Is it essential to couple machine learning with crop growth models for accurate predictions of crop yields using satellite remote sensing? Our new publication, “Integrating random forest and crop modeling improves the crop yield prediction of winter wheat and oil seed rape”, answers this question. The manuscript is authored by Maninder Singh Dhillon, Thorsten Dahms, Carina Kübert-Flock (Hlnug, Wiesbaden), Thomas Rummler (Uni Augsburg), Joel Arnault (KIT Garmisch-Partenkirchen), Ingolf Steffan-Dewenter and Tobias Ullmann. The research was conducted in the Bayklif project (https://www.bayklif.de/).
The link to the full paper (open access): https://www.frontiersin.org/articles/10.3389/frsen.2022.1010978/full
