2026/01/01 by Chen Xin, Yongwei Sheng
Earth and Planetary Sciences · Environmental Science · #Cryospheric studies and observations #Remote Sensing in Agriculture #Meteorological Phenomena and Simulations
paper · pdf · doi:10.1017/jog.2026.10174
Abstract Regional-scale glacier mass-balance (MB) estimates are essential for understanding the impacts of climate change on alpine hydrology but remain challenging to obtain. Remotely sensed phenology metrics, such as the cumulative melting index (CMI) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data, are increasingly used for MB estimation; however, the strength of this relationship varies across glaciers. This study analyzes the relationship between the MODIS-derived CMI and glacier annual MB across 88 alpine glaciers globally and investigates sources of variability. By standardizing CMI values, we reduce interglacier variability in CMI–MB relationship. Principal component analysis (PCA) and Gaussian mixture model are employed to classify glaciers into six climate-based clusters. Cluster-specific linear regression models improve MB estimation accuracy. When glacier predictions from the cluster-specific models are combined across all 88 glaciers worldwide, the overall root-mean-square error is 511.0 mm w.e., compared with 571.4 mm w.e. using a single regression model. Applied to 616 glaciers in the European Alps, the model estimates a 20 year (2002–21) annual average mass loss of −981.9 ± 11 mm w.e. a −1 . These findings highlight the potential of MODIS-derived phenology metrics for scalable glacier MB estimation and emphasize the influence of regional climatic controls on glacier sensitivity to climate variability.