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.

Satellite based data and ground measurements were assimilated in a surface energy balance (SEB) model to assess urban energy fluxes in Abu Dhabi (UAE) metropolitan area during the winter and the summer seasons. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data were used to derive land surface variables such as albedo, emissivity, and land cover. Solar radiation parameters (incoming shortwave and longwave radiation) have been derived from the Spinning Enhanced Visible and Infrared Imager (SEVIRI)/METEOSAT and from a ground station located in the study area. Meteorological variables have also been assimilated into the model. The analysis highlighted the particular characteristics of cities located in desert areas. The irrigated vegetation increases the contribution of latent heat flux (QE) component in the SEB of downtown areas compared to the surrounding desert areas. The hazy atmosphere observed during the summer period has also affected the retrieval of incoming shortwave radiation from satellite-based methods. The sensitivity of the model to this variable has been assessed and evaluated with in situ solar radiation measurements.

Remote sensing data from both LANDSAT and ASTER sensors were used to assess land cover-temperature interactions in Abu Dhabi metropolitan area in 1986 and 2008. The analysis has been performed through the retrieval of Land Surface Temperature (LST) and its correlation with Impervious Surface Area (ISA) and Normalized Difference Vegetation Index (NDVI). The results showed how the impact of vegetation (mainly derived from landscape activities) as well as the contribution of builtup areas, especially tall buildings (which channel the wind flow and shadowing) keep the temperature lower respect to desert areas. At district detail, the analysis of the builtup areas on the main island presented a lower temperature respect to the suburbs: increasing the ISA percentage, the temperature rose from the ISA class 20–40 %‥

In this letter, a method to automatically retrieve building surface temperature maps using hyperspectral imagery is presented. The approach can be conceptually described by considering two different problems. The first consists in the design of an automatic procedure for the extraction of building surfaces from the hyperspectral image. Such an issue has been addressed using both unsupervised and supervised neural networks. The second problem deals with the retrieval of land surface temperature from the same image. The final step is the merging of the temperature map with the building mask. It is worthwhile to observe that the proposed approach aims at retrieving the temperature values by reducing the manual editing and the use of ancillary data to a minimum level. The obtained results show an accuracy in the building identification of 83.7% and a root-mean-square error (rmse) in the temperature retrieval of 1.59 K. The importance of this methodology has to be considered within the studies on urban heat islands, which is becoming an important issue in urban management politics.

The information enhancement of an hyperspectral image through building shadows removal is presented. Starting from the energy conservation model and considering the development of the simulated reflectance algorithm, the study demonstrates how it is possible to exploit the wide spectrum of an hyperspectral image in urban areas allowing the visualization of image details which were shaded by urban structures. Moreover, the described methodology positively affects land classification: the accuracy detection of four land cover classes (vegetation, buildings, asphalt and bare soil) has been improved using as input to a neural network classifier simulated reflectance image instead of the original one. The experimental data consisted of an Airborne Hyperspectral Scanner (AHS) image acquired over the city of Madrid.

Since a few months TerraSAR-X has been acquiring X-band SAR images of the earth surface from space. This contribution reports on a study carried out to understand the main textural features of the X-band radar return from various kinds of surfaces and in particular to assess the potential of images acquired by X-band space borne radars in mapping fire scars and in classifying suburban/agricultural land cover. To this end, a novel unsupervised neural network algorithm, the Textural Self-Organizing Map (TexSOM), based on the textural features of the radar image, has been worked out and tested on areas in Greece and Italy.