Earth observation : An integral part of a smart and sustainable city

Over the course of the 21st century, a century in which the urbanization process of the previous one is ever on the rise, the novel smart city concept has rapidly evolved and now encompasses the broader aspect of sustainability. Concurrently, there has been a sea change in the domain of Earth observation (EO) where scientific and technological breakthroughs are accompanied by a paradigm shift in the provision of open and free data. While the urban and EO communities share the end goal of achieving sustainability, cities still lack an understanding of the value EO can bring in this direction, an next a consolidated framework for tapping the full potential of EO and integrating it in their operational modus operandi. The “SMart URBan Solutions for air quality, disasters and city growth” H2020 project (SMURBS/ERA-PLANET) sits at this scientific and policy crossroad, and, by creating bottom-up EO-driven solutions against an array of environmental urban pressures, and by expanding the network of engaged and exemplary smart cities that push the state-of-the-art in EO uptake, brings the international ongoing discussion of EO for sustainable cities closer to home and contributes in this discussion. This paper advocates for EO as an integral part of a smart and sustainable city and aspires to lead by example. To this end, it documents the project's impacts, ranging from the grander policy fields to an evolving portfolio of smart urban solutions and everyday city operations, as well as the cornerstones for successful EO integration. Drawing a parallel with the utilization of EO in supporting several aspects of the 2030 Agenda for Sustainable Development, it aspires to be a point of reference for upcoming endeavors of city stakeholders and the EO community alike, to tread together, beyond traditional monitoring or urban planning, and to lay the foundations for urban sustainability.Peer reviewe

Comparative analysis of Sentinel-1 and PlanetScope imagery for flood mapping of Evros River, Greece

The Evros region in Greece is prone to frequent floods, causing significant damage to infrastructure and communities. It is imperative to have an effective flood monitoring system in place to mitigate the risks associated with these natural disasters. Satellite remote sensing technology can provide vital information for monitoring and assessing the impact of such events. High-resolution satellite imagery and other remote sensing techniques can provide near real-time information on the floods' extent, severity, and dynamics, allowing for prompt and accurate responses of disaster management. This study integrated radar and optical imagery from Sentinel-1 and PlanetScope, respectively, to map the extent of a significant flood event along the Evros River from 12th of January to 01st of February 2021. Moreover, it exploits geomorphometric information through Digital Elevation Models (DEMs) to assess the flood risk zones. The region experienced the highest recorded rainfall in the last 50 years, making the event particularly noteworthy. More specifically, the Hellenic National Meteorological Service recorded an average monthly rainfall of 408 mm for January and 211 mm for the first ten days of February. The outcomes of this study highlight the benefits of interdisciplinary approaches and the synergy within optical and radar imagery, which can offer vital data to local authorities and stakeholders in decision making towards mitigation strategies and resilience in flood risk events.ERATOSTHENES Centre of Excellenc

Satellite Earth Observation Data in Epidemiological Modeling of Malaria, Dengue and West Nile Virus: A Scoping Review

Earth Observation (EO) data can be leveraged to estimate environmental variables that influence the transmission cycle of the pathogens that lead to mosquito-borne diseases (MBDs). The aim of this scoping review is to examine the state-of-the-art and identify knowledge gaps on the latest methods that used satellite EO data in their epidemiological models focusing on malaria, dengue and West Nile Virus (WNV). In total, 43 scientific papers met the inclusion criteria and were considered in this review. Researchers have examined a wide variety of methodologies ranging from statistical to machine learning algorithms. A number of studies used models and EO data that seemed promising and claimed to be easily replicated in different geographic contexts, enabling the realization of systems on regional and national scales. The need has emerged to leverage furthermore new powerful modeling approaches, like artificial intelligence and ensemble modeling and explore new and enhanced EO sensors towards the analysis of big satellite data, in order to develop accurate epidemiological models and contribute to the reduction of the burden of MBDs.JRC.F.7-Knowledge for Health and Consumer Safet