Explainable Artificial Intelligence for Quality Estimation of MARSIS Observations

Planetary remote sensing missions are critical for advancing our understanding of extraterrestrial systems. They operate in highly uncertain environments where reliability and resolution are not always guaranteed, often compromising data analysis and scientific outcomes. In this paper, we consider the challenging task of estimating the quality of the signal acquired by MARSIS, the subsurface sounder aboard ESA's Mars Express mission, which aims to map the presence of liquid water beneath the Martian surface. Quality estimation has a strategic impact on the scheduling of MARSIS observations, since the radar operates with strict constraints that greatly limit the number and size of observation opportunities available per day. Thus, maximizing the quality of scheduled observations becomes a crucial factor in reducing resource utilization and increasing the coverage of the target areas in search of liquid water. To this end, in a previous research we proposed a predict-then-optimize approach, which included a neural network regressor to predict signal quality achievable by future observation opportunities, based on contextual features. In this work, we advance the methodology by applying explainable artificial intelligence techniques that allow domain experts to interpret the results, by enhancing the comprehension of the physical phenomena that have an impact on signal acquisition. Specifically, we applied a SHAP analysis to the neural network predictions and trained an Explainable Boosting Machine (EBM) to provide interpretable models. We then analyzed and compared the results with existing domain knowledge, uncovering promising new avenues for investigation and highlighting limitations in the current dataset construction.