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Remote Sensing Using Google Earth Engine

Google Earth Engine in action

This two-day workshop will enable you to use Google Earth Engine API to perform basic digital image processing of remotely sensed imagery and perform more advanced geospatial analysis. Users who complete this course will have an intermediate understanding of the Google Earth Engine coder API and will be able to understand the capabilities and limitations of the Google Earth Engine. Be prepared for the course using the following requirements.

Learning Objectives

  • Define and describe remote sensing and explain its applications and history
  • Describe basic characteristics of remote sensing imagery
  • Find and visualize remotely sensed imagery in Google Earth Engine
  • Understand the difference between remotely sensed datasets based on sensor characteristics and how to choose an appropriate remotely sensed dataset based on these concepts
  • Identify image processing operators that may be useful in extracting information of interest for your image analyses
  • Implement spectral indices and transforms to accentuate the information of interest in your study area
  • Perform supervised classification and regression in Earth Engine
  • Perform analysis of multi-temporal data for determining trend and seasonality on a per-pixel basis

Course Format

The hands-on workshop, including brief lectures followed by intensive “follow me” coding labs and group discussions.

Topics Covered 

  • Remote sensing basics, history and applications
  • Javascript Syntax and Code Editor Use
  • Querying and filtering data collections, including imagery and feature collections
  • Loading, and visualizing imagery
  • Digital image processing, including image smoothing, sharpening, edge detection, morphological processing, texture analysis, resampling and re-projection, masking and cropping
  • Writing and mapping functions over collections
  • Implementing vegetation, water, snow, bare and burned area indices
  • Supervised classification and regression, including training data collection, classifier selection, classifier training, image classification and accuracy assessment
  • Time series analysis of remotely sensed data, including the concepts of smoothing, interpolation, linear modeling and phenology
  • Getting help online

Advanced Topics 

  • Working with arrays
  • Developing widgets
  • Importing and exporting data
  • Working with and troubleshooting your own application and data

Before the Workshop

All participants should bring a laptop computer with Google Chrome installed. Each participant should have registered to use Earth Engine prior to the workshop. Everyone is required to have their own laptop. The laptop should not have any restrictions and should be able to access these two websites:

Prior to coming to class everyone should have their own Google Earth Engine account and their username/email address should be sent to the instructor so that she can provide access to resources used during class.


No previous experience with Earth Engine or JavaScript is necessary for the beginner workshop, but programming experience along with a basic knowledge of remote sensing and/or GIS are highly desirable. A willingness to learn programming is required. Participants with no programming experience will require additional attention and the course length or cost (to hire Teaching Assistants) may have to be modified. Please speak with the course coordinator about the needs of your group.


Erin Hestir

Dr. Erin Hestir is an assistant professor in Environmental Systems at UC Merced. Her research focuses on aquatic ecosystems under threat from competing pressures to meet societal needs for water and food security while sustaining biodiversity and other ecosystem services such as water quality. She has expertise in geospatial analytics, hyperspectral and satellite remote sensing and sensor networks for inland and coastal waters and wetlands. Dr. Hestir also has expertise in the application of remote sensing for water resources and ecosystem management, and in facilitating the adoption of remote sensing for environmental reporting.

Christiana Ade

Christiana Ade is a PhD student in the Earth Observation and Remote Sensing lab at UC Merced. She holds a B.S. in Environmental Science from UNC-Chapel Hill, a graduate certificate in geospatial analytics, and a Masters in Marine, Earth and Atmospheric Science. She is passionate about using remote sensing to study environmental changes in wetlands and enjoys teaching others new programming skills to refine their imaging processing workflows.


Date: TBD Fall 2019

SSM 154
5200 N. Lake Road, Merced, CA 95343