EGU21-12357, updated on 04 Mar 2021
EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Target food security: assimilating ultra-high resolution satellite images into a crop-yield forecasting model

Matteo G. Ziliani1, Bruno Aragon1, Trenton Franz2, Ibrahim Hoteit1, Justin Sheffield3, and Matthew F. McCabe1
Matteo G. Ziliani et al.
  • 1KAUST, Thuwal, Saudi Arabia
  • 2University of Nebraska-Lincoln, Lincoln, USA
  • 3University of Southampton, Southampton, United Kingdom

Assimilating biophysical metrics from remote sensing platforms into crop-yield forecasting models can increase overall model performance. Recent advances in remote sensing technologies provide an unprecedented resource for Earth observation that has both, spatial and temporal resolutions appropriate for precision agriculture applications. Furthermore, computationally efficient assimilation techniques can integrate these new satellite-derived products into modeling frameworks. To date, such modeling approaches work at the regional scale, with comparatively few studies examining the integration of remote sensing and crop-yield modeling at intra-field resolutions. In this study, we investigate the potential of assimilating daily, 3 m satellite-derived leaf area index (LAI) into the Agricultural Production Systems sIMulator (APSIM) for crop yield estimation in a rainfed corn field located in Nebraska. The impact of the number of satellite images and the definition of homogeneous spatial units required to re-initialize input parameters was also evaluated. Results show that the observed spatial variability of LAI within the maize field can effectively drive the crop simulation model and enhance yield forecasting that takes into account intra-field variability. The detection of intra-field biophysical metrics is particularly valuable since it may be employed to infer inefficiency problems at different stages of the season, and hence drive specific and localized management decisions for improving the final crop yield.

How to cite: Ziliani, M. G., Aragon, B., Franz, T., Hoteit, I., Sheffield, J., and McCabe, M. F.: Target food security: assimilating ultra-high resolution satellite images into a crop-yield forecasting model, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12357,, 2021.