EGU2020-11467
https://doi.org/10.5194/egusphere-egu2020-11467
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Crop yield evaluation using sentinel satellite imagery and modelling methods

Anne Gobin1,2
Anne Gobin
  • 1Flemish Institute for Technological Research, Mol, Belgium (anne.gobin@vito.be)
  • 2Faculty of BioScience Engineering, Department of Earth & Environmental Sciences, University of Leuven, Belgium

Agricultural yield is largely determined by weather conditions during the crop growing season. A comparison of meteorological indicators between low and high arable yields revealed significant (p > 0.05) differences in meteorological indicators (Gobin, 2018), and these change with crop. Further analysis revealed differences in climate resilience (Kahiluoto et al., 2019).

An important aspect of crop yield assessment concerns crop growth development and subsequent yield prediction (Durgun et al., 2016). Current models have predominantly concentrated on the relation between meteorological data and crop yield (Gobin et al., 2017). A lot of data are available on the input side to include soil and weather, but very few on crop development and yield at the field scale.

A new era of satellite remote sensing and sensor technology has already offered a paradigm shift to data rich environments with unprecedented possibilities to monitor crop development at higher spatial, temporal and spectral resolutions. Combining modelling and statistical analysis with monitoring from remote sensing presents new opportunities to understand crop growth as a basis for crop yield assessment (Durgun et al., 2020) and further developments in the agriculture, insurance and bio-economy sector.

Examples of common arable crop growth assessment will be drawn from different grants and projects.

References:

  • Durgun, Ö, Gobin, A., Duveillier, G., Tychon, B., 2020. A study on trade-offs between spatial resolution and temporal sampling density for wheat yield estimation using both thermal and calendar time. International Journal of Applied Earth Observations and Geoinformation, 86. https://doi.org/10.1016/j.jag.2019.101988
  • Durgun, Y.Ö., Gobin, A., Vandekerchove, R., Tychon, B., 2016. Crop Area Mapping using 100m PROBA-V time series. Remote Sensing 8(7), 585; www.doi.org/10.3390/rs8070585.
  • Gobin, A., Kersebaum K.C., Eitzinger J., Trnka M., Hlavinka P., Takáč J., Kroes J., Ventrella D., Dalla Marta A., Deelstra J., Lalić B., Nejedlik P., Orlandini S., Peltonen-Sainio P., Rajala A., Saue T., Şaylan L., Stričevic R., Vučetić V., Zoumides C., 2017. Variability in the water footprint of arable crop production across European regions. Water 2017, 9(2), 93; https://doi.org/10.3390/w9020093
  • Gobin, A., 2018. Weather related risks in Belgian arable agriculture. Agricultural Systems 159: 225-236. https://doi.org/10.1016/j.agsy.2017.06.009
  • Kahiluoto H., Kaseva, J., Balek, J., Olesen, J.E., Ruiz-Ramos, M., Gobin, A., Kersebaum, K.C., Takáč, J., Ruget, F., Ferrise, R., Bezak, P., Capellades, G., Dibari, C., Mäkinen, H., Nendel, C., Ventrella, D., Rodríguez, A., Bindi, M., Trnka M., 2019. Decline in climate resilience of European wheat. Proceedings of the National Academy of Sciences of the USA 116: 123-128. https://doi.org/10.1073/pnas.1804387115

How to cite: Gobin, A.: Crop yield evaluation using sentinel satellite imagery and modelling methods, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11467, https://doi.org/10.5194/egusphere-egu2020-11467, 2020

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