Flood mapping with optical and microwave satellite data: from indices to machine learning

Flood mapping is a critical component of disaster response and climate adaptation, but it is often hindered by cloud cover, ambiguous spectral signals, and trade-offs in image resolution. This tutorial introduces participants to practical methods for flood detection using freely available multi-sensor satellite data. We begin with optical imagery from MODIS and Sentinel-2, showing how spatial resolution affects the level of detail captured in flood maps. We then move to microwave data from Sentinel-1 SAR, which can penetrate clouds and provide more reliable monitoring during flood events. Participants will implement rule-based approaches such as spectral indices, band ratios, and Z-score anomalies, followed by machine learning models including Logistic Regression and Random Forests. Finally, we demonstrate how trained models can be applied to flood events in different parts of the world, highlighting both the opportunities and limitations of generalizing across geographies. By the end of the tutorial, participants will understand how different satellite data sources and modeling approaches influence flood mapping outcomes, and how these methods can support disaster response and long-term climate resilience.