The article presents a new hybrid bio-optical transformation (HBT) method for the rapid modelling of bathymetry in coastal areas. The proposed approach exploits free-of-charge multispectral images and their processing by applying limited manpower and resources. The testbed area is a strait between two Greek Islands in the Aegean Sea with many small islets and complex seabed relief. The HBT methodology implements semi-analytical and empirical steps to model sea-water inherent optical properties (IOPs) and apparent optical properties (AOPs) observed by the Sentinel-2A multispectral satellite. The relationships of the calculated IOPs and AOPs are investigated and utilized to classify the study area into sub-regions with similar water optical characteristics, where no environmental observations have previously been collected. The bathymetry model is configured using very few field data (training depths) chosen from existing official nautical charts. The assessment of the HBT indicates the potential for obtaining satellite derived bathymetry with a satisfactory accuracy for depths down to 30 m.

Land subsidence significantly threatens urban infrastructure, agricultural productivity, and environmental sustainability. This study develops a land subsidence susceptibility model by integrating Small Baseline Subset (SBAS) Interferometric Synthetic Aperture Radar (InSAR) data with key geospatial factors using machine learning approaches. The study focuses on the Attica prefecture, Greece, and utilizes SBAS InSAR data from 2015 to 2021 to extract ground deformation velocities by classifying them into four susceptibility levels: stable, low, moderate, and high. The susceptibility results indicate that stable zones constitute 58.2% of the study area, followed by low (27.2%), moderate (11.2%), and high susceptibility zones (3.4%), predominantly concentrated in areas undergoing hydrological stress and urbanization. Random Forest (RF) and XGBoost (XGB) models incorporate a comprehensive set of causal factors, including slope, aspect, land use, groundwater level, geology, and rainfall. The evaluation of the models includes accuracy metrics and confusion matrices. The XGB model achieved the highest performance, recording an accuracy of 94%, with well-balanced predictions across all susceptibility classes. Addressing class imbalance during model training improved the recall of minority classes, though with slight trade-offs in precision. Feature importance analysis identifies proximity to streams, land use, aspect, rainfall, and groundwater extraction as the most influential factors driving subsidence susceptibility. This methodology demonstrates high reliability and robustness in predicting land subsidence susceptibility, providing critical insights for land-use planning and mitigation strategies. These findings establish a scalable framework for regional and global applications, contributing to sustainable land management and risk reduction efforts.

Land subsidence poses a significant risk in built-up environments, particularly in geologically complex and tectonically active regions. In this study, we integrated Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) techniques—Persistent Scatterer Interferometry (PS-InSAR) and Small Baseline Subset (SBAS)—with Global Navigation Satellite System (GNSS) observations to assess ground deformation in the Metamorphosis (MET0) area of Attica, Greece. A Kalman filtering approach was applied to fuse displacement measurements from GNSS, PS-InSAR, and SBAS, reducing noise and improving temporal consistency. Additionally, the PS and SBAS vertical displacement data were fused using Kalman filtering to enhance spatial coverage and refine displacement estimates. The results reveal significant subsidence trends ranging between −10 mm and −24 mm in localized zones, particularly near hydrographic networks and active fault systems. Fault proximity, fluvial processes, and unconsolidated sediments were identified as key drivers of displacement. Random Forest regression analysis, coupled with Partial Dependence analysis, demonstrated that distance to faults, proximity to streams, and the presence of stream drops and debris zones were the most influential factors affecting displacement patterns. This study highlights the effectiveness of integrating multi-sensor remote sensing techniques with data-driven machine learning analysis (Kalman filtering) to improve land subsidence assessment. The findings highlight the necessity of continuous geospatial monitoring for infrastructure resilience and geohazard risk mitigation in the Attica region.

Viticulture requires constant and meticulous care throughout the year to ensure a high-quality harvest. Precision agriculture has significantly advanced, enabling the early detection of potential threats or diseases without harming the crops. Understanding the distinct features of various grapevine varieties, such as chlorophyll content, canopy growth, stress, and interactions with specific soil elements, is crucial. To address these challenges, this study employs multispectral images captured by unmanned aerial vehicles (UAVs), providing a method for exploiting the spectral features of the vine canopies on a field with numerous vine varieties. The primary objective is to cluster different grapevine varieties in the same area based on common spectral characteristics.

Oceanic energy budgets and mixing parameterizations are largely framed around tidal forcing, reflecting the availability of long-term, global datasets for barotropic and baroclinic tides. In contrast, internal waves, particularly Internal Solitary Waves (ISWs), remain poorly represented in global energy frameworks, despite their recognized role in transferring energy across scales, driving localized mixing, and modulating stratification. This imbalance is not only conceptual but observational: the lack of consistent, global datasets has limited the integration of internal wave processes into large-scale circulation and climate-relevant ocean models.ISWs are nonlinear internal waves that propagate over long distances in stratified oceans, linking mesoscale and large-scale forcing to small-scale turbulence. Beyond their surface expressions observable from space, ISWs involve strong internal currents and large vertical displacements of isopycnals, with implications for offshore operations, marine structures, navigation, and ocean energetics. However, their transient nature and wide spatial extent make them particularly challenging to observe systematically, resulting in fragmented and geographically biased observational records.We present the Internal Waves Service (IWS), which provides a first step towards addressing this gap, as a global, open, service-oriented framework for the systematic detection, mapping, and archiving of ISWs from satellite Earth Observation data. The service currently exploits synthetic aperture radar (SAR) imagery acquired by Sentinel-1 in Wave Mode, which provides unique, globally distributed observations of ISW surface signatures. Unlike traditional studies focused on specific regions or short time periods, the IWS processes all Sentinel-1 Wave Mode acquisitions on a continuous basis, enabling consistent global mapping of ISW presence and absence.ISW detection is performed using an AI-assisted classification framework applied to SAR vignettes, supported by expert validation and iterative model refinement. The resulting products form a persistent, standardized dataset documenting spatial patterns and temporal variability of ISW activity across ocean basins.By consolidating previously fragmented observations into a coordinated global dataset, the IWS provides a new observational basis for assessing the role of internal waves within ocean energy pathways. This systematic mapping supports comparative analyses, facilitates model evaluation, and opens the door to more consistent integration of internal wave processes alongside tides in multiscale ocean dynamics and energy budgets. Developed as a community-driven initiative involving 24 research institutions across 12 countries, the IWS is designed to evolve towards broader sensor integration and enhanced spatial coverage, strengthening its relevance for ocean modelling and climate studies.