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<p>This study introduces a novel approach that combines satellite Synthetic Aperture Radar (SAR) measurements with an Artificial Neural Network (ANN) to monitor long-term glacier surface velocities. We focus on the Bara Shigri Glacier in the western Himalayas – a region with limited prior velocity observations – over a six-year period (2017–2023). Using 24 seasonal and 6 annual Sentinel-1 SAR image pairs, we derived glacier velocity maps via sub-pixel offset tracking, and we integrated meteorological variables (2-m air temperature, surface skin temperature, and 0–7&#xa0;cm soil temperature from ECMWF reanalysis) as inputs to an ANN model. We observed a peak seasonal mean velocity of 8.64&#xa0;m season⁻¹ (during the April–July 2019 interval) and Interannually, we observed a notable decline in mean velocity from 2017 to 2021, followed by a partial recovery, reflecting the glacier’s dynamic response to climate forcing. Results demonstrate significant seasonal velocity variations, with summer flows approximately 40% faster than winter velocities. While a single train–test split suggested high apparent accuracy (R² = 0.97), 6-fold cross-validation gave weaker generalisation, highlighting overfitting risks given the small dataset. Accordingly, the ANN is presented as a proof-of-concept linking temperature and velocity, rather than a robust predictive tool. This work demonstrates the efficacy of combining SAR remote sensing with machine learning for glacier monitoring, offering a new framework to assess glacier dynamics under changing climatic conditions. Aligned with SDG 13 (Climate Action), this proof-of-concept highlights a potentially scalable pathway for operational glacier monitoring that could support adaptation planning once validated more broadly.</p>

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Temperature-driven surface velocity of the Bara Shigri glacier: a proof-of-concept integration of SAR observations and ANN modelling

  • 期刊论文

  • 发表日期：

    2025年11月8日

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  • Vansheika,
  • Chander Prakash,
  • Anita Sharma

摘要

This study introduces a novel approach that combines satellite Synthetic Aperture Radar (SAR) measurements with an Artificial Neural Network (ANN) to monitor long-term glacier surface velocities. We focus on the Bara Shigri Glacier in the western Himalayas – a region with limited prior velocity observations – over a six-year period (2017–2023). Using 24 seasonal and 6 annual Sentinel-1 SAR image pairs, we derived glacier velocity maps via sub-pixel offset tracking, and we integrated meteorological variables (2-m air temperature, surface skin temperature, and 0–7 cm soil temperature from ECMWF reanalysis) as inputs to an ANN model. We observed a peak seasonal mean velocity of 8.64 m season⁻¹ (during the April–July 2019 interval) and Interannually, we observed a notable decline in mean velocity from 2017 to 2021, followed by a partial recovery, reflecting the glacier’s dynamic response to climate forcing. Results demonstrate significant seasonal velocity variations, with summer flows approximately 40% faster than winter velocities. While a single train–test split suggested high apparent accuracy (R² = 0.97), 6-fold cross-validation gave weaker generalisation, highlighting overfitting risks given the small dataset. Accordingly, the ANN is presented as a proof-of-concept linking temperature and velocity, rather than a robust predictive tool. This work demonstrates the efficacy of combining SAR remote sensing with machine learning for glacier monitoring, offering a new framework to assess glacier dynamics under changing climatic conditions. Aligned with SDG 13 (Climate Action), this proof-of-concept highlights a potentially scalable pathway for operational glacier monitoring that could support adaptation planning once validated more broadly.