Target food security: assimilating ultra-high resolution satellite images into a crop-yield forecasting model
Type
PresentationAuthors
Ziliani, Matteo G.Aragon Solorio, Bruno Jose Luis

Franz, Trenton

Hoteit, Ibrahim

Sheffield, Justin

McCabe, Matthew

KAUST Department
KAUST, Thuwal, Saudi ArabiaEnvironmental Science and Engineering Program
Environmental Science and Engineering
Biological and Environmental Sciences and Engineering (BESE) Division
Earth Science and Engineering Program
Physical Science and Engineering (PSE) Division
Water Desalination and Reuse Research Center (WDRC)
Date
2021-03-04Permanent link to this record
http://hdl.handle.net/10754/668037
Metadata
Show full item recordAbstract
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.Citation
Ziliani, M. G., Aragon, B., Franz, T., Hoteit, I., Sheffield, J., & McCabe, M. F. (2021). Target food security: assimilating ultra-high resolution satellite images into a crop-yield forecasting model. doi:10.5194/egusphere-egu21-12357Publisher
Copernicus GmbHAdditional Links
https://meetingorganizer.copernicus.org/EGU21/EGU21-12357.htmlae974a485f413a2113503eed53cd6c53
10.5194/egusphere-egu21-12357
Scopus Count
Except where otherwise noted, this item's license is described as Creative Commons Attribution 4.0 License