Student information

Msc thesis subject: Deriving alerting services from high-resolution crop monitoring time-series in precision agriculture

Changing needs in food production and associated safety are challenging the agricultural sector to develop a new generation of sustainable agricultural systems. The use of global navigation satellite systems, remote sensing and near-sensing instruments on tractors and in situ wireless sensor networks provide the modern farmer with a wealth of data. However, there is still a lack of scientific knowledge and models to derive the required high spatial-temporal data products from these complementary data streams to optimize management activities and the usage of (natural) resources in precision agriculture.

Recently, new time-series analysis techniques have been developed to investigate abrupt changes in the spectral-temporal vegetation response. These methods have been developed for satellite time-series but also could be adopted for high-resolution images acquired from Unmanned Aerial Vehicles (UAVs). These methods have often been implemented in accessible toolboxes (BFAST, DATimes-Artmo). These technique could be relevant for more local scale applications like precision agriculture to detect abrupt changes in crop status and condition within a field or between phenotyping plots. In a next step, this change could be related to additional data sources like soil and weather parameters to identify potential causes and for the farmer to decide on the required management actions. To investigate the potential of these time-series analysis techniques, several datasets for different crops (potato, maize, grassland) and sensor types (multi-spectral, hyperspectral are available to evaluate existing methods but also develop updated methods.


  • Investigate the potential use of time-series analysis techniques for the temporal analysis of crops over the growing season;
  • Develop and test a temporally based spectral vegetation index which is able to detect changes in crop status and condition.


  • Burkart, A; Hecht, V L ; Kraska, T ; Rascher, U, 2018. Phenological analysis of unmanned aerial vehicle based time series of barley imagery with high temporal resolution. Precision Agriculture 19: 134-146.
  • Berra, E.F., Gaulton, R., Barr, S., 2019. Assessing spring phenology of a temperate woodland: A multiscale comparison of ground, unmanned aerial vehicle and Landsat satellite observations. Remote Sensing of Environment 223: 229-242.

Theme(s): Integrated Land Monitoring