  The modernization of the Common Agricultural Policy (CAP) requires the large
scale and frequent monitoring of agricultural land. Towards this direction, the
free and open satellite data (i.e., Sentinel missions) have been extensively
used as the sources for the required high spatial and temporal resolution Earth
observations. Nevertheless, monitoring the CAP at large scales constitutes a
big data problem and puts a strain on CAP paying agencies that need to adapt
fast in terms of infrastructure and know-how. Hence, there is a need for
efficient and easy-to-use tools for the acquisition, storage, processing and
exploitation of big satellite data. In this work, we present the Agriculture
monitoring Data Cube (ADC), which is an automated, modular, end-to-end
framework for discovering, pre-processing and indexing optical and Synthetic
Aperture Radar (SAR) images into a multidimensional cube. We also offer a set
of powerful tools on top of the ADC, including i) the generation of
analysis-ready feature spaces of big satellite data to feed downstream machine
learning tasks and ii) the support of Satellite Image Time-Series (SITS)
analysis via services pertinent to the monitoring of the CAP (e.g., detecting
trends and events, monitoring the growth status etc.). The knowledge extracted
from the SITS analyses and the machine learning tasks returns to the data cube,
building scalable country-specific knowledge bases that can efficiently answer
complex and multi-faceted geospatial queries.
