Reevaluating the importance of simple elevation in wetland classification

Simple elevation, defined here as the unprocessed vertical height of the land surface above a reference datum without the application of derivative computations such as slope or curvature, is frequently cited as the most or among the most important predictor variables in wetland classification. However, importance is often measured using biased metrics and there have been numerous cases in which excluding simple elevation has had little to no impact on model performance. Additionally, although wetlands can form at any elevation, they are often more abundant and/or obvious at lower elevations. This can lead to biased training conditions that result in overfitting. To advance wetland mapping, it is essential to accurately quantify the importance of predictor variables and evaluate their impact on model generalization. The objectives of this study were therefore to re-evaluate the importance and highlight the potential risks associated with incorporating simple elevation into wetland classification models. Seasonal composites of C-band Sentinel-1 Synthetic Aperture Radar are classified in combination with three different simple elevation datasets to: (i) demonstrate a scenario in which, regardless of its quality, simple elevation offers limited predictive power to the model, and (ii) simulate biased training conditions to show how its inclusion can lead to overfitting. Results show that the correlation between wetland presence and elevation is spatially variable, limiting the ability of models to generalize across domains when elevation is included.