This study presents a benchmark for evaluating visual correspondence algorithms in underwater environments using both optical and sonar imaging. It analyzes the transferability of state-of-the-art feature matching methods designed initially for terrestrial data, under the specific challenges of marine sensing. Experiments on real and simulated datasets assess their accuracy, robustness, and downstream impact on visual odometry and image mosaicing. The findings highlight key limitations in generalization and provide insights toward developing more trustworthy perception systems for autonomous underwater robots (This work was supported in part by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - project number 535678995.).