Magray et al. (2026) Remote sensing-based assessment of albedo changes on benchmark glaciers in the Western Himalaya, India, between 2001 and 2022 using Google Earth Engine: implications for glacier mass loss

Identification

• Journal: Environmental Earth Sciences
• Year: 2026
• Date: 2026-01-07
• Authors: Seerat Magray, Sami Ullah Bhat, Irfan Rashid
• DOI: 10.1007/s12665-025-12749-5

Research Groups

• Department of Geoinformatics, University of Kashmir, Srinagar, Jammu and Kashmir, India
• Department of Environmental Science, University of Kashmir Hazratbal, Srinagar, Jammu and Kashmir, India

Short Summary

This study assessed the spatiotemporal variability of surface albedo on four benchmark glaciers in the Western Himalaya between 2001 and 2022 using satellite data, revealing a strong inverse correlation between reduced albedo and increased glacier surface lowering, underscoring albedo's significant role in glacier mass loss.

Objective

• Analyze glacier-wide albedo variations from 2001 to 2022 using satellite data for benchmark glaciers in Kashmir and Trans-Himalayan Ladakh.
• Investigate the relationship between albedo variations and glacier surface lowering.

Study Configuration

• Spatial Scale: Four benchmark glaciers (Kolahoi, Machoi, Parkachik, Drang Drung) in the Western Himalaya (Kashmir and Trans-Himalayan Ladakh, India). Data resolutions: MODIS (500 m), Landsat-8 OLI (30 m), glacier elevation change data (100 m).
• Temporal Scale: 2001–2022 for MODIS albedo; 2014–2022 for Landsat-8 OLI albedo; 2000–2019 for surface lowering data.

Methodology and Data

• Models used: Mann-Kendall test for trend significance. Narrowband-to-broadband conversion for Landsat-8 OLI albedo calculation.
• Data sources:
  • Satellite: MODIS Terra Snow Cover Daily L3 Global 500 m gridded product (MOD10A1), Landsat-8 OLI.
  • Platform: Google Earth Engine (GEE) for cloud-based geospatial analysis.
  • Reanalysis/Derived: Previously published geodetic surface lowering data from Hugonnet et al. (2021) (derived from ASTER stereo-images and TanDEM-X).

Main Results

• Glacier albedo varied significantly across time and space; Drang Drung showed the highest mean albedo (55% MODIS, 65.3% Landsat-8 OLI), while Machoi recorded the lowest (46.2% MODIS, 52% Landsat-8 OLI), likely due to debris cover and slope.
• Seasonal analysis revealed a marked decline in albedo during summer and fall (lowest: Machoi, 32.2% in summer) and higher albedo in winter and spring (highest: Drang Drung, 68.8% in spring).
• Landsat-8 OLI-derived albedo values were consistently higher than MODIS-derived values, with overall biases ranging from 4.6% to 11% across the glaciers.
• A strong inverse correlation was observed between annual surface lowering and albedo for most glaciers (e.g., Parkachik: -0.8, Drang Drung: -0.2), indicating that reduced albedo accelerates ice melt and contributes to surface lowering.
• The strongest seasonal negative correlations between surface lowering and albedo were observed in summer for Parkachik (-0.83) and Drang Drung (-0.89), in fall for Machoi (-0.94), and in spring for Kolahoi (-0.85).

Contributions

• Provides a comprehensive, long-term (2001-2022) remote sensing-based assessment of albedo variability and its implications for glacier surface lowering on benchmark glaciers in the Western Himalaya.
• Highlights the substantial role of albedo as a key driver of glacier mass loss, complementing the understanding of climate warming impacts.
• Demonstrates the utility of cloud-based platforms (GEE) for analyzing multi-source satellite data (MODIS, Landsat-8 OLI) for glacier dynamics.
• Quantifies the biases between MODIS and Landsat-8 OLI derived albedo, contributing to better interpretation of multi-sensor glacier research.

Funding

• Department of Science and Technology, Government of India (DST, GoI) for the WOS-A fellowship (Grant Number: EA/21/2021) to Seerat Magray.

Citation

@article{Magray2026Remote,
  author = {Magray, Seerat and Bhat, Sami Ullah and Rashid, Irfan},
  title = {Remote sensing-based assessment of albedo changes on benchmark glaciers in the Western Himalaya, India, between 2001 and 2022 using Google Earth Engine: implications for glacier mass loss},
  journal = {Environmental Earth Sciences},
  year = {2026},
  doi = {10.1007/s12665-025-12749-5},
  url = {https://doi.org/10.1007/s12665-025-12749-5}
}