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Chapter 2: Mapping Snow-Covered Area (SCA) Using Machine Learning and High-Resolution Satellite Imagery

Snow is an important component in the Earth’s hydrological and energy cycles. This Jupyter book focuses on mapping snow-covered areas (SCA) using machine learning models and high-resolution satellite imagery, particularly from Planet small satellites. The chapter covers the challenges of SCA mapping, the use of machine learning models, and provides a workflow and tutorial materials for educational purposes. All scripts and datasets are available in the accompanying GitHub Repository.

Introduction

  • Significance of SCA in Earth's processes and climate change
  • Limitations of mapping SCA with optical satellite imagery
  • Potential of high-resolution satellite imagery from Planet small satellites
  • Machine Learning Approach for SCA Mapping:

Application of machine learning models to overcome challenges

  • Data preparation, parameter tuning, model selection, and evaluation
  • Workflow and Tutorial Materials:

Detailed workflow for mapping SCA using machine learning and high-resolution satellite imagery

  • Step-by-step instructions and tutorial materials
  • Scripts and datasets available in the GitHub Repository
  • To access the full content of the Jupyter book and explore SCA mapping with machine learning, visit the GitHub Repository. Enhance your understanding of snowpack dynamics and contribute to the advancement of Earth science.

Detailed jupyter book can be accessed here