Solar Panel Farm Site Selection Using Remote Sensing and GEE

Solar Farm Site Selection Using Solar Radiation, Land Cover, DEM, and Google Earth Engine

Created byEarth's AI

Last updated 8/2025

English

What you'll learn

Understand the fundamentals of remote sensing data and its application in environmental analysis.
Learn how to process and analyze satellite data such as DEM, landcover, and solar radiation using Google Earth Engine.
Develop skills to identify and map suitable sites for solar panel farms by integrating multiple geospatial datasets.
Gain hands-on experience in creating suitability models and exporting geospatial results for real-world renewable energy planning projects.

Course content

1 section • 5 lectures • 55m total length

Lecture 1: Fundamentals of Remote Sensing13:19
This lecture introduces the core principles of remote sensing, including the types of satellite sensors, data acquisition methods, and key concepts such as spatial, spectral, and temporal resolution. Learners will explore how remote sensing captures Earth’s surface information and the importance of various datasets in environmental and land use studies. This foundation sets the stage for understanding how satellite imagery and geospatial data can be applied to analyze and monitor natural resources and human activities.
Lecture 2: Remote Sensing for Site Suitability Mapping8:03
In this session, students will learn how to use remote sensing data to perform site suitability analysis. The lecture covers essential steps like data preprocessing, classification, and the integration of multiple environmental factors such as elevation, land cover, and climate data. Learners will understand how to identify suitable locations for different purposes by analyzing terrain characteristics and natural resource availability, preparing them for practical applications like solar farm or landfill site selection.
Lecture 3: Introduction to Google Earth Engine (GEE)9:25
This lecture provides a comprehensive introduction to Google Earth Engine, a powerful cloud-based platform for processing and analyzing large-scale geospatial data. Students will learn how to navigate the GEE interface, access diverse satellite datasets, and utilize built-in functions for spatial analysis. The session emphasizes the advantages of GEE for rapid and scalable environmental monitoring, setting the foundation for advanced geospatial workflows in subsequent lessons.
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 4: Implementation in GEE for Solar Farm Site Selection21:08
The final lecture focuses on applying the skills learned to a real-world project: selecting suitable sites for solar farms using Google Earth Engine. Students will integrate solar radiation data, land cover classification, slope, and other environmental variables to create a suitability map. This hands-on session covers data processing, masking, weighting, visualization, and exporting results, equipping learners with practical expertise to support renewable energy planning and environmental decision-making.

Requirements

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

Description

The growing demand for renewable energy sources has made solar power a critical component of sustainable development. This course offers a comprehensive introduction to using remote sensing and Google Earth Engine (GEE) for solar farm site selection. It begins with foundational concepts of remote sensing, helping learners understand different types of satellite data and their applications in environmental analysis.

Next, the course covers site suitability mapping principles, focusing on how to analyze various environmental factors such as elevation, slope, land cover, and solar radiation to assess land suitability. Students will gain practical experience working with multi-source data in GEE, a cloud-based geospatial platform that enables efficient processing and visualization of large datasets.

The course culminates with an implementation project where learners create a solar farm suitability map for a chosen area using real-world satellite data. They will learn to apply masks for unsuitable land covers, calculate solar radiation indices, and integrate multiple layers into a weighted suitability model.

By the end, students will have the skills to perform advanced geospatial analyses, automate workflows in GEE, and make data-driven decisions for solar farm planning. This course is perfect for those aiming to contribute to renewable energy projects, environmental monitoring, or spatial data science using cutting-edge tools.

Who this course is for:

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