In this paper, we present the new research infrastructure of the ERATOSTHENES CoE for aerosol and cloud remote sensing observations and its capabilities to participate in cal/val activities. The research facility is installed less than 2 km from the coastline of Limassol, Cyprus. The multiplatform for atmospheric research consists of a multiwavelength lidar, 35 GHz cloud radar, a microwave radiometer, and ancillary instruments to become a fully ACTRIS cloud and aerosol remote sensing station. Measurements performed at Limassol’s station were already utilized for the validation of the aerosol products of Sentinel 5P and AEOLUS and will be used for the validation of the EarthCARE mission.

This paper explores how the Eratosthenes CoE that has been established in Cyprus through the EXCELSIOR H2020 Teaming project can be a hub for Earth Observation activities in Cyprus and the Eastern Mediterranean, Middle East and North Africa (EMMENA) region. Due to its geographical proximity, EXCELSIOR can become a hub for partners in Middle Eastern and Northern African countries. Cyprus' unique geostrategic position can support Earth Observation from satellite programmes in three continents and provide valuable services in the processes of satellite calibration and validation. Finally, the distinct needs and opportunities that motivate the establishment of an Earth Observation Centre of Excellence in Cyprus are presented in this paper.

The geological, ecological, and biological ecosystems of the planet have changed because of the global climate crisis, and this poses a serious threat to humanity as well as the conservation of agricultural productivity and food security. The European Commission outlined the continent's objective to become climate neutral by 2050 with zero net Greenhouse Gas (GHG) emissions. Soil organic carbon (SOC) is closely related to soil quality and has a significant impact on how soil and plants interact. SOC monitoring gives a unique role in agricultural sustainability thus precise prediction and monitoring of SOC is essential. Remote Sensing (RS) evolution, big data accessibility and Deep Learning (DL) architectures present enormous potential for extensive SOC monitoring. Several RS applications (e.g., Sentinels, MODIS, Landsat etc.) along with machine learning and DL methodologies (e.g., RF, ANN, CNN etc.) used in literature for SOC prediction. The current review paper emphasizes on the latest RS approaches used for SOC monitoring.

Soil organic matter (SOM) is an important component that exists in soils because it is closely related to soil health and fertility. Hence, knowing the existence of SOM in soils is crucial for management corrections. So far laboratory analysis is required for SOM determination. However, such procedures are costly and labor-time consuming. Alternative methodologies for SOM determination are needed to achieve sustainability. The rise of artificial intelligence and machine learning provide promising approaches that can be exploited for this purpose. The aim of this study is to identify the best regression algorithm for SOM prediction for citrus planted soils. Several machine learning approaches are investigated, including adaptive boosting, gradient boosting, random forest, and multi-layer perceptron neural network.

The rapid growth of Earth Observation (EO) and Remote Sensing (RS) data has underscored the critical need for identifying optimal solutions to effectively manage EO Big Data. This entails simplifying data sharing and facilitating adaptation across multidisciplinary applications to better serve the research community. Various architectures and structures have been developed to manage and deploy these data in an analysis-ready format. In this study, we provide a concise overview of an advanced EO Big Data infrastructure located in Limassol, Cyprus, comprising diverse data sources acquired from an acquisition station, an atmospheric ground base station, and various living labs. Additionally, we present the EO data ecosystem of Cyprus that is specifically designed to efficiently store the aforementioned data.

This study examines the long-term dynamics of the Paphos forest in Cyprus using Landsat satellite data for Vegetation Indices (VIs), MODIS data for evapotranspiration, and CHIRPS data for precipitation from 1991 to 2022. Sen's slope method was applied to analyse the trends in the data, revealing statistically significant positive trends in the vegetation indices despite the nearly constant precipitation, indicating increased forest vegetation over the past 30 years. Scatterplots were created mainly to examine correlations within the VIs and precipitation data but with low R-squared values ranging between 0.15-0.44. The study outcomes highlight a complex relationship with evapotranspiration and a weak correlation between precipitation and vegetation indices. These findings could be essential in understanding how forests work, especially in a semi-arid environment like Cyprus.