What can you discover using optical satellite imagery? In this Blended Learning course, you’ll learn how to use Landsat and Copernicus/Sentinel data to study vegetation, water, soil, and land cover. You’ll learn to select appropriate satellite images and spectral bands and to process, combine, analyze, and interpret them in QGIS. This will help you develop the knowledge and practical skills needed to effectively apply optical Earth observation data to a wide range of spatial issues.
During this Blended Learning course, you’ll learn how to analyze these issues and support your findings with optical satellite data in QGIS. You’ll work with up-to-date Earth observation data from sources such as the European Copernicus program—including Sentinel-2—and Landsat data. You’ll learn how different spectral bands provide information about the properties of the Earth’s surface. In QGIS, you’ll select, process, combine, and analyze satellite images, and translate satellite observations into usable spatial information for applications such as nature conservation, agriculture, water management, climate adaptation, and spatial development.
A thorough analysis of optical satellite imagery begins with an understanding of the interaction between electromagnetic radiation and the Earth’s surface. You’ll learn how solar radiation is reflected, absorbed, and transmitted, and why vegetation, water, soil, and built-up areas each have a characteristic spectral response.
You’ll also be introduced to visible light, near-infrared, and short-wave infrared, and you’ll learn what information different parts of the electromagnetic spectrum can provide. You’ll delve into spectral, spatial, temporal, and radiometric resolution and discover how these properties determine which phenomena you can observe with a satellite sensor. This will enable you not only to view satellite images but also to explain why certain objects and processes become visible in different spectral bands.
A reliable remote sensing analysis begins with selecting the right satellite data. That’s why you’ll work with current and widely used Earth observation data from the Copernicus program and Landsat. You’ll use Sentinel-2 and Landsat images, among others, and learn which sensors and spectral bands are available and for which applications they’re suitable.
You’ll learn to find, select, and use satellite images based on factors such as study area, acquisition date, cloud cover, and desired resolution. In doing so, you’ll compare the characteristics of Sentinel-2 and Landsat and discover which data source is best suited for a specific spatial problem.
You’ll also learn how the spectral, spatial, and temporal characteristics of different satellite systems can complement one another. As a result, the focus isn’t on any one specific satellite; instead, you’ll learn to make an informed choice from the available optical Earth observation data.
After covering the theoretical basics, you’ll get hands-on experience working with Landsat and Copernicus/Sentinel images in QGIS. You’ll learn how to load, manage, and visualize satellite data, as well as how to view different spectral bands individually and combine them.
You’ll create natural-color images and false-color composites that make the characteristics of vegetation, water, soil, and built-up areas more visible. You’ll then use raster tools in QGIS to analyze satellite images, compare areas, and reveal spatial patterns.
You will analyze the spectral characteristics of various types of land cover and learn to interpret these patterns in terms of their meaning. You’ll also examine the influence of the atmosphere, cloud cover, shadow, season, and acquisition conditions. This will help you distinguish between actual differences on the Earth’s surface and differences caused by the conditions under which a satellite image was acquired.
The goal is not only to process satellite images technically in QGIS, but above all to understand what information can be derived from optical Earth observation data and how reliable that information is for addressing a spatial issue.
During the Blended Learning course, you’ll work with realistic real-world examples and actual Landsat and Copernicus/Sentinel data. You’ll step into the role of a remote sensing specialist and tackle issues similar to those faced by government agencies, water boards, conservation organizations, agricultural organizations, and consulting firms. You’ll determine which satellite data is suitable, analyze the images in QGIS, interpret the spectral information, and translate your findings into usable spatial information.
You’ll work on assignments such as:
Upon completion, you will have the knowledge and practical skills to independently select, process, analyze, and interpret optical satellite imagery. You will be able to use Landsat and Copernicus/Sentinel data in QGIS, assess which satellite data are suitable for a specific issue, and determine what information can be derived from different spectral bands. This enables you to translate optical Earth observation data into reliable spatial information for nature conservation, agriculture, water management, climate adaptation, and spatial development.
You’ll receive 1-on-1 guidance. After signing up, our course coordinator will contact you to schedule your first session.
Do you have questions about the course content? Or are you unsure whether the course aligns with your learning goals or preferences? Would you prefer an in-house or private course? We’d be happy to help.
Optical remote sensing is the observation of the Earth's surface using sensors that measure reflected electromagnetic radiation. Satellites such as Landsat and Sentinel-2 capture different parts of the spectrum, including visible light, near-infrared, and short-wave infrared. This allows for the identification and analysis of the characteristics and differences in vegetation, water, soil, and built-up areas.
Much of the optical satellite data is available for free. Sentinel data from the European Copernicus program can be found through the Copernicus Data Space Ecosystem. Landsat data is available through USGS data sources. These satellite images can then be downloaded and processed and analyzed in QGIS.
Landsat and Sentinel-2 both provide optical satellite imagery, but differ in terms of spatial resolution, spectral bands, and the frequency with which an area is imaged, among other factors. Sentinel-2 offers higher spatial resolution for various bands, while Landsat has a very long historical data series. Which data source is most suitable therefore depends on the spatial issue at hand.
Vegetation, water, soil, and buildings reflect electromagnetic radiation in different ways. By using near-infrared and short-wave infrared in addition to visible light, characteristics become visible that are not immediately perceptible to the human eye. By combining different spectral bands, spatial patterns and differences can be studied more effectively.
In QGIS, you can visualize Landsat and Sentinel-2 images, combine spectral bands, create natural-color images and false-color composites, and perform raster analyses. This allows you, for example, to study vegetation, surface water, soil, and built-up areas, and to visualize spatial differences and changes.
