Schedule
Course Outline
This course introduces principles of microwave satellite remote sensing and modern artificial intelligence methods for geophysical retrieval and environmental analysis. Students will build Python skills using Jupyter Lab and Colab while working through weekly coding notebooks and graded assignments. Core topics include microwave scattering and emission theory, radar and radiometer observations, GNSS reflectometry, feature extraction from satellite data, supervised and unsupervised learning, deep learning for image and time series data, and case studies in soil moisture, vegetation, and hydrometeorological monitoring. A midterm project proposal, final presentation, and report provide hands on experience applying these methods to real world satellite datasets. By the end of the course, students will be prepared to analyze, model, and interpret microwave remote sensing data with statistical rigor and AI based approaches..
Prerequisites
• Background in linear regression analysis, statistical inference, and linear algebra
• Basic working knowledge in a scientific programming language (for example Python or R)
• All course examples will be in Python
Textbooks and References
• Microwave Remote Sensing: Active and Passive, Volumes 1 to 3, by Ulaby, Moore, and Fung
• Remote Sensing of the Environment by Jensen
• An Introduction to Statistical Learning by James, Witten, Hastie, and Tibshirani
• Deep Learning by Goodfellow, Bengio, and Courville
• Additional course material and reading assignments will be provided via instructor notes and recent journal articles
• Anaconda
• Additional course material and reading assignments will be provided via instructor notes and recent journal articles.
Grading
• Attendance: 10%
• Assignments: 50%
• Project Proposal: 15%
• Final Project Report: 25%
Additional Information
• Course materials will be available on the class webpage.
• Pre-class assignments (reading and coding) are required to prepare for the lectures. Failure to complete these may result in a lower participation grade.
Contact
For more information or inquiries about the course, please contact Prof. Hyunglok Kim at hyunglokkim@gist.ac.kr.