Thesis subject

Retrieving canopy characteristics of new potato cultivars from remote sensing measurements


The use of remote sensing data to inform crop growth models (assimilation/calibration) is a promising approach that can help improving the reliability of crop models. However such approach requires robust methods for the estimation of canopy biophysical state variables (e.g. LAI) from remote sensing. These methods are continuously evolving thanks to new sensors, new models and new cultivars, and therefore the collection of ground truth data needs to keep the pace with the theoretical developments.

The Business Unit Agrosystems promotes the use of crop growth models for decision support in crop management.  We realized that the collection of potato canopy data in the Netherlands received little attention over the last decades, resulting in a limited number of studies carried out on new cultivars and methodologies. We would like therefore to strengthen our confidence in modelling canopy characteristics using remote sensing information by acquiring new data.

The project will consist in the measurement of field canopy characteristics (e.g. LAI, chlorophyll content, intercepted radiation) and reflectance spectra from farmer fields at different Dutch locations. We collaborate with a larger project ‘ Yield gap analysis for sustainable potato production’ in which field experiments are conducted. The student will collect field data and use them to revise existing regression equations relating vegetation indices (e.g. WDVI) and canopy characteristics. These data will also be used by the student to verify the use of use of inverse radiative transfer models (e.g. PROSAIL) to retrieve canopy characteristics.

The overarching objective of the project is to create an application programming interface (API) to retrieve canopy characteristics for potatoes from reflectance information. This is a well-needed application for advancing the routine applicability of crop models. 

Type of work

Field data measurement, sample processing, and modeling.


A strong background in agronomy, with interest in quantitative approaches.


Wageningen, the Netherlands


May-November 2020


Pytrik Reidsma                                   0317 – 48 55 78

Bernardo Maestrini                             0317 – 48 23 92