This technical course is designed for environmental scientists, GIS professionals, researchers, and students who want to build expertise in satellite-based flood monitoring using Sentinel-2 and the Google Earth Engine (GEE) platform. The course combines theoretical knowledge with hands-on practice to develop a deep understanding of remote sensing applications in flood detection and mapping.
You will begin by exploring the fundamentals of optical remote sensing, including how satellite sensors like Sentinel-2 capture Earth’s surface data in multiple spectral bands. Special focus is given to bands B3 (green), B8 (near-infrared), and indices such as NDWI (Normalized Difference Water Index), which are crucial for detecting surface water and differentiating flooded areas.
The course then introduces GEE, a cloud-based geospatial processing platform that allows for rapid access and analysis of satellite imagery without the need for downloading large datasets. You will learn how to create composites, apply spectral indices, detect changes between pre-flood and post-flood periods, and export flood maps as GeoTIFFs for further use.
Using real-world case studies like the 2023 flood event in Bangladesh, you’ll implement flood detection algorithms using Sentinel-2 surface reflectance imagery. You will learn how to set cloud filters, clip areas of interest, and define threshold-based water classification rules to isolate flooded regions effectively.
By the end of the course, you will be able to independently design, implement, and visualize flood monitoring projects using freely available satellite data and open-source tools—skills that are in high demand in both academic and disaster response sectors.
- Students, researchers and professionals in agriculture, environmental science, geography, or remote sensing looking to apply satellite data in real-world scenarios.
