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Offshore Wind Turbine Detection With Sentinel-1 And Gee
Published 7/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 51m | Size: 300 MB
Detect offshore wind turbines using Sentinel-1 SAR data and Google Earth Engine for scalable, cloud-based remote sensing
What you'll learn
Understand the basics of Synthetic Aperture Radar (SAR) data from Sentinel-1 and its advantages for offshore wind farm monitoring.
Use Google Earth Engine (GEE) to access, filter, and preprocess Sentinel-1 SAR imagery for targeted areas and timeframes.
Apply speckle noise reduction techniques, such as the Lee filter, and spectral thresholding to identify potential offshore wind turbine locations.
Export processed detection results as geospatial raster files for further analysis and visualization in GIS software.
Requirements
No prior experience with Google Earth Engine is required - the course will guide you step-by-step.
Description
Offshore wind energy is rapidly expanding as a sustainable solution to global energy needs. Monitoring and mapping these wind farms from space is essential for environmental management, maritime safety, and infrastructure planning. This course provides a comprehensive, practical introduction to detecting offshore wind turbines using Sentinel-1 Synthetic Aperture Radar (SAR) data in Google Earth Engine (GEE).You'll start by understanding the fundamentals of SAR remote sensing, including the benefits of radar imagery for all-weather, day-and-night observation. The course guides you through accessing and filtering Sentinel-1 data over your region of interest, focusing on the VV polarization channel ideal for detecting man-made structures like turbines.A key part of the workflow is reducing speckle noise inherent in radar data. You will implement the Lee filter to smooth the imagery while preserving important features. Using thresholding techniques, you will isolate turbine candidates based on their radar backscatter intensity relative to surrounding water.Beyond detection, you'll learn to visualize your results within GEE and export the turbine candidate maps as GeoTIFF files for integration with other GIS tools. The course emphasizes scalable, cloud-based analysis, enabling you to apply these techniques to different regions and time periods without heavy computing resources.This course is ideal for environmental scientists, geospatial analysts, and renewable energy professionals looking to harness satellite data for offshore wind farm monitoring and maritime spatial planning.
Who this course is for
Students, researchers and professionals in agriculture, environmental science, geography, or remote sensing looking to apply satellite data in real-world scenarios.
