Temporal and spectral classification of satellite-derived aridity reveals multi-scale environmental signals in southern South America

Abstract

Aridity strongly influences ecosystem productivity, land degradation, and dust emissions in drylands. In Southern South America (SSA), traditional assessments rely on mean-state changes, implicitly assuming monotonic trends and potentially overlooking temporal variability.Here, we analyze the dynamics of the Satellite-based Aridity Index (SbAI) over 2005–2025 using time-series and frequency-domain approaches. Pixels were classified using K-means++ and Adaptive Archetypes based on temporal behavior and segmented power spectra.Results reveal coherent spatial domains characterized by distinct temporal patterns and frequency sensitivities, which are not captured by mean-based analyses. Regions exhibit variability consistent with hydrological processes, climatic forcing, and anthropogenic influence across multiple timescales. These findings demonstrate that satellite-derived aridity indices encode multi-scale environmental signals, providing an integrated framework beyond static climatological approaches.