PLSCS 4290

Remote Sensing and Modeling for Ecosystems

Course Description

Course information provided by the Courses of Study 2023-2024.

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.

When Offered Fall.

Prerequisites/Corequisites Prerequisite: knowledge of the basics of remote sensing, calculus, physics, and programming skills, and some background in agro-ecosystems.

• 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.

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