Description

Floods are the most common and widespread of all weather-related natural disasters. Lasting from minutes to weeks, floods can place a huge burden on a community, including loss of life, damage to infrastructure and economies, and long-term impacts on human health and well-being. Remote sensing observations and Earth system models, when combined with hydrologic models, can support both flood monitoring and prediction efforts to significantly improve preparedness and response planning.

This three-part training includes an overview and demonstration of flood monitoring tools based on remotely sensed optical observations, NASA's near real-time Global Flood Product, and several Observational Products for End-users from Remote Sensing Analysis (OPERA). Additionally, the training introduces a streamflow prediction tool — the Group on Earth Observations Global Water Sustainability (GEOGLOWS) model — which provides global, historical, and 15-day streamflow predictions. Trainers guide participants through hands-on exercises to access and visualize selected flood data.

Prerequisites

• Fundamentals of Remote Sensing

Objectives

By the end of this training attendees will be able to:

• Identify datasets in NASA's Global Flood Product, which is based on remotely sensed optical observations.
• Use the webtool, NASA Worldview, to access and visualize flooded regions from Global Flood Product, available from multiple satellites, from 2021 to near-real time.
• Identify OPERA dynamic surface water extent data for flood detection derived from optical and SAR observations.
• Access and visualize OPERA dynamic surface water extent data for flood events using NASA Worldview.
• Identify the capabilities of GEOGLOWS River Forecast System (RFS) for global streamflow prediction.
• Use GEOGLOWS Hydroviewer to access globally available retrospective and predicted streamflow for selected rivers.

Target Audience

Local, regional, federal, and non-governmental organizations, as well as companies, involved in flood and water resources management, emergency response, disaster relief and rescue operations, flood insurance, and real estate and infrastructure building. 

Students and academics who are interested in learning about remote sensing of flood monitoring and predictions.

Course Format

• The complete course consists of three 1.5-hour parts, with Part 1 offered on June 18, Part 2 on June 23, and Part 3 on June 25.
• On each day, there are two opportunities to take the course (identical offerings):
  • Session A: 11:00 a.m. to 12:30 p.m. EDT (UTC-4)
  • Session B: 2:00 p.m. to 3:30 p.m. EDT (UTC-4)
• Each part includes a 30-minute live Q&A.
• Those who attend Parts 1, 2, and 3 and complete the homework by the due date will receive a certificate of attendance.

Part 1: Overview of Global Flood Product Derived from NASA Optical observations

June 18, 2026

• Introduction
• Overview of NASA's Global Flood Product
• Access and Visualize the Global Flood Product
• Summary and Q&A

ARSET Instructors

Amita Mehta

Guest Instructors

Dan Slayback, NRT Global Flood Product Research Scientist (NASA GSFC)

Materials

• Part 1 Presentation Slides (PDF, .04 MB)
• Part 1 Q&A Transcript (PDF, .04 MB)

Part 2: Overview of Monitoring Floods using OPERA Surface Water Extent based on Optical and SAR Observations

June 23, 2026

• Introduction
• Overview of OPERA Dynamic Surface Water Data Product 
• Visualize Dynamic Surface Water from Optical and SAR Imagery
• Summary and Q&A

ARSET Instructors

Amita Mehta

Guest Instructors

Renato Frasson (NASA JPL)

Materials

• Part 2 Presentation Slides (PDF, .04 MB)
• Part 2 Q&A Transcript (PDF, .04 MB)

Part 3: Overview of GEOGLOWS for Monitoring and Predicting Flood Risk

June 25, 2026

• Introduction
• Introduction to GEOGLOWS
• Access Streamflow Data using GEOGLOWS Hydroviewer
• Summary and Q&A

ARSET Instructors

Amita Mehta

Guest Instructors

Rachel Huber Magoffin (Aquaveo LLC) & Riley Hales (BYU Hydroinformatics Research Lab)

Materials

• Part 3 Presentation Slides (PDF, .04 MB)
• Part 3 Q&A Transcript (PDF, .04 MB)