Robust Trend Estimation from GOCE SGG Satellite Track Cross-Over Differences

Due to their outstanding accuracy and resolution, the innovative satellite gravity gradient (SGG) measurements from the European mission GOCE need dedicated calibration and validation procedures. It has been shown, that comparing the measurements in satellite track cross-overs offer an opportunity to get relative quality information, when applying a straight-forward reduction method. From SGG cross-over differences data sets, calibration parameters can be estimated. Here, the advantage of robust estimation methods over the standard least-squares approach for the analysis of short SGG cross-over data sets is shown. Simulated SGG data with different artificial trends are processed in different subsets. The least-squares approach is feasible to produce trend estimates from the cross-over differences of several days of measurements. Long-term trends can be estimated from cross-overs of a single revolution when applying robust estimation. Anticipating these estimation techniques, cross-over validation offers a fast approach to assess independently the quality of space gradiometry with focus on certain time intervals.