Introduction to portable spectroradiometers. Applications of ground remote sensing spectrometry in the scientific fields of rural, surveying and geoinformatics engineering. Radiative transfer in the atmosphere. Atmospheric corrections. Thermal Remote Sensing. Fundamentals of Microwave Remote Sensing. Hyperspectral remote sensing and spectral unmixing. Time series analysis. Remote sensing of environment with emphasis in vegetation, water and soil applications. Digital texture analysis.
- Teacher: Βασιλεία Καραθανάση
ECTS : 5
Language : el
Learning Outcomes : Upon successful completion of the course, the student will be able to develop the following skills: • Conduct ground measurements with a spectroradiometer, evaluate their accuracy, combine them with satellite data and integrate them into methodologies for determining atmospheric parameters. • Assess the quality of satellite data and select the optimal radiometric or atmospheric corrections depending on the application. • Retrieve Modis, Landsat, Sentinel satellite data and COPERNICUS products and select the most suitable data for monitoring a phenomenon and/or solving a problem. • Process thermal satellite images (MODIS, Landsat, Sentinel 3) to estimate apparent and kinetic temperature for detecting objects/phenomena/events, analyzing annual temperature cycles and estimating diachronic temperature changes. • Interpret SAR (intensity) images and process them in SNAP software to extract information for marine (oil spills, ship detection) and terrestrial (floods, biomass estimation, etc.) applications. • Reproduce MODis products by applying vegetation indices, moisture indices, classifications, etc., compare them with available data on the MODIS website, evaluate algorithms and interpret results. • Apply texture algorithms, evaluate them and adapt them to remote sensing data for optimal information extraction. • Apply spectral unmixing to hyperspectral images and calculate abundance maps for objects/categories present in an area. • Decompose time series of remote sensing data and find trend, seasonality, periodicity and randomness.