Courses in Remote Sensing Study Track

The preliminary syllabus for the academic year 2025-2026 has been  published in the Studies service and the final syllabus will be published in Sisu by the end of June. Please note, that some courses will not be lectured every academic year.

See recommended previous knowledge and skills for this study track in the end of the page.

Compulsory studies for all study tracks (45-50 cr)

ATM301 Atmospheric and Earth Sciences Today (5 cr)
ATM345 Atmospheric science seminar for Master’s students (5 cr)
ATM350 Master's thesis (30 cr)
ATM399 Maturity test (0 cr)
Personal study plan (0 cr) (included in ATM301)

Select either ATM302 Climate.now (5 cr) or ATM386 Climate.now (2 cr).

Select either ATM303 Project course in atmospheric sciences (3 or 5 cr) or ATM384 Integrating art and science (5 cr) or ATM380 Solutions.now (5 cr).

Mathematical and computational methods in remote sensing course package (10-15 cr)

Compulsory courses:

• ATM357 Atmospheric Radiation (5 cr)
• ME-204 Geoinformatics I (5 cr)

Optional course: MAST32005 Spatial Modelling and Bayesien Inference (5 cr)

Optional courses / course packages (complete at least 40 cr)

You can complete whole course packages or singular courses.

Observation systems and methods course package

ATM322 Meteorological observation systems (5 cr)
ATM323 Advanced Course in Radar Meteorology (5 cr)
ATM324 Laboratory Course in Radar Meteorology (5 cr)

Remote sensing of the atmosphere and hydrosphere course package

ATM325 Satellite Remote Sensing Methods in Aerosols Science (5 cr)
ATM310 Time Series Analysis in Geosciences (5 cr)
PAP314 Introduction to Light Scattering (5 cr)

Remote sensing of the biosphere and land use course package

GEOG-G302 Introduction to Remote Sensing of Environment (5 cr)
GEOG-322 Advanced remote sensing of environment (5 cr)
GEOG-324 Hyperspectral remote sensing (5 cr)
ME-230 Geoinformatics 2 (5 cr)
ME-232 Remote sensing 1 (5 cr)
ME-234 Counting methods in forest sciences (5 cr)
FOR-258 GIS analysis and modelling (5 cr)
FOR-260 Advanced course in remote sensing (5 cr)

Optional advanced courses for all study tracks (0-30 credits)

ATM373 Leadership for Sustainable Change (5 cr)
ATM377 Introduction to Earth System Modelling (5 cr)
ATM378 Sustainable.now (5 cr)
ATM379 SystemsChange.now (5 cr)
ATM380 Solutions.now (5 cr)
ATM389 Living with changing climate (5 cr)
ATM397 Forests and Climate Change (2 cr)
ATM398 Climate University for Virtual Exchanges (CLUVEX) (1 cr)
ATM405 Una Europa Virtual Exchanges for Sustainability (1 cr)
ATM404 Environmental and Climate Regulation in the EU (5 cr)
ATM396 Application of AI/ML techniques in Atmospheric Science (3 cr)
ATM384 Integrating art and science (5 cr)
 
Data Science

ATM308 Statistical Tools for Climate and Atmospheric Science (5 cr)
ATM309 Analysis of atmosphere-surface interactions and feedbacks (5 cr)
ATM310 Time Series Analysis in Geosciences (5 cr)
DATA11001 Introduction to Data Science (5 cr)
DATA12001 Advanced Course in Machine Learning (5 cr)
LSI35002 Bayesian Data Analysis (5 cr)
DATA20046 Neural Networks and Deep Learning (5 cr)

Select at most one of the following courses:

• DATA11002 Introduction to Machine Learning (5 cr)
• ATM4171 Introduction to Machine Learning for Atmospheric and Earth System Research (5 cr)

Other studies can include study modules or courses from other programmes or courses from other study tracks. Also practical training and language studies can be included in other studies.

To successfully complete the studies good, knowledge of bachelor's degree level physics, mathematical methods as well as practical skills in scientific computing (e.g., Python, Matlab). In order to successfully pursue Master's studies in the remote sensing study track it is recommended that you have the following knowledge and skills before entering to the programme: 

• Physics: Basic studies in Physics (at least 25 cr). You should be familiar with at least the following:
  • Newton’s laws
  • Concept of work. Kinetic energy and potential energy
  • Laws of thermodynamics
  • Concept of ideal gas and the ideal gas equation of state
  • Basics of statistical gas theory
  • Basics of electrodynamics and electromagnetism (e.g. electric field, charge, Coulomb’s law)
  • Basics of electromagnetic radiation: black body radiation, refractive index, electromagnetic spectrum
• Mathematics
  • Basics of vectors and linear algebra
  • Complex numbers
  • Differentiation and integration of functions
  • First and second degree ordinary and partial differential equations 
  • Solving sets of linear equations using matrix algebra
• Programming and statistics
  • some prior experience of programming and statistical analysis of observations (e.g., Python, Matlab)
  • basic statistical concepts