Data on Insurance Reported by Researchers at Swiss Federal Institute of Technology (Pixel-based Yield Mapping and Prediction From Sentinel-2 Using Spectral Indices and Neural Networks): Insurance

2023 MAR 10 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- Investigators publish new report on Insurance. According to news reporting originating from Zurich, Switzerland, by NewsRx correspondents, research stated, “Mapping and predicting crop yield on a large scale is increasingly important for use cases such as policy-making, risk insurance and precision agriculture applications at farm and field scale. The higher spatial resolution of Sentinel-2 compared to Landsat allows for satellite-based crop yield mapping even in relatively small scaled agricultural settings such as found in Switzerland and other central European regions.”

Financial supporters for this research include Swiss Federal Office for Agriculture, Swiss National Science Foundation (SNSF).

Our news editors obtained a quote from the research from the Swiss Federal Institute of Technology, “In this study, five years (2017-2021) of cereal crop yield data from a combine harvester were used to model crop yield within-field, on a spatial scale corresponding to the Sentinel-2 pixel level. Three established methods from literature using (i-ii) spectral indices and (iii) raw satellite reflectance as well as (iv) a recurrent neural network (RNN) were chosen for analysis. Although the RNN approach did not outperform the other methods, it was more efficient because of the comparatively simple end-to-end training of the model, resulting in much less time spent on data cleaning and feature extraction needed for spectral index time series analysis. The RNN was also able to discriminate cloudy data by itself, reaching similar performance levels as if using pre-processed, cloud-free data. Modelling was performed on individual years, all years combined and on unseen years using leave-one-year-out crossvalidation. The models performed best when using data from all years (R2 up to 0.88, relative RMSE up to 10.49 %) and showed poor performance when predicting on unseen data years, especially for years with previously unknown weather patterns.”

According to the news editors, the research concluded: “This highlights the importance of yearly model calibration and the need for continuous data collection enabling long time series for future crop yield models.”

This research has been peer-reviewed.

For more information on this research see: Pixel-based Yield Mapping and Prediction From Sentinel-2 Using Spectral Indices and Neural Networks. Field Crops Research, 2023;292. Field Crops Research can be contacted at: Elsevier, Radarweg 29, 1043 Nx Amsterdam, Netherlands. (Elsevier - www.elsevier.com; Field Crops Research - http://www.journals.elsevier.com/field-crops-research/)

The news editors report that additional information may be obtained by contacting Gregor Perich, Swiss Federal Institute of Technology, Crop Sci, Zurich, Switzerland. Additional authors for this research include Lukas Valentin Graf, Helge Aasen, Achim Walter, Mehmet Ozgur Turkoglu, Jan Dirk Wegner and Frank Liebisch.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.fcr.2023.108824. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

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