Msc thesis subject: Assessing the physical background of spatial variation in biomass production of corn: a case study for the Hupselse Beek catchment.

From NIR data, acquired by UAV and/or airborne platforms, crop biomass and health can be derived. Biomass (dry matter) is amongst others a function of plant transpiration. Remote sensing has been used for monitoring transpiration using satellites but application from (unmanned) aerial (thermal) imagery is still limited. The Hupselse Beek catchment area has a KNMI meteorological station. It is hydrologically well-defined, evapotranspiration data can be taken from the water balance. Irrigation is absent in the area, due to a lack of water resources.

Regional Water Authorities are serving agriculture by water management. Farmers are focussing on crop production. Depending on soil type, soil- and groundwater storage, and weather conditions, crops have sufficient water or face drought/oxygen stress. Stress leads to sub-optimal crop production.

We focus on catchment- and field-scale applications of RS data are on evapotranspiration and crop production. RS data have been collected by RPAS and aircraft in 2017 on May 31, July 4, and September 20-21. More data will be collected in 2018. Field-scale spatial resolution ≈10 cm, catchment-scale spatial resolution ≈1 m.
Available satellite Sentinel-2 data give us information in time on NDVI as the growing season of the crop progresses.


  • To determine the spatial variability of crop production of corn within the Hupselse Beek catchment area using remote sensing information.
  • To analyse and find causes for this spatial variability, assuming spatial uniform precipitation, reference-crop evapotranspiration, and manure/fertilizer applications.
  • To recommend on near-future applications of data acquisition by remote sensing platforms and specific sensors.

Materials and methods - suggestion

  • Elaborate specific Hupsel RS data 2017 (RGB, NIR, TIR) on field and catchment scale.
  • Combine/integrate NDVI (by NIR) data with Sentinel-2 NDVI, analyse and explain differences in space on dates available.
  • Use spatial maps of soil type, groundwater depth, and actual land use ( to explain spatial patterns of UAV- and airborne acquired images for the catchment.
  • Calculate changes in grid/field biomass through time.
  • Relate spatial patterns of crop biomass to CBS, LEI (now WER) amongst other data on agricultural production and hydrological modelling results (SPHY-model, model water board Rijn and IJssel, …) on (cumulative) evapotranspiration.
  • Discuss causality of spatial relationships and their implication for agriculture crop yield forecast.


  • Roelofsen, H. (2014) Remote sensing of vegetation characteristics in support of ecological modelling. PhD thesis, VU University Amsterdam.
  • Terink, W., et al. (2015) SPHY v2.0 hydrological simulations in Hupselse Beek catchment.
  • Water Board Rijn en IJssel: AMIGO groundwater model (and updates).


  • Interest in using remote sensing data/products in empirical (hypothesis-driven) research.
  • Interest in hydrology.

Theme(s): Sensing & Measuring, Integrated Land Monitoring