  Big streams of Earth images from satellites or other platforms (e.g., drones
and mobile phones) are becoming increasingly available at low or no cost and
with enhanced spatial and temporal resolution. This thesis recognizes the
unprecedented opportunities offered by the high quality and open access Earth
observation data of our times and introduces novel machine learning and big
data methods to properly exploit them towards developing applications for
sustainable and resilient agriculture. The thesis addresses three distinct
thematic areas, i.e., the monitoring of the Common Agricultural Policy (CAP),
the monitoring of food security and applications for smart and resilient
agriculture. The methodological innovations of the developments related to the
three thematic areas address the following issues: i) the processing of big
Earth Observation (EO) data, ii) the scarcity of annotated data for machine
learning model training and iii) the gap between machine learning outputs and
actionable advice.
  This thesis demonstrated how big data technologies such as data cubes,
distributed learning, linked open data and semantic enrichment can be used to
exploit the data deluge and extract knowledge to address real user needs.
Furthermore, this thesis argues for the importance of semi-supervised and
unsupervised machine learning models that circumvent the ever-present challenge
of scarce annotations and thus allow for model generalization in space and
time. Specifically, it is shown how merely few ground truth data are needed to
generate high quality crop type maps and crop phenology estimations. Finally,
this thesis argues there is considerable distance in value between model
inferences and decision making in real-world scenarios and thereby showcases
the power of causal and interpretable machine learning in bridging this gap.
