Beyond open water: mapping the spectral and hydrological limits of Pampean wetlands classification with Landsat time series

Conventional wetland mapping approaches using satellite remote sensing often exhibit a pronounced bias towards detecting open water bodies, while systematically underestimating vegetated wetlands and those with intermittent inundation regimes. This limitation becomes particularly acute in agricultural landscapes like Argentina’s Pampa region, where altered hydrology and vegetation patterns further complicate detection. Our study evaluates how wetland landscape configuration mediates classification performance across different sampling strategies, and assesses the capacity of the optimal approach to characterize wetland hydroperiods when compared against expert-derived maps. We applied Random Forest classification to the complete Landsat 5 and 8 archives (1984–2024) across six pilot sites representing distinct wetland patterns in Buenos Aires Province. A per-scene classification strategy was implemented to preserve ephemeral hydrological signals. The selected model (EG) demonstrated robust performance (AUC-ROC: 0.87) while revealing a critical limitation: wetlands with low wetness frequency (