How can you use satellite data to track the development and condition of vegetation and agricultural crops? In this Blended Learning course, you’ll learn to recognize and monitor changes in growth, vegetation stress, drought, and crop development. You’ll use time series and satellite observations to analyze trends throughout the season and over multiple years, and translate them into actionable information for agriculture and nature conservation.
In this Blended Learning course, you’ll learn how satellite data is used to systematically monitor vegetation and agricultural areas. You’ll combine spectral information, vegetation indices, and time series to reveal differences and changes in vegetation. You’ll work with open-access satellite data from sources such as Sentinel and Landsat and learn how to translate observations into actionable information about vegetation development and condition.
Vegetation absorbs and reflects electromagnetic radiation in a characteristic way. For example, healthy green vegetation absorbs a large portion of visible red light for photosynthesis and reflects a relatively large amount of near-infrared radiation. These properties make it possible to identify vegetation from satellites and track changes in vegetation condition.
You’ll learn how factors such as chlorophyll, leaf structure, biomass, soil background, and moisture influence the spectral response of vegetation. This will help you better assess why satellite observations change during the growing season and which changes may indicate stress or abnormal conditions.
Spectral indices combine information from different satellite bands to make certain characteristics of vegetation more visible and measurable. You’ll work with well-known vegetation indices such as NDVI and be introduced to additional indices that can be used for specific vegetation and agricultural issues.
You’ll learn not only how to calculate an index but, more importantly, how to interpret the results. A high or low index value does not have the same meaning in every situation. Crop type, growth stage, soil, moisture, season, and acquisition conditions can all influence the result.
By comparing different indices and satellite observations, you’ll learn to determine which information is most suitable for the issue you’re investigating.
A single satellite image provides a snapshot. For agriculture and vegetation monitoring, however, development over time is often more important. That is why you will learn to combine satellite observations from different periods into time series.
You’ll analyze how vegetation develops over the course of a growing season and learn to recognize patterns associated with emergence, growth, peak vegetation development, harvest, and other phases. By comparing data across multiple years, you can also investigate whether developments deviate from normal seasonal patterns.
Time series thus make it possible not only to determine where vegetation is present, but also how it develops and when anomalies occur.
Deviations in vegetation development can have various causes. Drought, heat, flooding, diseases, soil properties, and agricultural practices can all influence the satellite signal.
You will learn how changes in spectral properties and time series can be used to identify areas with abnormal vegetation development. In doing so, you will combine satellite observations with supplementary information—such as precipitation, temperature, soil, land use, and other open geodata—as needed.
The goal is not only to identify an anomaly but, above all, to investigate which potential causes play a role and how reliably the satellite observation can be interpreted.
Satellite data make it possible to regularly observe both individual agricultural plots and large natural and agricultural areas. You will learn to summarize results by plot or area and analyze spatial differences between locations.
This allows, for example, the identification of plots with abnormal crop development, the investigation of differences within agricultural areas, and the monitoring of developments in nature areas. By combining multiple satellite images and data sources, a systematic form of monitoring is created that can be repeated regularly.
You will work with open data and open-source techniques. QGIS serves as a key platform for combining, analyzing, and visualizing satellite data and other geographic information. The focus is always on the vegetation or agricultural issue at hand, rather than on the software used.
During the Blended Learning program, you’ll work with realistic real-world examples and open satellite data. You’ll investigate vegetation development at various spatial and temporal scales and translate the results into maps, graphs, and well-supported conclusions.
You’ll work on assignments such as:
Upon completion, you will be able to independently use satellite data to analyze and monitor vegetation and agricultural areas. You will be able to interpret spectral indices and time series, identify anomalies in vegetation development, and combine satellite observations with other data to explain trends and translate them into actionable information for agriculture, nature conservation, and spatial monitoring.
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.
By regularly analyzing satellite images of the same agricultural plot, you can create a time series of crop development. Using tools such as Sentinel-2 and vegetation indices, you can track emergence, growth, peak vegetation development, and harvest. Differences between plots or compared to previous years can reveal where a crop is developing differently than expected.
Drought and other forms of stress can cause changes in the spectral properties of vegetation. By comparing vegetation indices and time series, areas with abnormal development can be identified. By combining this information with data on precipitation, temperature, soil, and moisture, it is then possible to investigate whether drought, heat, flooding, or another factor is a possible cause.
Satellite data can be used to monitor vegetation changes in nature areas over extended periods. This allows researchers to investigate, for example, changes in vegetation cover, drought, recovery following disturbance, or differences between nature areas. Time series make it possible to distinguish normal seasonal variation from structural changes that may require further investigation or management measures.
By analyzing satellite observations on a plot-by-plot basis, agricultural plots can be compared with one another. Plots or parts of plots with notably low or anomalous values can then be investigated in detail. Satellite monitoring thus helps to systematically screen large agricultural areas and identify locations where additional field observations or measures may be necessary.
