Snow Coverage Mapping by Learning from Sentinel-2 Satellite Multispectral Images via Machine Learning Algorithms

Yucheng Wang, Jinya Su*, Xiaojun Zhai, Fanlin Meng, Cunjia Liu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)
4 Downloads (Pure)


Snow coverage mapping plays a vital role not only in studying hydrology and climatology, but also in investigating crop disease overwintering for smart agriculture management. This work investigates snow coverage mapping by learning from Sentinel-2 satellite multispectral images via machine-learning methods. To this end, the largest dataset for snow coverage mapping (to our best knowledge) with three typical classes (snow, cloud and background) is first collected and labeled via the semi-automatic classification plugin in QGIS. Then, both random forest-based conventional machine learning and U-Net-based deep learning are applied to the semantic segmentation challenge in this work. The effects of various input band combinations are also investigated so that the most suitable one can be identified. Experimental results show that (1) both conventional machine-learning and advanced deep-learning methods significantly outperform the existing rule-based Sen2Cor product for snow mapping; (2) U-Net generally outperforms the random forest since both spectral and spatial information is incorporated in U-Net via convolution operations; (3) the best spectral band combination for U-Net is B2, B11, B4 and B9. It is concluded that a U-Net-based deep-learning classifier with four informative spectral bands is suitable for snow coverage mapping.

Original languageEnglish
Article number782
Number of pages19
JournalRemote Sensing
Issue number3
Publication statusPublished - 8 Feb 2022

Bibliographical note

This research was funded by UK Science and Technology Facilities Council (STFC) under Newton fund with Grant ST/V00137X/1.

Data Availability Statement

The dataset in this study will be openly shared upon publication at, accessed on 31 December 2021.


  • snow coverage
  • sentinel-2 satellite
  • remote sensing
  • multispectral image
  • random forest
  • u-net
  • semantic segmentation


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