Trans-Atlantic Training 2023 (TAT-10): Remote Sensing for Environmental Monitoring and Modelling

This post contains educational materials from the event Trans-Atlantic Training 2023 (TAT-10): Remote Sensing for Environmental Monitoring and Modelling held in Prague and Brno, Czech Republic, from 27 June – 1 July 2023.

Access specific videos (click on ), presentation slides (click on ), and exercise data (click on ) from the sessions here below, or download all the theory slides, practical slides and datasets from the link on the left side window.

ESA introduction:

• ESA Intro and ESA EO Missions, F. Sarti (theory) (6 MB)
• ESA Phi-lab, B. LeSaux and A. Lacroix (theory) (3 MB)

SAR for Soil Moisture and land cover:

• SAR for Soil Moisture, W. Wagner (theory) (17 MB)
•  SAR for Land Cover, A. Mouratidis (theory) (5 MB)
• SAR for Land Cover, A. Mouratidis (practical) (1 MB)

Introduction to SAR Polarimetry:

• Introduction to SAR Polarimetry, A. Marino (theory) (3 MB)
• Introduction to SAR Polarimetry, A. Marino (Practical) (1.9 GB, to access, download all materials from the course using the big download button)

Optical and SAR for forestry using timeseries and ML:

• Optical for Timeseries, P. Stych and J. Lastovicka (theory) (7 MB)
• Optical and SAR for Forestry using machine learning, D. Paluba (theory) (2 MB)
• Optical and SAR for Forestry using machine learning, D. Paluba (practical) (8 MB)

NASA introduction:

• Introduction to land remote sensing, G. Gutman (theory) (3 MB)

Upscaling land cover assessment from satellite to UAV multispectral data for sparsely vegetated areas:

• Upscaling land cover assessment from satellite to UAV multispectral data for sparsely vegetated areas, P. Vaczi, J. Hruska, H. Svatonova (practical) (224 MB)

Remote sensing and Hydrology:

• Remote sensing for hydrology, S. Woznicki (theory) (7 MB)
• Remote sensing for hydrology, S. Woznicki (practical) (8 MB)

Agriculture land cover mapping using machine learning:

• Agriculture land cover mapping using machine learning, S. Skakun (theory) (4 MB)
• Agriculture land cover mapping using machine learning, S. Skakun (practical) (1.9 GB, to access, download all materials from the course using the big download button)

Hyperspectral data in environmental studies:

•  Hyperspectral data in environmental studies, P. Campbell (theory) (20 MB)
•  Hyperspectral data in environmental studies, P. Campbell (practical) (19 MB)