Synthetic Aperture Radar (SAR) Applications for Earth Observation

Learn the fundamentals of SAR imaging, interferometry, and polarimetry to analyze Earth observation data for environmental monitoring and disaster management.

โฑ 1 oras 50 min ๐Ÿ“š 6 aralin ๐ŸŽง Audio version

Tungkol sa kursong ito

Earth observation has been transformed by radar technology that can see through clouds and darkness. Understanding how to interpret and analyze Synthetic Aperture Radar (SAR) data is a highly valued skill in environmental science, disaster response, and urban planning. This text-only course guides you from the fundamental physics of radar remote sensing to advanced applications. You will learn to read and interpret SAR images, understand polarimetry, and explore interferometry techniques to detect surface changes, all through structured written explanations and step-by-step analytical exercises. What you'll learn: Understand the fundamental principles of radar remote sensing, backscatter, and SAR image acquisition; Interpret SAR intensity imagery to identify land cover types, water bodies, and urban structures; Explore polarimetric SAR (PolSAR) concepts to analyze surface roughness and scattering mechanisms; Grasp the basics of SAR interferometry (InSAR) for measuring ground deformation and topography; Apply modern Python-based open-source tools to process and visualize SAR datasets; Analyze real-world case studies in disaster mapping, forestry, and environmental change detection. We begin with the core physics of radar and how SAR sensors operate compared to optical satellites. From there, you will progress through polarimetry, interferometry, and modern digital processing workflows using open-source tools. This course is designed for beginners in remote sensing, GIS analysts, environmental scientists, and students looking to expand their spatial analysis toolkit. No prior experience with radar data is required. Start reading today to unlock the power of radar-based Earth observation.

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    1 oras 50 min ng practical content

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