Technical note: Euclidean Distance Score (EDS) for algorithm performance assessment in aquatic remote sensing

Abstract. In the absence of community consensus, there remains a gap in standardized, consistent performance assessment of remote-sensing algorithms for water-quality retrieval. Although the use of multiple metrics is common, whether reported individually or combined into scoring systems, approaches are often constrained by statistical limitations, redundancy, and dataset- and context-dependent normalizations, leading to subjective or inconsistent interpretations. To address this, we propose the Euclidean Distance Score (EDS), which integrates five statistically appropriate and complementary metrics into a composite score. Capturing three core aspects of performance (regression fit, retrieval error, and robustness), EDS is computed as the Euclidean distance from an idealized point of perfect performance, providing a standardized and interpretable measure. We demonstrate the applicability of EDS in three scenarios: assessing a single algorithm for different retrieved variables, comparing two algorithms on shared retrievals, and evaluating performance across contrasting trophic conditions. By offering an objective framework, EDS supports consistent validation of aquatic remote sensing algorithms and transparent comparisons in varied contexts.