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The recent advances in machine learning and the availability of free and openbig Earth data (e.g., Sentinel missions), which cover large areas with highspatial and temporal resolution, have enabled many agriculture monitoringapplications. One example is the control of subsidy allocations of the CommonAgricultural Policy (CAP). Advanced remote sensing systems have been developedtowards the large-scale evidence-based monitoring of the CAP. Nevertheless, thespatial resolution of satellite images is not always adequate to make accuratedecisions for all fields. In this work, we introduce the notion ofspace-to-ground data availability, i.e., from the satellite to the field, in anattempt to make the best out of the complementary characteristics of thedifferent sources. We present a space-to-ground dataset that containsSentinel-1 radar and Sentinel-2 optical image time-series, as well asstreet-level images from the crowdsourcing platform Mapillary, for grasslandfields in the area of Utrecht for 2017. The multifaceted utility of our datasetis showcased through the downstream task of grassland classification. We trainmachine and deep learning algorithms on these different data domains andhighlight the potential of fusion techniques towards increasing the reliabilityof decisions.

Towards Space-to-Ground Data Availability for Agriculture Monitoring