PLSCI 4290
Remote Sensing and Modeling for Ecosystems
This course introduces advanced concepts of remote sensing and numerical modeling, with hands-on experience in data acquisition, processing, and interpretation. This course aims to explore key questions facing the agronomic and natural eco-systems using remote sensing techniques and ecological modeling at various scales. It provides hands-on experience in remote sensing techniques and using datasets/tools and model simulations to address research questions.
Topics
- Describe the basic principles in remote sensing.
- Describe the spectral signatures of land surface properties and appropriate application.
- Acquire satellite dataset from NASA, ESA, and Google Earth Engine.
- Process remote sensing data using ENVI, and R (or Python).
- Run mechanistic model simulations in the CLM framework.
- Apply remote sensing observations and model simulations to interpret agro-ecological phenomena.
- Conduct an independent applications-based project.
- Develop and present an oral and collaborative group project.
Official roster learning outcomes
Prerequisites & course information
knowledge of the basics of remote sensing, calculus, physics, and programming skills, and some background in agro-ecosystems.
Sources
- Course catalogue ↗2026-2027