With regard to the Sustainable Development Goals (SDGs), which address a range of social, economic and environmental challenges faced by the world today, the present work is targeting a specific indicator of relevance for security. A methodology to support the calculation of the SDG indicator 13.1.1 (Number of deaths, missing persons and directly affected persons attributed to disasters per 100,000 population) is presented; while the indicator is currently based on data from national demographic agencies, the proposed method incorporates the use of Earth Observation (EO) data. The workflow has been tested in the Sahel region (Niger), one of the hot-spots for Climate Security concerns.

The impact of climate change on the security and well-being of citizens is increasingly recognized as a threat multiplier, with consequences that can have a local, regional and global impact. A major consequence of this new security paradigm is the so-called Climate Security, which refers to how climate change related events amplify existing risks in society, endangering the safety of citizens, key infrastructures, economies or ecosystems. Earth Observation (EO) has long been regarded as a critical resource for understanding Climate Security scenarios. Together with technologies such as Big Data and Artificial Intelligence (AI), as well as the emergence of the Digital Twins concept, these technologies are providing a key framework for a holistic advanced analysis. The present work aims to present the European Union Satellite Centre (SatCen) approach to better understand Climate Security issues, displaying its different activities in Research and Innovation (R&I) as well as an example of an operational use case, where the construction of a water canal in the Amu Darya river basin could have significant consequences on the access to water for the whole region, leading to possible security concerns.

The impact of climate change on the security and well-being of citizens is increasingly recognized as a threat multiplier, with consequences that can have a local, regional and global impact. Earth Observation (EO) resources have long been regarded as valuable tools for understanding climate security scenarios. The emergence of technologies such as Big Data and Artificial Intelligence (AI) has provided the infrastructure and tools needed for advanced analysis. This paper demonstrates how the integration of ancillary data (e.g. statistics, economics, meteorological) and heterogeneous data could support a better understanding of climate security scenarios. The study utilizes three years of Sentinel-2 data to generate maps of areas that are more susceptible to potential flooding. Normal and anomalous situations were analyzed, exploring the temporal distribution of variables to characterize seasonal cycles in a climate security hotspot: the Sahel region.

The security domain cannot longer be considered as a standalone silo. To properly understand the complex current security scenarios, it is necessary to have a holistic approach and investigate the links of security with different domains (e.g. climate, hazards, health, energy, food). A new security paradigm is emerging and new data, technologies and models are needed to comprehend recent complex dynamics. The goal is being able to reproduce interconnected scenarios through a digital replica of the Earth, with a specific focus on security.

Oil stock estimation has a direct impact on the oil price and is a critical asset in the global economy. The COVID-19 pandemic and the subsequent lockdowns in multiple countries had a big impact on the oil inventories. In this paper, a methodology to monitor oil inventories using Sentinel-1 data is presented. It exploits the differences in the backscatter response with respect to the roof level in floating roof tanks. The methodology doesn't imply advanced processing techniques (such as interferogram or coherence estimation) and it can be complemented with other data sources (e.g. Sentinel-2, VHR) for a more comprehensive foundation of the oil stock estimation.