2nd EARSeL SIG Workshop on Agriculture

Agriculture plays a key role in the Earth’s future, with strong ties to many United Nations Sustainable Development Goals (SDG). Earth observation (EO) is a key for achieving the SDGs and provides support to agricultural practices in several ways. Precision farming builds on satellite imagery to crops in terms of phenology, plant health, water stress, yield prediction, pest detection, natural hazards-related damages and many others. Scaling-up, agricultural management directly and indirectly impacts carbon sequestration, habitat quality and landscape configuration. Monitoring agricultural systems at different spatial and temporal scales is proving to give insights on positive and negative effects of agriculture management and to support near real time decisions from farmers along the entire crop growing season. Against this background and given the steadily increasing number of Earth Observation platforms (satellite, aerial and drones), this workshop aims to bring together researchers, industry, data users and stakeholders that share an interest in using Earth Observation for applications in the agriculture domain from the field to the continental level.

List of topics

  • EO-based Services and Products for Agriculture
    • support for risk modeling and management in agriculture
    • evaluation of direct and indirect economic benefits of EO products and services in agriculture
    • EO data integration with other services (e.g. agro-meteo networks, phenological networks etc…)
    • mapping yield for crop monitoring and optimization
    • mitigation of environmental impact in farming activities (e.g. monitoring gas emissions, reduction of impact from treatments, optimization of water usage etc….)
    • damage assessment for multiple purposes – insurance, economic loss estimation, etc…
    • definition of quality assurance/quality control (QA/QC) products specific for agricultural applications
  • Regional/Continental/Global Applications of EO in Agriculture
    • mapping and monitoring biodiversity in agricultural landscapes
    • monitoring crop management practices (e.g. land use intensity, growing strategies s) in the context of climate change adaptation and mitigation
    • advantages and pitfalls of deep/machine learning and AI-based approaches
    • spatial and temporal capability of generalization of EO-based models, wide area mapping / historical land use mapping
    • supporting Common Agricultural Policies (CAP)


Scientific committee

Link to the SIG webpage