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Snow coverage mapping by learning from Sentinel-2 satellite multispectral images via machine learning algorithms

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posted on 2022-02-14, 11:28 authored by Yucheng Wang, Jinya Su, Xiaojun Zhai, Fanlin Meng, Cunjia LiuCunjia Liu
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.

Funding

Space-enabled Crop disEase maNagement sErvice via Crop sprAying Drones (SCENE-CAD)

Science and Technology Facilities Council

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History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

Remote Sensing

Volume

14

Issue

3

Publisher

MDPI AG

Version

  • VoR (Version of Record)

Rights holder

© the Authors

Publisher statement

This is an Open Access Article. It is published by MDPI under the Creative Commons Attribution 4.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/

Acceptance date

2022-02-01

Publication date

2022-02-08

Copyright date

2022

eISSN

2072-4292

Language

  • en

Depositor

Dr Cunjia Liu. Deposit date: 12 February 2022

Article number

782

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