Review article: Remote sensing methods for snowline dynamics detection in the Andes: a critical review

Abstract. Remote sensing has become a key tool for monitoring snowline dynamics in the Andes, yet cross-study comparison remains limited by heterogeneous definitions, sensors, classification schemes and spatial scales. Drawing on more than 60 studies covering satellite data from 1984 to 2025, spanning optical, SAR and hybrid approaches, and following a PRISMA-informed structure, this review examines how snowline-related metrics are defined, applied and interpreted in Andean research, with emphasis on local snowline altitude, snow persistence and catchment- to regional-scale snowline elevation. Andean applications rely predominantly on MODIS-derived snow products and snow-persistence approaches, whereas fewer studies exploit Landsat, Sentinel-2 or SAR workflows to derive transferable metrics or uncertainty-aware frameworks. Reported trends in the extratropical Andes (29° – 37° S) range from 10 to 30 m yr⁻¹, but these estimates are not directly comparable because they derive from different metrics, sensors, temporal windows and aggregation rules. Comparability is further constrained by inconsistent terminology, variable thresholding, limited treatment of digital elevation model and mixed-pixel effects, and scarce in situ validation. Rather than identifying a single optimal metric, the evidence indicates that different approaches suit different objectives: local metrics for event-scale analyses, snow persistence for climatological zonation, and regional snowline elevation for basin-to-regional trend assessment. We identify four priorities: standardized terminology and metric reporting; explicit propagation of uncertainty; systematic ground-truth validation; and stronger integration of SAR and cloud-computing workflows. Reframing snowline monitoring as a problem of metric selection, scale, uncertainty and application provides a basis for more reproducible and hydrologically meaningful assessments of Andean snowline dynamics.