  In this work, we introduce a recently developed early classification
mechanism to satellite-based agricultural monitoring. It augments existing
classification models by an additional stopping probability based on the
previously seen information. This mechanism is end-to-end trainable and derives
its stopping decision solely from the observed satellite data. We show results
on field parcels in central Europe where sufficient ground truth data is
available for an empiric evaluation of the results with local phenological
information obtained from authorities. We observe that the recurrent neural
network outfitted with this early classification mechanism was able to
distinguish the many of the crop types before the end of the vegetative period.
Further, we associated these stopping times with evaluated ground truth
information and saw that the times of classification were related to
characteristic events of the observed plants' phenology.
