  The continuous increase in global population and the impact of climate change
on crop production are expected to affect the food sector significantly. In
this context, there is need for timely, large-scale and precise mapping of
crops for evidence-based decision making. A key enabler towards this direction
are new satellite missions that freely offer big remote sensing data of high
spatio-temporal resolution and global coverage. During the previous decade and
because of this surge of big Earth observations, deep learning methods have
dominated the remote sensing and crop mapping literature. Nevertheless, deep
learning models require large amounts of annotated data that are scarce and
hard-to-acquire. To address this problem, transfer learning methods can be used
to exploit available annotations and enable crop mapping for other regions,
crop types and years of inspection. In this work, we have developed and trained
a deep learning model for paddy rice detection in South Korea using Sentinel-1
VH time-series. We then fine-tune the model for i) paddy rice detection in
France and Spain and ii) barley detection in the Netherlands. Additionally, we
propose a modification in the pre-trained weights in order to incorporate extra
input features (Sentinel-1 VV). Our approach shows excellent performance when
transferring in different areas for the same crop type and rather promising
results when transferring in a different area and crop type.
