Thesis subject

MSc thesis topic: Assessing the seasonal variations of the Sentinel-1 C-band backscatter signal for tropical forests

Understanding the spatial-temporal forest heterogeneities in the tropics is key to better monitor natural and human-induced changes. Seasonality induced fluctuations can severely impact the reliability of synthetic aperture radar (SAR) based forest monitoring. The lack of openly available and dense time series in the past did not allow to study the seasonal fluctuations at scale.

A recent study by Koyama et al., 2019 has assessed the seasonal variations of long-wavelength L-band SAR backscatter data for tropical forest and also attributed and described the principal causes (e.g. flooding, short rainfall ...).

A comparable study for short-wavelength Sentinel-1 C-band backscatter is outstanding. For C-band backscatter we expect fluctuation and principle causes to vary from L-band.

In this thesis you will study the seasonal variations of short-wavelength C-band Sentinel-1 SAR backscatter data for tropical forest and also attributed and described the principal causes. Temporally dense Sentinel-1 C-band SAR data is available via Google Earth Engine.

Data processing and analysis should be done in Google Earth Engine.

Software: GEE, R/Python


  • Literature review on seasonal fluctuations and dynamics of C-band SAR data in the tropics.
  • Adopt the methods and concepts applied in Koyama et al., 2020 to study seasonal variations in Sentinel-1 C-band backscatter data
  • Assess and discuss the principle causes of seasonal fluctuations


  • Koyama, et al., 2019. Mapping the spatial-temporal variability of tropical forests by ALOS-2 L-band SAR big data analysis. Remote Sensing of Environment, 233 (2019) 111372


  • Advanced Earth Observation course
  • Geo-scripting course (Good knowledge in scripting is an asset; e.g. Google Earth Engine, R, python, java script)

Theme(s): Sensing & measuring; Modelling & visualisation