PLSCI 4290 - Remote Sensing and Modeling for Ecosystems

Fall. 3 credits. Letter grades only.

Prerequisite: knowledge of the basics of remote sensing, calculus, physics, and programming skills, and some background in agro-ecosystems. Co-meets with PLSCI 5290.

Y. Sun.

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.

Outcome 1: Describe the basic principles in remote sensing.

Outcome 2: Describe the spectral signatures of land surface properties and appropriate application.

Outcome 3: Acquire satellite dataset from NASA, ESA, and Google Earth Engine.

Outcome 4: Process remote sensing data using ENVI, and R (or Python).

Outcome 5: Run mechanistic model simulations in the CLM framework.

Outcome 6: Apply remote sensing observations and model simulations to interpret agro-ecological phenomena.

Outcome 7: Conduct an independent applications-based project.

Outcome 8: Develop and present an oral and collaborative group project.