- Schedule of Classes - December 18, 2022 7:30PM EST
- Course Catalog - December 18, 2022 7:14PM EST
Course information provided by the Courses of Study 2022-2023.
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.
Regular Academic Session. Combined with: PLSCS 5290
Credits and Grading Basis
3 Credits Graded(Letter grades only)
Class Number & Section Details
- TR Emerson Hall 135
- Aug 22 - Dec 5, 2022
Instruction Mode: In Person
Prerequisite: knowledge of the basics of remote sensing, calculus, physics, and programming skills, and some background in agro-ecosystems.
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