  Accurate prediction of crop yield before harvest is of great importance for
crop logistics, market planning, and food distribution around the world. Yield
prediction requires monitoring of phenological and climatic characteristics
over extended time periods to model the complex relations involved in crop
development. Remote sensing satellite images provided by various satellites
circumnavigating the world are a cheap and reliable way to obtain data for
yield prediction. The field of yield prediction is currently dominated by Deep
Learning approaches. While the accuracies reached with those approaches are
promising, the needed amounts of data and the ``black-box'' nature can restrict
the application of Deep Learning methods. The limitations can be overcome by
proposing a pipeline to process remote sensing images into feature-based
representations that allow the employment of Extreme Gradient Boosting
(XGBoost) for yield prediction. A comparative evaluation of soybean yield
prediction within the United States shows promising prediction accuracies
compared to state-of-the-art yield prediction systems based on Deep Learning.
Feature importances expose the near-infrared spectrum of light as an important
feature within our models. The reported results hint at the capabilities of
XGBoost for yield prediction and encourage future experiments with XGBoost for
yield prediction on other crops in regions all around the world.
