Resource Monitoring. TIGER-NET aims to support the satellite-based assessment and monitoring of water resources from watershed to cross-border basin levels through the provision of a free and powerful software package, with associated capacity building, to African authorities. More than 28 EO data processing solutions for water resource management tasks have been developed, in correspondence with the requirements of the participating key African water authorities, and demonstrated with dedicated case studies utilizing the software in operational scenarios. They cover a wide range of themes and information products, including basin-wide characterization of land and water resources, lake water quality monitoring, hydrological modeling and flood forecasting and mapping. For each monitoring task, step-by-step workflows were developed, which can either be adjusted by the user or largely automatized to feed into existing data streams and reporting schemes. The WOIS enables African water authorities to fully exploit the increasing EO capacity offered by current and upcoming generations of satellites, including the Sentinel missions.

The Sentinel-1 will carry onboard a C-band radar instrument that will map the European continent once every four days and the global land surface at least once every twelve days with finest 5 × 20. m spatial resolution. The high temporal sampling rate an