Scale-dependent biases in systematic land cover maps undermine freshwater ecological assessment

Land cover is a key determinant of landscape structure and ecological processes across spatial and temporal scales. In freshwater ecosystems, where riparian and catchment land cover regulate hydrology, nutrient inputs, and habitat quality, most ecological assessments rely on systematically produced land cover maps whose reliability varies with scale and context. Despite their widespread use, the extent to which these products accurately represent landscape patterns and long-term dynamics at ecologically relevant scales remains poorly understood. Here, we evaluate the reliability of systematic land cover maps and assess whether Landsat-based supervised classification (SC) provides more accurate and temporally consistent representations of land cover patterns in draining catchments and riparian areas. We analysed five headwater catchments (< 70 km²) within Natura 2000 sites in northern Spain and reconstructed land cover dynamics over four decades (1984-2023) using Random Forest classification implemented in Google Earth Engine. SC results were compared with CORINE Land Cover, SIOSE, and the Spanish National Forest Inventory across three spatial scales, relevant for freshwater ecological processes: catchment, riparian corridor, and reach. SC achieved consistently high accuracy across all periods (mean overall accuracy = 87.8%), outperforming CORINE and SIOSE and matching NFI only for forest classes. Discrepancies between SC and systematic maps increased at finer spatial scales and varied with landscape context, particularly in heterogeneous riparian environments. Systematic maps also showed limited temporal consistency, frequently misrepresenting land cover trends relative to SC. These scale-dependent and context-specific biases can alter estimates of riparian vegetation and landscape composition and their temporal dynamics, potentially leading to misleading assessments of freshwater ecosystem condition. Our findings demonstrate that reliance on coarse-scale land cover products can distort ecological inference, particularly for freshwater ecosystems where ecological condition depends strongly on catchment and riparian landscape structure. Integrating reproducible, remote sensing-based classifications offers a practical pathway to improving the accuracy and interpretability of land cover indicators in landscape ecological research.