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 so the need for collecting ground truth data to keep the pace with the theoretical and technological development.
Our 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. The student will collected 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.
- Produce a dataset of field measurement of potato canopies
- Retrieve satellite images for the ground points.
- Model canopy characteristics by updating old equations with new data and use surface reflectance models to estimate canopy characteristics.
- Clevers JGPW, Kooistra L, van den Brande MMM (2017) Using Sentinel-2 data for retrieving LAI and leaf and canopy chlorophyll content of a potato crop. Remote Sens 9:1–15.
- Roosjen PPJ, Brede B, Suomalainen JM, et al (2018) Improved estimation of leaf area index and leaf chlorophyll content of a potato crop using multi-angle spectral data – potential of unmanned aerial vehicle imagery. Int J Appl Earth Obs Geoinf 66:14–26.
- Bouman BAM, van Kasteren HWJ, Uenk D (1992) Standard relations to estimate ground cover and LAI of agricultural crops from reflectance measurements. Eur J Agron 1:249–262
- van Evert FK, Booij R, Jukema JN, et al (2012) Using crop reflectance to determine sidedress N rate in potato saves N and maintains yield. Eur J Agron 43:58–67.
Wallach D, Makowski D, Jones JW, Brun F (2019) Front Matter. In: Working with Dynamic Crop Models (Third Edition), Third Edit. Academic Press, pp i–ii (General overview on crop models (chapter 1), calibration of models (chapter 6 paramter estimation), assimilation of data in crop models chapter 14))
- Interest in conducting field measurements in May-August (~ 2 days in the field + 1 day of sample processing @ unifarm).
- Operative knowledge of statistics (e.g. regressions).
- Minimal knowledge of programming (R preferred).
Theme(s): Sensing & measuring