Satellite radar altimetry is a powerful technique for measuring sea surface height variations. It has a wide range of applications in, e.g., operational oceanography or climate research. However, coastal sea-level change from satellite altimetry is challenging due to land influence on the estimated sea surface height (SSH), significant wave height (SWH), and backscatter. There exist various algorithms which allow retrieving meaningful estimates up to the coast. The Spatio Temporal Altimetry Retracker (STAR) algorithm partitions the total return signal into individual sub-signals, which are then processed, leading to a point cloud of potential estimates for each of the three parameters which tend to cluster around the true values, e.g., the real sea surface. The STAR algorithm interprets each point cloud as a weighted directed acyclic graph (DAG). The spatiotemporal ordering of the potential estimates induces a sequence of connected vertex layers, where each layer is fully connected to the next with weighted edges. The edge weights are based on a chosen distance measure between the vertices, i.e., estimates. Finally, the STAR algorithm selects the estimates by searching the shortest path through the DAG using forward traversal in topological order. This approach includes the inherent assumption that neighboring SSH, SWH, and backscatter estimates should be similar. A significant drawback of the original STAR approach is that the point clouds for the three parameters, SSH, SWH, and backscatter, can only be treated individually since the applied standard shortest path approach can not handle multiple edge weights. Hence, the output of the STAR algorithm for each parameter does not necessarily correspond to the same sub-signal, which prevents the algorithm from providing physically mutually consistent estimates of SSH, SWH, and backscatter. With mSTAR, we find coherent estimates that take the weightings of two or three point clouds into account by employing multicriteria shortest paths computation. An essential difference between the single and multicriteria shortest path problems is that there are, in general, a multitude of Pareto-optimal solutions in the latter. A path is Pareto-optimal if there is no other path that is strictly shorter for all criteria. The number of Pareto-optimal paths can be exponential in the input size, even if the considered graph is a DAG. There are different common ways to tackle this complexity issue. A simple approach is to use the weighted sum scalarization method. The objective functions are weighted and combined to a single objective function, such that a single criteria shortest path algorithm can find a Pareto-optimal path. However, even though different Pareto-optimal solutions can be obtained by varying the weights, it is usually impossible to find all Pareto-optimal solutions this way. In order to find all Pareto-optimal paths, label-correcting or label-setting algorithms can be used, which can also be speed-up using various approximation techniques. The mSTAR framework supports scalarization and labeling techniques as well as exact and approximate algorithms for computing Pareto-optimal paths. This way, mSTAR can find multicriteria consistent estimates of SSH, SWH, and backscatter.