Snow cover mapping for mountainous areas by fusion of MODIS L1B and geographic data based on stacked denoising auto-encoders

Xi Kan, Yonghong Zhang, Linglong Zhu, Liming Xiao, Jiangeng Wang, Wei Tian, Haowen Tan

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)


Snow cover plays an important role in meteorological and hydrological researches. However, the accuracies of currently available snow cover products are significantly lower in mountainous areas than in plains, due to the serious snow/cloud confusion problem caused by high altitude and complex topography. Aiming at this problem, an improved snow cover mapping approach for mountainous areas was proposed and applied in Qinghai-Tibetan Plateau. In this work, a deep learning framework named Stacked Denoising Auto-Encoders (SDAE) was employed to fuse the MODIS multispectral images and various geographic datasets, which are then classified into three categories: Snow, cloud and snow-free land. Moreover, two independent SDAE models were trained for snow mapping in snow and snow-free seasons respectively in response to the seasonal variations of meteorological conditions. The proposed approach was verified using in-situ snow depth records, and compared to the most widely used snow products MOD10A1 and MYD10A1. The comparison results show that our method got the best performance: Overall accuracy of 98.95% and F-measure of 73.84%. The results indicated that our method can effectively improve the snow recognition accuracy, and it can be further extended to other multi-source remote sensing image classification issues.

Original languageEnglish
Pages (from-to)49-68
Number of pages20
JournalComputers, Materials and Continua
Issue number1
Publication statusPublished - 2018
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Biomaterials
  • Modelling and Simulation
  • Mechanics of Materials
  • Computer Science Applications
  • Electrical and Electronic Engineering


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