Intro to Google Earth Engine for Remote Sensing (Fall 2025)

Overview

This workshop introduces the fundamentals of remote sensing through the powerful cloud-based platform Google Earth Engine (GEE). Participants will explore a variety of satellite imagery—optical, radar, and multispectral—used in environmental monitoring and analysis.

The workshop is designed to build both conceptual understanding and practical skills in remote sensing, with a strong emphasis on hands-on learning using GEE. Learn how to access, visualize, and analyze satellite data for various applications such as land cover classification, forest change detection, and flood monitoring.

Workshop Dates

• Tuesday, October 7 (6:00 - 9:00 p.m.) Class meets on Zoom
• Tuesday, October 14 (6:00 - 9:00 p.m.) Class meets on Zoom
• Tuesday, October 21 (6:00 - 9:00 p.m.) Class meets on Zoom

Learning Objectives

• Navigate and utilize the Google Earth Engine platform for remote sensing analysis.
• Access and import diverse datasets such as Sentinel-2, Landsat, and Sentinel-1 for environmental and land monitoring.
• Apply preprocessing techniques such as cloud masking, image filtering, generating spectral indices (NDVI, NDWI) and creating image composites (median, mean) to enhance satellite data and support analysis.
• Conduct spectral and temporal analysis to interpret land cover, vegetation dynamics, and surface changes.

Lesson Plan

Session 1

• Fundamentals of Remote Sensing
• What is Google Earth Engine (GEE)?
• Introduction to the Earth Engine code editor (Javascript API)
• Check GEE account set up
• Basics of programming in GEE

Session 2

• Remote Sensing vocabulary: Spatial, temporal, spectral and radiometric resolution; spectral indices
• Exploring GEE’s data catalog and datasets
• Access and import satellite datasets such as Sentinel-2, Landsat and Sentinel-1 in GEE
• Display images in GEE
• Generate NDVI, create image composites using Sentinel-2/Landsat

Session 3

• Overview of GEE land applications
• Introduction to image classification
• Supervised land cover classification in GEE

Meet your Instructor: Preethi BalajiI

I earned my Ph.D. in Synthetic Aperture Radar (SAR) remote sensing for forest disturbance monitoring in Ireland before moving to the United States. My expertise also includes optical remote sensing, where I have worked on various applications such as forest and crop monitoring, flood mapping, and ecological forecasting. Recently, I completed the NASA DEVELOP program at Idaho State University, Pocatello. I also write blogs on Medium, offering tutorials on SAR foundational principles and applications: https://medium.com/@preet.balaji20.

Outside of work, I enjoy nature photography, particularly bird photography.

Questions?

• Contact Rick Kos, BayGeo's Education Director, at rkos@baygeo.org
• For more information about BayGeo, the Bay Area's hub for the geospatial community, visit baygeo.org