Spring 2027

Advanced Environmental Remote Sensing

Advanced Environmental Remote Sensing covers fundamental physical principles of terrestrial remote sensing and advanced topics in earth observation and digital image processing.

offering recorded3 credits
Recorded instructors · Spring 2027 Mutlu Ozdogan

Summary

1 / 3

Recent recorded grades — Spring 2024: 3.95 GPA, 100.0% A/AB (n=11 letter grades); Spring 2025: 4.00 GPA, 100.0% A/AB (n=12 letter grades); Spring 2026: 4.00 GPA, 100.0% A/AB (n=15 letter grades).

What you’ll be able to do

  • Demonstrate understanding of major theories, approaches, concepts, and methods in remote sensing science.

  • Apply knowledge of the nature and properties of electromagnetic radiation and how it is affected by interactions with the atmosphere and the Earth’s surface, including transmission, absorption, reflectance, refraction, and scatter.

  • Outline several historical models used to explain the nature of light (e.g. wave, particle), describe phenomena associated with each model, and discuss the principle of complementarity.

  • Recall the basic physical principles of radiation laws derived from Planck, Wien, and Stefan-Boltzmann, and describe how these principles make remote sensing possible.

  • Differentiate the effects of atmospheric scatter (Rayleigh, Mie, and non-selective scatter) on Earth observation imagery, and determine how to remove atmospheric effects (or differences) across images.

  • Understand the fundamental laws of radiative transfer, such as heating and cooling of the surface and atmospheric layers, and its effects in both clear and cloudy conditions.

  • Illustrate the inverse methodologies used to derive geophysical parameters from remote measurements.

  • Use this knowledge to interpret complex multispectral radiance data sets to derive geophysical information for environmental applications.

  • Describe the differences between heat, thermal energy, and emissivity, distinguish how the amount of energy stored determines the temperature, and determine how these properties affect thermal remote sensing image analysis.

  • Summarize how the earth's surface scatters incident radiation anisotropically at different wavelengths, and describe how this wavelength dependence can be characterized by the Bidirectional Reflectance Distribution Function (BRDF).

  • Understand the role of vegetation in terrestrial ecosystem processes, describe the propagation of radiative transfer in vegetation, and how vegetation biophysical variables can be derived and modeled using remote sensing data and mathematical solutions.

  • Propose and carry out an original research project using remote sensing data, including outlining a plan, assembling necessary data, executing the analysis, and synthesizing the results.

  • Communicate effectively through discussion, oral presentations, and written reports.

Source: UW Guide

Grade history

↗
average GPA
letter grades
A
AB
B
BC
C
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All recorded terms · compare terms & instructors

Prerequisites

Course map

LAND ARC/​ENVIR ST/​G L E/​GEOG/​GEOSCI 371 and graduate/professional standing (or FW ECOL 371 prior to Summer 2026)

ENVIRST 956

This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree

Professors

Spring 2027

Recent recorded grades — Spring 2024: 3.95 GPA, 100.0% A/AB (n=11 letter grades); Spring 2025: 4.00 GPA, 100.0% A/AB (n=12 letter grades); Spring 2026: 4.00 GPA, 100.0% A/AB (n=15 letter grades).

Historical instructors & teaching patterns

MUTLU OZDOGAN is recorded teaching in Spring 2020, Spring 2022, Spring 2023, Spring 2024, Spring 2025, Spring 2026. Recorded history may be incomplete and does not establish a future schedule.

Recorded history may be incomplete and does not establish a future schedule.

Calendar & sections

Spring 2027

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction0 / 300

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.

Meeting source records
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Student experience

the class

Recent recorded grades — Spring 2024: 3.95 GPA, 100.0% A/AB (n=11 letter grades); Spring 2025: 4.00 GPA, 100.0% A/AB (n=12 letter grades); Spring 2026: 4.00 GPA, 100.0% A/AB (n=15 letter grades).

difficulty & workload

No workload feedback recorded.

Topics

  • Radiation interaction with atmosphere/surface and radiative transfer theory
  • Land surface characteristics, energy balance, and thermal sensing
  • Optical, RADAR, LiDAR, and hyperspectral data types
  • Object-based versus per-pixel pattern recognition approaches

Skills

  • Hands-on digital image processing using open source software
  • Atmospheric and radiometric correction of image data, including automated cloud detection
  • Biophysical remote sensing, 3D vegetation canopy modeling, time series analysis, and data fusion
  • Planning and executing original remote sensing research projects

Grades

Latest available · Spring 2026— not enough history to project Spring 2027.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

A
AB
B
BC
C
D
F

Grades over time

Through Spring 2026

More grade details Grade mix, volume & source data

Not enough comparable courses for Spring 2027 in UW–Madison.

Sources & history

UW–Madison

Catalog & offerings

Descriptions, prerequisites, and recorded course offerings.

Catalog observation history

Observations at scan time; dates do not imply when a catalog change took effect.

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Selected offering source records
ENVIRST 956 · Spring 2027

Advanced Environmental Remote Sensing

Recorded 2026-10-14
Raw records 
[
  {
    "run_id": "20261014T203208-0851c9a1",
    "semester": "1274",
    "observed_at": "2026-10-14 17:50:09.728220+00:00",
    "offering_id": "1274:360:025417",
    "course_id": "ENVIRST 956",
    "course_uid": "course_cefa5e4934c622d29e565450",
    "term_id": "1274",
    "source_course_id": "025417",
    "source_subject_id": "360",
    "title": "Advanced Environmental Remote Sensing",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Not Applicable"
  }
]
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Student reviews

Original comments behind the course and instructor summaries.

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Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

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Dataset & model

Recorded 2026-10-14

View the dataset on Hugging Face
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
Download part 1
Full model traces

Recorded model configuration, reasoning, and tool conversations.

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Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "51281d839b51f5992bb48335b576e28af85c1ece11df23f3bc005ecf91852ed9",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}