PLSCS 4290

PLSCS 4290

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.

Outcomes
  • 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.

View Enrollment Information

Syllabi: none
  •   Regular Academic Session.  Combined with: PLSCS 5290

  • 3 Credits Graded

  •  3042 PLSCS 4290   LEC 001

    • TR To Be Assigned
    • Aug 22 - Dec 5, 2022
    • Sun, Y

  • Instruction Mode: In Person
    Prerequisite: knowledge of the basics of remote sensing, calculus, physics, and programming skills, and some background in agro-ecosystems.