From Floods to Droughts: Exploring Surface Water with Satellite Data

  • New global data collection “SWIM – Surface Water Inventory and Monitoring” releases more than one million high-resolution water masks
  • Sentinel-1 and Sentinel-2 images are continuously being analyzed to extend the archive and ensure that the collection remains up to date
  • Focus: Space, earth observation, disaster response, climate change, big data, artificial intelligence

Rivers shift their course, reservoirs fill and empty, wetlands expand and contract, and floods can transform landscapes within hours. Monitoring these changes at regional and global scales is essential for disaster response, water resource management, environmental protection, and climate research.

Surface Water Inventory and Monitoring

To support these applications, we have developed a comprehensive archive of satellite-derived surface water maps that integrates observations from Sentinel-1 radar and Sentinel-2 optical imagery. This new collection, entitled “SWIM – Surface Water Inventory and Monitoring”, currently provides nearly three years (2024-2026) of global coverage, comprising more than one million high-resolution water masks at a 10 m grid spacing (Figure 1). New observations are continuously added to extend the archive and ensure that the collection remains up to date. For priority regions such as Europe, the archive already covers more than six years (2020-2026) of observations. In these regions, we operate a continuous monitoring service that processes every newly available satellite scene as soon as it is released.

Terrabyte and EOC-Geoservice as digital enabler

Large-scale processing of satellite images is done on terrabyte with a dedicated processing pipeline that uses pretrained convolutional neural networks for Sentinel-1 and Sentinel-2. Clouds and cloud shadows in Sentinel-2 images are masked using our UKIS Cloud Shadow MASK tool. The analysis-ready water products can be accessed through EOC-Geoservice via WMS, Direct Download and STAC-API. The STAC catalog makes these data easily searchable by location, time, and other metadata, simplifying access for researchers and operational users alike.

From research to application

By combining observations collected over many years, the archive highlights seasonal flooding, recurring wetland dynamics, reservoir fluctuations, and long-term changes in lakes and rivers (Figure 2). The archive also supports rapid emergency mapping. During flood events, newly acquired satellite images can be compared with historical observations to identify inundated areas, estimate impacts, and support disaster response (Figure 3). The EOC-Geoservice access points provide seamless integration of SWIM water products into ZKI Map Viewer for situational pictures in support of first responders. Furthermore, the same data enable drought monitoring, water resource assessment, ecosystem management, and climate research (Figure 4).

By combining long-term satellite observations with an open, searchable data catalog, SWIM transforms individual images into a continuously growing record of our planets changing surface water. As new Sentinel observations become available, the collection will continue to grow and support research and operational services in different applications.

Related Links

  • SWIM Water Extent
  • UKIS Cloud Shadow MASK
  • Bereczky, M., Wieland, M., Krullikowski, C., Martinis, S., and Plank, S. (2022). Sentinel-1-Based Water and Flood Mapping: Benchmarking Convolutional Neural Networks Against an Operational Rule-Based Processing Chain, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 2023-2036. DOI: 10.1109/JSTARS.2022.3152127
  • Martinis, S., Groth, S., Wieland, M., Knopp, L., Rättich, M. (2022). Towards a global seasonal and permanent reference water product from Sentinel-1/2 data for improved flood mapping. Remote Sensing of Environment, 278, 113077. DOI: 10.1016/j.rse.2022.113077
  • Wieland, M., Martinis, S. (2020). Large-scale surface water change observed by Sentinel-2 during the 2018 drought in Germany, International Journal of Remote Sensing, 41(12), 4740-4754. DOI: 10.1080/01431161.2020.1723817
  • Wieland, M., Martinis, S. (2019). A Modular Processing Chain for Automated Flood Monitoring from Multi-Spectral Satellite Data. Remote Sensing, 11(19), 2330. DOI: 10.3390/rs11192330