How can you observe the Earth’s surface when clouds or darkness limit optical satellite imagery? In this Blended Learning course, you’ll learn how radar remote sensing and SAR are used to analyze the characteristics and changes of the Earth’s surface. You’ll work with radar data from sources such as Sentinel-1 and learn how to apply it to issues related to water, soil, vegetation, land use, and monitoring.
During this Blended Learning course, you’ll learn how active radar sensors are used to observe the Earth’s surface under a variety of conditions. You’ll work with current radar data from sources such as the European Sentinel-1 mission and learn to interpret radar observations and apply them to issues related to water, soil, vegetation, land cover, and changes in the Earth’s surface.
Radar Remote Sensing differs fundamentally from optical remote sensing. A radar satellite does not use reflected sunlight but instead transmits microwaves toward the Earth’s surface and measures which portion of the signal returns to the sensor. This allows radar observations to be conducted both during the day and at night, and microwaves can penetrate clouds to a significant extent.
You will learn how Synthetic Aperture Radar (SAR) works and why characteristics such as wavelength, angle of incidence, polarization, surface roughness, and moisture content influence the reflected radar signal. This so-called backscatter forms the basis for interpreting and analyzing SAR images.
Radar images look different from optical satellite images. Light and dark areas are not directly determined by color, but by the amount of radar energy reflected back to the sensor. For example, smooth water is often dark, while rough surfaces, buildings, and certain vegetation structures can cause stronger backscatter.
You’ll learn how to recognize various landscape features in SAR images and how acquisition geometry affects the image. You’ll also focus on radar-specific phenomena such as speckle, radar shadow, layover, and foreshortening. By understanding these effects, you’ll be able to interpret radar images more effectively and avoid misinterpreting image features as changes in the landscape.
Within the European Copernicus program, Sentinel-1 is a key source of freely available SAR data. You’ll learn which Sentinel-1 products are available, what information they contain, and how to select appropriate radar data for various applications.
In addition to Sentinel-1, you’ll be introduced to other radar satellites and radar bands, such as ALOS PALSAR. This will help you discover how different wavelengths and sensors can provide complementary information about vegetation, soil, moisture, and landscape structures.
You’ll learn how to prepare radar data for analysis and combine it with other geoinformation. You’ll work with open data and open-source techniques. QGIS serves as a key environment for analysis and visualization, and where necessary, specialized open-source tools such as ESA SNAP are used for processing SAR data.
A key strength of radar is its sensitivity to properties of the Earth’s surface that are not always clearly visible in optical imagery. You will investigate how differences in moisture, surface roughness, and vegetation structure influence the radar signal.
As a result, SAR can be used to detect flooding, track changes in surface water, analyze soil moisture, and monitor agriculture and vegetation. You will learn to assess which characteristics of the radar signal are relevant to the problem at hand and what other data are needed to interpret the observations reliably.
When radar images of the same area taken at different times are compared, changes can be analyzed with great precision. You’ll be introduced to techniques that use differences in backscatter to identify changes in water, vegetation, or land cover.
You will also be introduced to radar interferometry (InSAR). This technique combines the phase information from multiple SAR images to determine very small changes in the distance between the sensor and the Earth’s surface in the radar’s line of sight. This allows for the monitoring of, for example, land subsidence, ground settlement, landslides, and other deformations.
In this introduction to SAR, you will primarily learn to understand what interferometry can measure, what data is required for this, and how results should be interpreted. The emphasis is on selecting and evaluating a suitable radar approach for the spatial problem at hand.
During the Blended Learning course, you’ll work with realistic real-world examples and open SAR data. You’ll learn to combine radar observations with other geographic information and translate the results into maps and well-founded conclusions.
You’ll work on assignments such as:
By the end of the course, you will be able to interpret SAR data and assess which radar observations are suitable for various spatial issues. You will understand how backscatter, polarization, wavelength, and acquisition geometry influence the radar signal, and you will be able to apply radar to analyze and monitor water, soil, vegetation, land cover, and changes in the Earth’s surface.
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.
SAR is highly suitable for detecting and monitoring surface water and flooding. Because radar can observe even when it is cloudy or dark, areas can be monitored during extreme weather conditions when optical satellite imagery is often less useful. By comparing Sentinel-1 images taken before and after an event, the extent of a flood can be mapped, and changes in the water surface can be tracked.
The radar signal responds to characteristics such as soil moisture, surface roughness, and vegetation structure. This allows SAR to be used to monitor changes in agricultural parcels, soil conditions, and vegetation. By combining observations from different time periods and polarizations, it is possible to identify trends that are difficult to detect using optical satellite imagery alone.
Radar interferometry (InSAR) combines SAR images of the same area taken at different times. Small changes in the phase of the radar signal can be used to determine movements of the Earth’s surface. As a result, InSAR can be used to monitor land subsidence, ground subsidence, landslides, and deformations around, for example, infrastructure, mining areas, and urban areas.
Buildings and infrastructure often produce strong and stable radar reflections. This allows successive SAR observations to be used to track changes in urban areas and around infrastructure. When combined with InSAR, even very small deformations over longer periods can be detected, for example in roads, rail lines, levees, buildings, and other engineering structures. This makes radar data valuable for early detection and targeted monitoring.
