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Drought Risk Mapping Using Remote Sensing and GEE
Rating: 4.5 out of 5(1 rating)
13 students

Drought Risk Mapping Using Remote Sensing and GEE

Master the use of satellite remote sensing data and Google Earth Engine to analyze, map, and monitor drought risk
Created byEarth's AI
Last updated 8/2025
English

What you'll learn

  • Analyze drought risk factors using remote sensing data and understand their impact on agriculture.
  • Process, filter, and visualize multi-source satellite data effectively in Google Earth Engine.
  • Calculate, normalize, and interpret various drought risk indices using NDVI, rainfall, and soil moisture data.
  • Develop, export, and utilize detailed drought risk maps to support informed agricultural decision-making and resource management.

Course content

1 section • 6 lectures • 1h 4m total length

  • Lecture 1: Fundamentals of Remote Sensing13:19

    This lecture introduces the basic principles of remote sensing, including how satellites capture Earth data. You will learn about electromagnetic spectrum, sensor types, and data acquisition. Understanding these fundamentals is essential for analyzing environmental phenomena such as drought. This session lays the groundwork for interpreting satellite imagery, which is critical for effective drought risk assessment and agricultural monitoring.

  • Lecture 2: Remote Sensing for Risk Mapping10:54

    Explore how remote sensing is used for environmental risk mapping, focusing on drought hazards. This lecture covers techniques to identify vulnerable areas using satellite data, including vegetation health, rainfall patterns, and soil moisture. You’ll learn how risk factors are detected and mapped to provide actionable insights for decision-makers to mitigate drought impacts.

  • Lecture 3: Sentinel-2 Satellite and Data4:51

    Get to know the Sentinel-2 satellite, its spectral bands, spatial resolution, and how it captures vital data for vegetation and water analysis. This lecture details how Sentinel-2’s imagery is processed and utilized in drought monitoring, emphasizing NDVI and other vegetation indices essential for assessing crop health and drought stress.

  • Lecture 4: Introduction to Google Earth Engine (GEE)9:25

    This session introduces Google Earth Engine, a powerful cloud-based platform for processing large-scale satellite data. Learn the basics of GEE’s interface, coding environment, and data catalog. You’ll gain skills to manipulate datasets, perform analysis, and visualize drought-related variables efficiently.

  • 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: Implementing Drought Risk Mapping in GEE22:35

    In this practical lecture, apply your knowledge to create drought risk maps using Google Earth Engine. Learn to integrate NDVI, rainfall, and soil moisture datasets, calculate anomalies, normalize data, and generate drought risk indices. By the end, you’ll export actionable maps to support agricultural drought management.

Requirements

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

Description

Drought poses a significant threat to global agriculture, impacting food security, water resources, and livelihoods. This course equips you with cutting-edge skills to analyze and map drought risk using remote sensing data and the Google Earth Engine platform. Beginning with the fundamentals of remote sensing, you’ll gain a solid understanding of how satellites like Sentinel-2 capture essential data on vegetation health and soil moisture.

The course then delves into remote sensing applications for environmental risk mapping, with a special focus on drought. You will learn to process time-series satellite images, calculate vegetation indices such as NDVI, and assess rainfall and soil moisture anomalies by comparing current data with historical baselines.

Next, you will be introduced to Google Earth Engine—a cloud-based platform that simplifies large-scale data analysis. You’ll get hands-on experience scripting in GEE to integrate multiple datasets and develop comprehensive drought risk indices. The course also covers normalization techniques and anomaly detection to identify areas of elevated drought risk accurately.

In the final modules, you will implement a complete drought risk mapping workflow in GEE. This includes selecting the area of interest, processing seasonal data, combining multiple drought indicators, and visualizing results with effective color palettes. The course culminates with exporting high-quality maps for use in agricultural planning and disaster management.

By the end of the course, you will confidently apply remote sensing and GEE tools to support drought monitoring initiatives, helping mitigate the impacts of drought on agriculture and natural resources.

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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