Aim: This study aimed to assess the impact of spring snow persistence changes on vegetation dynamics in Khorasan Razavi Province, Iran, over 2001–2025.
Material & Method: MODIS NDVI (MOD13A2) and NDSI (MOD09A1) data were used in Google Earth Engine. NDSI was computed for spring (March–May) as a snow persistence proxy, NDVI for the growing season (April–October). Trends were assessed via modified Mann-Kendall and Sen’s slope; spatial patterns via pixel correlation, overlay of significant trends (four ecological classes), the Getis-Ord Gi* statistic, and hierarchical clustering.
Finding: Provincial mean NDVI and NDSI exhibited non-significant declining trends; however, spatial analyses revealed pronounced heterogeneity. The overall pixel-level NDSI–NDVI correlation was weak (r = –0.281), but the Spearman rank correlation (ρ = –0.443) indicated a monotonic nonlinear relationship. Negative correlation significantly weakened with elevation, from -0.69 in lowlands to -0.18 at high peaks (p < 0.001). Of the areas with concurrent trends (~9,028 km²), Class 4 (snow increase, vegetation decline) covered 29.6% and Class 1 (co-decline) for 27.3%, representing ~5,135 km² of critical degradation hotspots. Hot spots (weakening inverse link) concentrated in highlands; cold spots (strengthening) in lowlands. City clustering revealed three distinct groups with contrasting correlation behaviors.
Conclusion: Topography is a key modulator of the snow–vegetation relationship. Critical degradation zones (Classes 1 & 4) representing ~5,135 km² of potential critical degradation hotspots, while greening despite snow decline (Class 2, ~1,987 km²) may suggest unsustainable pressure on groundwater resources. This spatially explicit classification of ecological states provides a practical tool for adaptive water resource management and conservation prioritization.
Innovation: Innovations include overlay analysis of trends as four ecological classes, hot/cold spot mapping of the snow– vegetation link, and city clustering, yielding a novel spatially differentiated map to guide adaptive water management and conservation.