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MODIS Flood Mapping with Google Earth Engine
Rating: 5.0 out of 5(1 rating)
158 students

MODIS Flood Mapping with Google Earth Engine

Satellite Flood Mapping
Created byEarth's AI
Last updated 7/2025
English

What you'll learn

  • Access and process MODIS satellite data using Google Earth Engine, including filtering by date and region for flood monitoring purposes.
  • Calculate and interpret the Normalized Difference Water Index (NDWI) to identify water bodies and detect flood-affected areas.
  • Apply thresholding techniques on NDWI values to accurately detect and classify flooded regions.
  • Visualize, analyze, and export flood maps from Google Earth Engine for use in GIS software or decision-making workflows.

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Course content

1 section • 8 lectures • 1h 5m total length

  • Intro1:13

    This lecture series introduces remote sensing and its use in flood mapping with Google Earth Engine (GEE). It covers key topics including MODIS data, NDWI calculation, and GEE-based preprocessing. By the end, learners gain practical skills to build a complete flood mapping workflow using satellite data and cloud computing tools.


  • Lecture 1: Fundamentals of Remote Sensing13:17

    This lecture introduces the fundamentals of remote sensing, explaining how satellites collect data using reflected or emitted energy. You'll learn about key concepts like spectral bands, spatial and temporal resolution, and how these affect image quality. We’ll also explore major applications, including land monitoring, agriculture, and disaster response such as flood mapping.


  • Lecture 2: Introduction to Google Earth Engine (GEE) Basics of GEE platform8:45

    This lecture introduces Google Earth Engine (GEE), a powerful cloud-based platform for geospatial analysis. Students will explore its interface, including the Code Editor and Docs, and learn how to load, filter, and visualize satellite data. Basic JavaScript concepts for GEE scripting will also be covered to build foundational coding skills.


  • Lecture 3: Understanding MODIS Data for Flood Mapping8:21

    In this lecture, we explore the MODIS sensor and its role in flood mapping. You'll learn about its spatial and temporal resolution, key spectral bands useful for water detection, and important product

  • Lecture 4: Building NDWI and Preprocessing in GEE NDWI concept6:46

    In this lecture, you’ll learn the concept of NDWI and how to calculate it using MODIS bands in Google Earth Engine. We’ll cover essential preprocessing steps including data filtering by date and regio

  • Getting Started with the Google Earth Engine Interface3:38


    This section introduces the Google Earth Engine platform, guiding learners through accessing the Code Editor, understanding its interface, and exploring key panels like the script editor, map viewer, inspector, and data catalog. It helps beginners get comfortable navigating GEE before writing any code or performing analysis.


  • Lecture 5: Flood Mapping Workflow in GEE19:34


    In this lecture, you'll build a complete flood mapping workflow in the Google Earth Engine (GEE) environment. Step-by-step, we’ll write code to filter MODIS data, calculate NDWI, apply thresholding to detect flooded areas, and visualize the results. Finally, you’ll learn how to export the flood map from GEE for further analysis.





  • Extra Lecture: 2024 Brazil Flood Mapping4:20

    This bonus case study explores the severe flooding in Rio Grande do Sul, Brazil, during late April to May 2024. Using MODIS satellite data, we demonstrate flood mapping techniques, highlighting affected areas like Macaé and Santarém, and showcasing practical applications of remote sensing in disaster monitoring and management.

Requirements

  • No prior experience with Google Earth Engine is required — the course will guide you step-by-step.

Description

In this course, you will learn how to perform flood mapping using MODIS satellite data within the powerful cloud-based platform, Google Earth Engine (GEE). The journey begins with the fundamentals of remote sensing—covering essential concepts like spectral, spatial, and temporal resolution—and how satellite sensors such as MODIS observe Earth’s surface. You'll understand why MODIS is suitable for regional flood monitoring and how its high temporal frequency supports rapid response to natural disasters.

Next, you'll dive into Google Earth Engine, exploring its interface, scripting environment, and vast public data catalog. Step-by-step, you’ll learn how to access and load MODIS data, apply filters based on time and geography, and calculate the Normalized Difference Water Index (NDWI) to highlight water bodies. You'll use thresholding techniques to detect flooded areas, apply visualization tools to display results, and finally export your maps for use in reports or GIS software.

The course also introduces preprocessing steps like compositing and quality assurance filtering to improve reliability. You’ll practice writing JavaScript code in the GEE Code Editor, enabling efficient, reproducible analysis. By the end, you'll have built a complete flood detection workflow and gained the skills to adapt and apply it to different regions and flood events globally.


Who this course is for:

  • Students and researchers in agriculture, environmental science, geography, or remote sensing looking to apply satellite data in real-world scenarios.

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