Epistemic and aleatoric uncertainty in optical vegetation trait retrieval: Concepts, Methods, and Outlook

Remote sensing of vegetation traits, such as leaf area index, chlorophyll content, and canopy nitrogen content, underpins assessments of ecosystem health, crop productivity, and climate impacts. Yet the uncertainty of these retrievals is often under-reported or ambiguously defined. This scoping review clarifies and operationalizes the distinction between aleatoric (sensor- and observation-driven, irreducible) and epistemic (model- and knowledge-driven, reducible) uncertainty, a conceptual framework that is only beginning to gain traction in vegetation-trait mapping. It highlights how both components originate and propagate through the full Earth-observation processing chain, from top-of-atmosphere radiance (L1) to surface reflectance (L2A) and vegetation traits (L2B), and how they can be consistently quantified and combined. We synthesize methodologies to quantify and, where possible, disentangle these contributions: analytical and Monte Carlo propagation for aleatoric error; Gaussian process regression, Bayesian neural networks, ensembles, and quantile-based methods for epistemic uncertainty; and their integration into retrieval frameworks such as hybrid approaches that couple radiative transfer models with machine learning regression algorithms. We further review diagnostics (coverage, scoring rules, reliability diagrams, probability-integral-transform histograms), out-of-distribution detection, and strategies to reduce epistemic uncertainty via active learning, domain adaptation, and improved priors and models. Looking ahead, upcoming optical ESA missions such as S2NG, FLEX, and CHIME place increasing emphasis on traceable uncertainty budgets and are expected to provide either per-pixel L2A uncertainty layers or the metadata required for their derivation. Such information will be critical for propagating measurement-driven (aleatoric) error into L2B trait products and for interpreting total predictive uncertainty, including prediction intervals. We advocate routine release of L2B uncertainty layers (components and totals) with transparent calibration, benchmarking, and interoperable metadata to support data assimilation, operational monitoring, and risk-aware decision-making.