Automated Delineation of Supraglacial Debris Cover Using Deep Learning and Multisource Remote Sensing Data

Saurabh Kaushik*, Tejpal Singh, Anshuman Bhardwaj, P. K. Joshi, Andreas J. Dietz

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)
2 Downloads (Pure)


High-mountain glaciers can be covered with varying degrees of debris. Debris over glaciers (supraglacial debris) significantly alter glacier melt, velocity, ice geometry, and, thus, the overall response of glaciers towards climate change. The accumulated supraglacial debris impedes the automated delineation of glacier extent owing to its similar reflectance properties with surrounding periglacial debris (debris aside the glaciated area). Here, we propose an automated scheme for supraglacial debris mapping using a synergistic approach of deep learning and multisource remote sensing data. A combination of multisource remote sensing data (visible, near-infrared, shortwave infrared, thermal infrared, microwave, elevation, and surface slope) is used as input to a fully connected feed-forward deep neural network (i.e., deep artificial neural network). The presented deep neural network is designed by choosing the optimum number and size of hidden layers using the hit and trial method. The deep neural network is trained over eight sites spread across the Himalayas and tested over three sites in the Karakoram region. Our results show 96.3% accuracy of the model over test data. The robustness of the proposed scheme is tested over 900 km2 and 1710 km2 of glacierized regions, representing a high degree of landscape heterogeneity. The study provides proof of the concept that deep neural networks can potentially automate the debris-covered glacier mapping using multisource remote sensing data.

Original languageEnglish
Article number1352
Number of pages19
JournalRemote Sensing
Issue number6
Early online date10 Mar 2022
Publication statusPublished - 10 Mar 2022

Bibliographical note

Funding: S.K. acknowledges funding from the DST-India via INSPIRE fellowship scheme (DST/INSPIRE Fellowship/2017/IF170680). S.K. acknowledges funding from Deutscher Akademischer Austauschdienst (DAAD) funding ID 2021/22 (57552338).


  • Climate change
  • Debris cover glacier
  • Deep neural network
  • Himalaya
  • Remote sensing
  • SAR
  • Semantic segmentation


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