Join the Global Flood Partnership May 2026 Webinar Series "Advancing Global Flood Mapping with Earth Observation"! This event will feature three exciting presentations from distinguished experts in the field, focusing on Earth Observation data.

• Registration required: https://cuboulder.zoom.us/meeting/register/u2HFsauLQkmRxHMb3uqVug
• When: May 7th, 2026, 4:00–5:30 p.m. CET

This season’s series of the Global Flood Partnership (GFP) focuses on the use of Earth Observation data to strengthen adaptation, mitigation, and resilience strategies for flood disasters. The webinar will feature three distinguished speakers:

• Dr. Renato Prata de Moraes Frasson (Jet Propulsion Laboratory - NASA)

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• Dr. Jonathan Giezendanner (Massachusetts Institute of Technology (MIT)) & Alex Saunders (University of Arizona)  

  Title: Global daily fractional water detection with VIIRS satellite imagery and deep learning

  Abstract: NASA’s Global Flood Product has provided near real-time daily flood maps since 2012 using the Moderate Resolution Imaging Spectroradiometer (MODIS). With MODIS’s impending retirement, NASA introduced in 2025 a successor flood product using the Visible Infrared Imaging Radiometer Suite (VIIRS). Building on this MODIS–VIIRS transition and motivated by methodological advances largely untested in satellite production systems of public agencies, we developed VIIRS Machine Learning Fractional Water Detection (ML-FWD): a deep learning algorithm that estimates fractional water extent from 375 m surface reflectance imagery. Trained on over 95,000 global water observations from the Dynamic World Sentinel-2 land-cover classification, ML-FWD achieves a test R2 of 0.83 with minimal bias (mean error of -0.6% water extent) and consistent performance across 14 global biomes. Importantly, ML-FWD offers improvements in daily global flood mapping; across 100 flood events, we outperform existing VIIRS algorithms from NASA and NOAA by 0.27 and 0.12 in absolute R2—a 141% and 35% relative improvement, respectively. ML-FWD also reduces the likelihood of false-positive water detections; on expert visual inspection of 19 scenarios that commonly challenge optical satellite methods, ML-FWD achieves a mean score of 3.1 (on a scale of 1–4, from poor to great), compared with 2.1 for the NASA algorithm. Our study demonstrates that combining deep learning and medium resolution observations greatly improves surface water extent estimates from moderate-resolution sensors such as VIIRS. Upon NASA’s approval, public deployment of ML-FWD will make the updated NASA Global Flood Product the first ML-based, routinely generated global flood dataset with daily coverage and the first public near real-time flood monitoring at NASA that uses ML. This anticipated update will ensure the public has access to accurate information with daily revisit and short latency, critical for flood monitoring, response, and relief across a broad range of sectors.

• Professor Cynthia Gerlein-Safdi (University of California, Berkeley)

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  Abstract: Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.