10 Jan 2025 | by

Residents across Cyprus frequently observe vivid shades of red, orange, and crimson during sunrise and sunset, particularly following episodes of Saharan or Middle Eastern dust. These striking colours are the result of well-understood atmospheric optical processes that become more pronounced under specific environmental conditions. As sunlight travels through a greater thickness of the atmosphere near the horizon, shorter wavelengths such as blue and violet are scattered away through Rayleigh scattering, leaving longer red and orange wavelengths to dominate the visible spectrum. When additional aerosols, dust particles, smoke, or water vapour are present in the atmosphere, Mie scattering further enhances the intensity of these warm colours, producing spectacular dawn and dusk skies. Cyprus is particularly susceptible to this phenomenon because of its geographical position at the crossroads of Europe, Asia, and Africa, where dust transported from the Sahara Desert and the Middle East frequently influences atmospheric conditions. Satellite observations, atmospheric models, and ground-based monitoring systems are routinely used to track these aerosol events, helping scientists assess air quality, visibility, and potential health impacts. Beyond creating visually impressive landscapes, these atmospheric observations provide valuable information about climate processes, aerosol transport, and environmental conditions across the Eastern Mediterranean.

25 Apr 2024 | by

The rapid expansion of satellite constellations, aerial sensing platforms, Internet of Things (IoT) devices, and environmental monitoring networks has led to an unprecedented growth in Earth Observation data, commonly referred to as Big Earth Data. This transformation is reshaping how governments, researchers, and industry monitor the planet, enabling continuous observation of environmental changes at regional and global scales. Artificial Intelligence (AI), particularly machine learning and deep learning algorithms, has become an essential tool for extracting meaningful information from these vast datasets. AI-driven approaches are increasingly used to automate land cover classification, detect environmental changes, monitor natural disasters, forecast weather and climate patterns, support precision agriculture, assess water resources, and improve urban planning. These technologies significantly reduce processing time while increasing the accuracy and scalability of geospatial analyses. The integration of AI with cloud computing, high-performance computing infrastructures, and open-access satellite missions such as Copernicus is creating new opportunities for near real-time environmental intelligence. At the same time, important challenges remain, including the need for standardized datasets, improved interoperability between platforms, explainable AI models, data quality assurance, cybersecurity, and ethical governance of automated decision-making systems. As Earth Observation continues to evolve, AI is expected to become a cornerstone technology supporting climate resilience, disaster risk reduction, sustainable development, and evidence-based policymaking.