  The recent advances in machine learning and the availability of free and open
big Earth data (e.g., Sentinel missions), which cover large areas with high
spatial and temporal resolution, have enabled many agriculture monitoring
applications. One example is the control of subsidy allocations of the Common
Agricultural Policy (CAP). Advanced remote sensing systems have been developed
towards the large-scale evidence-based monitoring of the CAP. Nevertheless, the
spatial resolution of satellite images is not always adequate to make accurate
decisions for all fields. In this work, we introduce the notion of
space-to-ground data availability, i.e., from the satellite to the field, in an
attempt to make the best out of the complementary characteristics of the
different sources. We present a space-to-ground dataset that contains
Sentinel-1 radar and Sentinel-2 optical image time-series, as well as
street-level images from the crowdsourcing platform Mapillary, for grassland
fields in the area of Utrecht for 2017. The multifaceted utility of our dataset
is showcased through the downstream task of grassland classification. We train
machine and deep learning algorithms on these different data domains and
highlight the potential of fusion techniques towards increasing the reliability
of decisions.
