The Geosciences Instrumentation and geological process modelling by using multiscale satellite, UAV and ground‑based data session offers an open forum for presenting advances in geoscientific instrumentation, methods, data systems, and modelling. The focus is on innovative techniques and integrated multiscale observations—from satellites, UAV platforms, and ground-based sensors—to investigate crustal processes and support environmental and engineering applications.
Contributions are welcome from all geoscience measurement domains, including optical, electromagnetic, seismic, acoustic and gravity methods, as well as studies on data infrastructure, multi-sensor integration, and novel processing or modelling workflows. The session encourages cross-disciplinary interaction to stimulate new insights and foster breakthroughs in applied geosciences.
Given the growing relevance of UAVs in geophysical surveys, submissions on drone-based instrumentation, data acquisition strategies, and case studies across magnetics, electromagnetics, gravity, GPR, seismics, and remote sensing are particularly encouraged. Applications related to environmental monitoring, natural hazards, and security—such as archaeological prospection, waste site characterization, UXO detection, dam inspection, and seismic hazard monitoring—also fall within the scope.
By bringing together researchers, practitioners, and industry, the session aims to highlight emerging trends, opportunities, and challenges shaping the future of geoscience instrumentation and modelling.

Modern environmental and societal pressures, ranging from intensifying climate extremes to resource insecurity, require an evolution in how we observe, measure, and govern our changing planet. Traditional boundaries between hardware engineering, data science, and public policy are increasingly insufficient for addressing complex global crises. This session highlights how next-generation geoscience instrumentation and data systems, such as IoT networks, autonomous platforms, and edge AI, serve as drivers of sustainable development and evidence-based governance.
While sensing innovation accelerates, critical gaps remain in translating raw technical measurements into actionable policy and equitable societal impact. This session examines how digital tools interact with institutional frameworks and local communities, focusing on bridging the digital divide between the Global South and North to promote digital sovereignty and inclusive participation.
Contributions are invited at the intersection of science, technology, and society, welcoming research involving the quadruple helix, engaging academia, public authorities, industry, and civil society, to co-design monitoring infrastructures and governance frameworks.
Areas of interest and priority:
• Innovative hardware, low-cost sensors, embedded systems, and autonomous platforms tailored for vulnerable environments;
• Edge AI and automated on-device processing algorithms for real-time anomaly detection and autonomous instrument responses;
• Multisource data integration fusing in-situ sensors with satellite observations to power continuous early-warning systems;
• Digital pipelines and spatial data infrastructures linking in-situ observations directly to institutional decision-making and risk reduction frameworks;
• Quadruple-helix partnerships and participatory co-design models incorporating civil society and local stakeholders into the monitoring lifecycle;
• Digital sovereignty, technology transfer, ethical data governance, and knowledge-sharing initiatives between the Global South and North;
• Applications aligning geoscience data systems with current international agreements and United Nations mandates, including the SDGs, the Sendai Framework and Ecosystem Restoration;
• Real-world field deployments and case studies addressing environmental change, natural hazards, and resource management.

The MacGyver session focuses on novel sensors made, or data sources unlocked, by scientists. All geoscientists are invited to present:
- new sensor systems, using technologies in novel or unintended ways,
- new data storage or transmission solutions sending data from the field with LoRa, WIFI, GSM, or any other nifty approach,
- started initiatives (e.g., Open-Sensing.org) that facilitate the creation and sharing of novel sensors, data acquisition and transmission systems.

Connected a sensor to an Arduino or Raspberri Pi? Used the new Lidar in the new iPhone to measure something relevant for hydrology? 3D printed an automated water quality sampler? Or build a Cloud Storage system from Open Source Components? Show it!

New methods in hydrology, plant physiology, seismology, remote sensing, ecology, etc. are all welcome. Bring prototypes and demonstrations to make this the most exciting Poster Only (!) session of the General Assembly.

This session is co-sponsered by MOXXI, the working group on novel observational methods of the IAHS.

Effective and enhanced hydrological monitoring is essential for understanding water-related processes in a rapidly changing world. Image-based river monitoring, remote and proximal sensing, low-cost and opportunistic sensors, citizen science and artificial intelligence are reshaping the way hydrological processes are observed across scales, environments and conditions. Yet the value of these innovations depends on methodological rigour: new observational approaches need to be critically evaluated, benchmarked against established methods and integrated with existing datasets before their contribution to process understanding, modelling and operational hydrology can be established.

This session is co-sponsored by MOXXI (Measurements and Observations in the XXI century), the IAHS working group on novel observational methods, and provides a forum for research in which observation itself is the subject of investigation. We invite contributions on:

• Disruptive and Innovative sensors and technologies in hydrology (e.g., UAS, camera systems – RGB, thermal, multispectral and hyperspectral – low-cost and open-hardware sensors, distributed fibre-optic sensing).
• Advancing opportunistic sensing strategies in hydrology (e.g., commercial microwave links, GNSS reflectometry, smartphones, personal weather stations).
• Automated and semi-automated methods for extracting hydrological variables (e.g., water level, flow velocity, discharge, turbidity, plastic transport and river health parameters), including image processing, machine learning, data fusion, and edge-computing.
• Critical evaluation, benchmarking and intercomparison of observational approaches, datasets and products, including calibration/validation and uncertainty quantification.
• Integration of novel and conventional observations, and new approaches to long-term hydrological monitoring.
• Innovative citizen science and crowd-based methods for monitoring hydrological extremes.
• Novel strategies to enhance the detail and accuracy of observations in remote areas or data-scarce contexts.
• Demonstrations of how novel observations advance process understanding, model development and operational practice.•

The goal of this session is to bring together scientists advancing hydrological monitoring, to foster a critical discussion on the reliability and added value of emerging observational approaches, and to explore how these innovations can be scaled up to larger applications.

A wide range of geo-electromagnetic methods, including natural source magnetotelluric, time-domain, and frequency-domain controlled source EM, as well as DC resistivity and induced polarization are uniquely sensitive to the earth’s electrical properties and are capable of probing from shallow depths near the surface to even hundreds of kilometers into the Earth's crust. They are invaluable for revealing subsurface structures, fluid distributions, mineral resources, tectonic features, and even engineered infrastructure. Traditionally essential in resource exploration, geo-electromagnetic methods are now becoming increasingly relevant in addressing new global challenges related to energy systems, the impacts of climate change, environmental problems, and urban development and resilience.

This session serves as an annual platform for showcasing the latest advancements in geo-electromagnetic research. We encourage contributions from a broad range of topics, including methodological breakthroughs, novel field observations, theoretical advancements, and case studies. This year, we particularly welcome submissions that highlight innovative uses of geo-electromagnetic methods in emerging areas—whether through state-of-the-art instrumentation, unconventional applications, or studies with significant societal or environmental relevance.

The investigation of the surfaces and internal structures of planetary bodies provides key insights into their formation and evolution. The surfaces of planets and moons can be studied using cameras, multispectral data, geochemical measurements, and laboratory experiments. Geophysical datasets—especially seismic, gravity and (electro)magnetic observations—are key to inferring the internal structure and dynamics of terrestrial and giant planets, their moons, as well as asteroids and planetesimals.
The combination of surface observations and laboratory experiments with geophysical modeling is crucial to reconstruct the internal structure of planetary bodies and to explore the geodynamic processes that have shaped planetary surfaces and interiors. For instance, multispectral imaging and experimental analyses link remote sensing data to mineralogical and physical properties, offering insights into the composition of outer and internal shells. Gravity and magnetic data together with altimetry measurements provide constraints on the differentiation, density structure, and mechanical properties (i.e., rheology, elastic properties) of planetary interiors. Terrestrial analogs (e.g., terrestrial lava tubes, impact craters, volcanic products, tectonic structures, glaciers, subglacial lakes, etc.) are also investigated through a multidisciplinary approach that combines surface observations with modeling of magnetic and gravity data to understand the geological, volcanic, and tectonic processes across terrestrial planets and icy satellites. These complementary approaches, together with geological mapping and interpretation, provide an integrated framework for how planetary bodies formed, differentiated, and evolved.
This session focuses on the instruments, measurement techniques, modeling approaches, geological interpretation, terrestrial analogues and laboratory studies that provide important constraints on the evolution of planetary surfaces and interiors. We invite contributions addressing current methods and innovative strategies to overcome existing scientific/technical/methodological challenges. Results from past, ongoing, and forthcoming missions, integrative multi-dataset analyses, and forward-looking exploration concepts are also encouraged. The session aims to provide an overview of the latest observations, methods, and experiments that allow exploring the processes shaping planetary bodies and outline pathways for major discoveries in the coming decades.

Earth Observation (EO) offers a powerful means of monitoring changes in climate, ecosystems, and human environments at both global and local scales. These observations generate a wide array of climate and environmental variables, and they are delivered as Analysis-Ready Data (ARD). While ARD is globally accessible and scientifically robust, it might lack the specificity and contextual relevance required to effectively address local challenges. To bridge this gap, ARD must be transformed into Action-Ready Information (ARI): tailored data products and insights that support local decision-making and reflect community priorities. This transformation depends on co-creation, a collaborative process involving local communities, scientists, engineers, policymakers, and private sector stakeholders. For example, by integrating satellite EO with locally collected data from ground, water, and airborne platforms, we can enhance data granularity, validate satellite outputs, and generate customized, equitable, and actionable solutions. This session will explore how data can be harnessed to support environmental monitoring, local climate mitigation and adaptation, and sustainable development. It will emphasize the importance of identifying gaps between global datasets and local needs, and present strategies to close these gaps through innovation (e.g. new technologies and open FAIR science), inclusive engagement, and capacity building. Economic and policy dimensions will also be addressed, including the sustainability of community-led initiatives, the role of citizen science, funding mechanisms, and scalable technologies that enhance data utility for local solutions. The practical implementation challenges confronting policymakers when seeking to engage with EO data, particularly in the context of constrained policy capacities, will also be discussed. We invite participants from across/around the EO ecosystem: researchers in both physical and social sciences, community leaders, and stakeholders from policy and business sectors. We do not limit us only to satellite EO. We do consider non-EO observations and data, and their applications. We will share case studies, identify synergies between global and local efforts, and co-create knowledge that informs both local action and global strategies. By synthesizing diverse experiences, this session aims to advance EO as a tool for addressing the interconnected climate and environmental challenges we face locally and globally.

Time series are a common type of data generated by observational and modelling efforts across Earth, environmental and space sciences. Long-term observations are particularly important for understanding gradual changes and assessing risks, yet are often difficult to sustain and fund. Their characteristics can vary substantially, from short to long records, linear to nonlinear dynamics, univariate to multivariate data, and single- to multi-scale variability. These differences call for both tailored methodologies and general approaches.

A key challenge is distinguishing random fluctuations from long-term changes in order to better understand processes within and across Earth system components. This requires knowledge of temporal variability and, often, sufficiently long observations. For example, reliable sea-level trends may require several decades of continuous measurements because of decadal variability. Likewise, the stochastic variability of geophysical time series can exhibit power-law scaling, requiring long records for robust statistical assessment.

Time series analysis encompasses a broad range of tasks, including:
- characterizing nonlinear variability in the time and/or frequency domain;
- quantifying complexity, predictability and scaling properties;
- identifying statistical interdependencies within and between time series;
- distinguishing co-variability from causal relationships;
- reducing dimensionality and identifying meaningful modes of variability; and
- developing stochastic and deterministic statistical or dynamical models.

This session invites contributions on the development and application of modern methods for analysing observational and model time series across the EGU community, including geophysical, geodynamic, oceanographic, geodetic and climate observations from terrestrial observatories and remote sensing. Contributions addressing advances in sensors, instrumentation, monitoring, analysis and interpretation, as well as comparisons of different approaches, are welcome. Studies using novel methods, including AI, for the analysis of long time series are particularly encouraged. We aim to foster interdisciplinary exchange and cross-fertilization between different EGU divisions.

In recent years, technologies based on Artificial Intelligence (AI), such as image processing, smart sensors, and intelligent inversion, have garnered significant attention from researchers in the geosciences community. These technologies offer the promise of transitioning geosciences from qualitative to quantitative analysis, unlocking new insights and capabilities previously thought unattainable.
One of the key reasons for the growing popularity of AI in geosciences is its unparalleled ability to efficiently analyze vast datasets within remarkably short timeframes. This capability empowers scientists and researchers to tackle some of the most intricate and challenging issues in fields like Geophysics, Seismology, Hydrology, Planetary Science, Remote Sensing, and Disaster Risk Reduction.
As we stand on the cusp of a new era in geosciences, the integration of artificial intelligence promises to deliver more accurate estimations, efficient predictions, and innovative solutions. By leveraging algorithms and machine learning, AI empowers geoscientists to uncover intricate patterns and relationships within complex data sources, ultimately advancing our understanding of the Earth's dynamic systems. In essence, artificial intelligence has become an indispensable tool in the pursuit of quantitative precision and deeper insights in the fascinating world of geosciences.
For this reason, aim of this session is to explore new advances and approaches of AI in Geosciences.

Geochemical mapping is rapidly evolving from classical interpolation toward integrated workflows combining robust multivariate analysis, geostatistics, and artificial intelligence (AI). Modern environmental and exploration geochemistry relies on high-dimensional, multi-source datasets requiring advanced methods to unravel complex spatial patterns, quantify uncertainty, and support decision-making. Traditional approaches often struggle with non-linear relationships, compositional constraints, and heterogeneous data integration. This session highlights the shift toward next-generation analytical frameworks capable of handling contemporary geochemical challenges.

We welcome methodological and applied contributions, including:

- Advanced multivariate statistics: PCA, factor analysis, robust/fuzzy clustering, compositional data analysis (CoDA), ilr transformations, and handling censored data.
- Geostatistical modeling: Variography, kriging variants (ordinary, universal, co-kriging, indicator), sequential Gaussian simulation, multiple-point statistics, and uncertainty assessment.
- Machine & Deep Learning: Random forests, gradient boosting, SVMs, neural networks (CNNs, GNNs) for anomaly detection, source apportionment, and predictive mapping.
- Hybrid approaches: Integration of geological knowledge, process-based understanding, GIS, and AI-driven models to improve interpretability and physical consistency.
- Data infrastructures: Science- and AI-ready data standards, reproducible workflows, and open-source tools (R, Python, GIS).

We particularly encourage studies that:
a) Demonstrate novel combinations of multivariate statistics, geostatistics, and AI in environmental, hydrogeochemical, or mineral exploration contexts;
b) Address challenges such as spatial heterogeneity, imbalanced training data, high dimensionality, model interpretability, and uncertainty propagation;
c) Present validated case studies in contamination assessment, radiological risk, baseline definition, or mineral resource targeting;
d) Explore emerging topics, including deep learning for irregular spatial patterns, AI-assisted source identification, transfer learning, and domain-knowledge constraints;
e) Develop practical tools bridging innovation and end-user applications in academia, industry, and regulatory agencies.

The radioactive materials are known as polluting materials that are hazardous for human society, but are also ideal markers in understanding dynamics and physical/chemical/biological reactions chains in the environment. Therefore, man-made radioactive contamination involves regional and global transport and local reactions of radioactive materials through atmosphere, soil and water system, ocean, and organic ecosystem, and its relations with human and non-human biota. The topic also involves hazard prediction, risk assessment, nowcast, and countermeasures, which is now urgent important for the nuclear power plants in Ukraine, the Middle East, etc.

By combining long monitoring data (> halftime of Cesium 137 after the Chornobyl Accident in 1986, 16 years after the Fukushima Accident in 2011, and other events), we can improve our knowledgebase on the environmental behavior of radioactive materials and its environmental/biological impact. This should lead to improved monitoring systems in the future including emergency response systems, acute sampling/measurement methodology, and remediation schemes for any future nuclear accidents. Furthermore, the discharge of ALPS-treated water into the ocean, carried out as part of the decommissioning of the Fukushima Daiichi Nuclear Power Station, has attracted international attention and demonstrated that decommissioning a nuclear power plant that has suffered an accident requires a fundamentally different approach from that of a conventional decommissioning. Studies on past nuclear contamination events and other environmental radioactivity datasets are also welcome.

The following specific topics have traditionally been discussed:
(a) Atmospheric Science (emissions, transport, deposition, pollution);
(b) Hydrology (transport in surface and ground water system, soil-water interactions);
(c) Oceanology (transport, bio-system interaction);
(d) Soil System (transport, chemical interaction, transfer to organic system);
(e) Forestry;
(f) Natural Hazards (warning systems, health risk assessments, geophysical variability);
(g) Measurement Techniques (instrumentation, multipoint data measurements);
(h) Ecosystems (migration/decay of radionuclides).

Subsurface energy and environmental systems like geothermal energy systems, geological carbon storage, underground hydrogen storage, groundwater flow systems, etc., play a vital role for mitigating climate change and achieving net-zero. At the same time, their complex, heterogeneous, multiscale, and uncertain nature presents major challenges for modelling, prediction, monitoring, and decision-making, particularly in multiphysics and multiscale model coupling, data integration, and real-time monitoring and control.
Artificial intelligence (AI) and scientific machine learning (SciML) are creating new opportunities to address these challenges. This session aims to bring together researchers from geoscience, hydrology, subsurface energy, computational science, and AI to discuss recent advances at the interface between these fields. We invite contributions on the development and application of AI and SciML methods for subsurface energy and environmental systems. Topics include, but are not limited to, physics-informed machine learning, neural operators, reduced-order modelling, surrogate and generative modelling, multimodal data fusion, digital twins, explainable and trustworthy AI, large language models, and agentic AI.
The session will provide a platform for interdisciplinary exchange on emerging methods, practical applications, current limitations, and future research directions.

Time series are a common type of data generated by observational and modelling efforts across Earth, environmental and space sciences. Long-term observations are particularly important for understanding gradual changes and assessing risks, yet are often difficult to sustain and fund. Their characteristics can vary substantially, from short to long records, linear to nonlinear dynamics, univariate to multivariate data, and single- to multi-scale variability. These differences call for both tailored methodologies and general approaches.

A key challenge is distinguishing random fluctuations from long-term changes in order to better understand processes within and across Earth system components. This requires knowledge of temporal variability and, often, sufficiently long observations. For example, reliable sea-level trends may require several decades of continuous measurements because of decadal variability. Likewise, the stochastic variability of geophysical time series can exhibit power-law scaling, requiring long records for robust statistical assessment.

Time series analysis encompasses a broad range of tasks, including:
- characterizing nonlinear variability in the time and/or frequency domain;
- quantifying complexity, predictability and scaling properties;
- identifying statistical interdependencies within and between time series;
- distinguishing co-variability from causal relationships;
- reducing dimensionality and identifying meaningful modes of variability; and
- developing stochastic and deterministic statistical or dynamical models.

This session invites contributions on the development and application of modern methods for analysing observational and model time series across the EGU community, including geophysical, geodynamic, oceanographic, geodetic and climate observations from terrestrial observatories and remote sensing. Contributions addressing advances in sensors, instrumentation, monitoring, analysis and interpretation, as well as comparisons of different approaches, are welcome. Studies using novel methods, including AI, for the analysis of long time series are particularly encouraged. We aim to foster interdisciplinary exchange and cross-fertilization between different EGU divisions.

The concept of Earth as the sole body in the Solar System with liquid water that can harbor microbial life has been overturned by the discovery of multiple 'ocean worlds'. The Solar System is home to several planetary bodies with subsurface oceans of liquid water, including icy satellites such as Europa, Ganymede, Callisto, Enceladus, Titan and Triton, as well as dwarf planets like Pluto, and chief among these ocean worlds, the Earth. Furthermore, new icy and ocean worlds are being continuously discovered in other planetary systems as well. Like Earth, the exploration of these oceans includes both aspects of planetary evolution and habitability. The geodynamic role of oceans in planetary evolution is thus a crucial aspect of understanding not only planet formation, but the onset of biological activity as well. In what ways can the oceans of Earth serve as analogs for other oceans of the Solar System? What instrumentation can be implemented on the Earth now to further our understanding of these ocean worlds, and what technological advances might we expect in future exploration of subsurface liquid water environments beyond Earth?

This session focuses on analog sites, laboratory simulation, modeling, instrumentation and mission proposals. Coordination between Earth, marine and planetary science communities is encouraged, as well as emphasis on upcoming (e.g. JUICE and Dragonfly) and proposed missions (e.g. Enceladus Orbilander). Analog sites might encompass either geological or biological themes in the broader frame of habitability. Interfaces of ice-water (e.g. underside of floating ice shelfs and subglacial lakes), clathrate-water (e.g. ocean floor sediments, veins/fractures/faults, layered horizons and atmosphere particulates), seafloor-ocean, and rock-ice (i.e. glaciers) are of particular curiosity. Instrumentation includes sensors, buoys, submersibles, drilling and coring, as well as satellite instrumentation (e.g. spectrometers, magnetometers and gravimeters).

The investigation of the surfaces and internal structures of planetary bodies provides key insights into their formation and evolution. The surfaces of planets and moons can be studied using cameras, multispectral data, geochemical measurements, and laboratory experiments. Geophysical datasets—especially seismic, gravity and (electro)magnetic observations—are key to inferring the internal structure and dynamics of terrestrial and giant planets, their moons, as well as asteroids and planetesimals.
The combination of surface observations and laboratory experiments with geophysical modeling is crucial to reconstruct the internal structure of planetary bodies and to explore the geodynamic processes that have shaped planetary surfaces and interiors. For instance, multispectral imaging and experimental analyses link remote sensing data to mineralogical and physical properties, offering insights into the composition of outer and internal shells. Gravity and magnetic data together with altimetry measurements provide constraints on the differentiation, density structure, and mechanical properties (i.e., rheology, elastic properties) of planetary interiors. Terrestrial analogs (e.g., terrestrial lava tubes, impact craters, volcanic products, tectonic structures, glaciers, subglacial lakes, etc.) are also investigated through a multidisciplinary approach that combines surface observations with modeling of magnetic and gravity data to understand the geological, volcanic, and tectonic processes across terrestrial planets and icy satellites. These complementary approaches, together with geological mapping and interpretation, provide an integrated framework for how planetary bodies formed, differentiated, and evolved.
This session focuses on the instruments, measurement techniques, modeling approaches, geological interpretation, terrestrial analogues and laboratory studies that provide important constraints on the evolution of planetary surfaces and interiors. We invite contributions addressing current methods and innovative strategies to overcome existing scientific/technical/methodological challenges. Results from past, ongoing, and forthcoming missions, integrative multi-dataset analyses, and forward-looking exploration concepts are also encouraged. The session aims to provide an overview of the latest observations, methods, and experiments that allow exploring the processes shaping planetary bodies and outline pathways for major discoveries in the coming decades.

This session invites contributions on the latest developments and results in lidar remote sensing of the atmosphere, covering • new lidar techniques as well as applications of lidar data for model verification and assimilation, • ground-based, airborne, and space-borne lidar systems, • unique research systems as well as networks of instruments, • lidar observations of aerosols and clouds, thermodynamic parameters and wind, and trace-gases. Atmospheric lidar technologies have shown significant progress in recent years. While, some years ago, there were only a few research systems, mostly quite complex and difficult to operate on a longer-term basis because a team of experts was continuously required for their operation, advancements in laser transmitter and receiver technologies have resulted in much more rugged systems nowadays, many of which are already operated routinely in networks and several even being fully automated and commercially available. Consequently, also more and more data sets with very high resolution in range and time are becoming available for atmospheric science, which makes it attractive to consider lidar data not only for case studies but also for extended model comparison statistics and data assimilation. Here, ceilometers provide not only information on the cloud bottom height but also profiles of aerosol and cloud backscatter signals. Scanning Doppler lidars extend the data to horizontal and vertical wind profiles. Raman lidars and high-spectral resolution lidars provide more details than ceilometers and measure particle extinction and backscatter coefficients at multiple wavelengths. Other Raman lidars measure water vapor mixing ratio and temperature profiles. Differential absorption lidars give profiles of absolute humidity or other trace gases (like ozone, NOx, SO2, CO2, methane etc.). Depolarization lidars provide information on the shapes of aerosol and cloud particles. In addition to instruments on the ground, lidars are operated from airborne platforms in different altitudes. Even the first space-borne missions are now in orbit while more are currently in preparation. All these aspects of lidar remote sensing in the atmosphere will be part of this session.

The study of water-related ecosystems covers a wide range of applicative contexts, entailing many scientific challenges and several diversified technological solutions.
Nowadays, the sustainable management of water resources requires a holistic approach, which attains to the soil, vegetation and all the living things interacting with the water.
The transition from the mere monitoring of the processes related to water systems to the wider concept of “water habitats”, implies the study of such ecological interactions in various possible scenarios, which are often characterised by a strong relationship between natural and anthropogenic contexts.
In this challenging framework, research activities aimed at developing efficient monitoring technologies and management strategies are encouraged to embrace a highly multidisciplinary approach. Here, water management meets noticeable ecological, economic and social implications, and the public awareness of such implications is rapidly growing.
Accordingly, scientific/technological advancements have to go beyond the observation of water bodies and their related processes and infrastructures, by extending the scope to the water habitats and the many measurable indicators of their functions and health status, directly or indirectly related to water, such as water quality, biodiversity, plant ecophysiology, and resilience to environmental extremes.

This session welcomes contributions related to the monitoring of water systems and their characteristic habitats about:
• design of field measurement instrumentation
• development of new sensing techniques, innovative field experiments
• application of remote sensing products
• advancements in sensor networks
• Integration between sensor systems and computational tasks
• Investigations about data science aspects, e.g. geospatial analyses, big data and AI applications.

Contributions may regard (but are not limited to) rivers & lakes, wetlands, irrigated areas, forests and natural habitats, coastal zone, urban habitats and water infrastructures, including distribution networks. Both qualitative and quantitative assessments are appreciated.
Studies regarding groundwater monitoring and management and its interaction with surface processes are also relevant to this session and are very encouraged.

Uncrewed Aircraft Systems (UAS, also commonly referred to as drones, UAV or RPAS) are an emerging technology that is significantly expanding observational capabilities across the geosciences. The rapid development of these platforms (including multicopters, fixed-wing UAS, and tethered systems) combined with major advances in miniaturized payloads—spanning meteorological sensors, multispectral/hyperspectral cameras, and geophysical instruments—has led to a rapidly growing dataset that supports diverse scientific disciplines.
This session invites abstracts discussing scientific contributions using UAS across all fields of geosciences. Topics of interest include, but are not limited to:
• Atmospheric and climate sciences: boundary-layer research, weather prediction, urban environment, and climate monitoring networks.
• Agricultural and environmental sciences: precision agriculture, soil characterization, hydrology, and ecology.
• Geophysics and volcanology: high-resolution drone-borne geophysical surveying (e.g., magnetometry, GPR, EMI), site zonation, and hazard monitoring.
We welcome presentations on the development of novel platforms and instrumentation, recent measurement efforts and field campaigns, data analysis and synthesis, and other scientific interpretations of UAS-based datasets to improve process understanding, numerical model prediction, and data assimilation.

Thermal remote sensing is an increasingly established technique employing passive sensors to deriveEarth’s surface properties from the radiation emitted in the Thermal Infrared (TIR) domain. Its main focus is the thermal state of an object or surface, together with the associated surface temperature and emissivity. These properties are relevant across geological, environmental, climatic, agricultural, biological, and engineering applications.

Recent technological advances have driven the development of TIR remote sensing: satellite sensors and data infrastructure systems can now acquire and manage large volumes of high-fidelity TIR data at a wide range of spatial and temporal resolutions. Besides airborne and ground-based systems, Unmanned Aerial Systems (UAS) are increasingly used as versatile platforms that combine high spatial resolution with flexible temporal revisit. Together with a growing catalogue of current and upcoming missions, this makes it a timely moment to take stock of where the field stands.

This session addresses established and emerging research directions in TIR remote sensing and discusses the community's upcoming challenges. We welcome contributions on new frontiers, case studies, and data-integration analysis related to:

• Geosciences: volcanoes, hydrothermal systems, geothermal potential, mineral exploration, rare earths, cryosphere.

• Climate, Urban Systems, and Ecosystems: urban heat islands, global warming impacts, ecosystem stress, forest health, fire risk assessment, water management.

• Agriculture and Precision Farming: crop stress monitoring, irrigation management, soil analysis and pest/disease monitoring.

• Technological and Methodological Innovations: new sensors for satellite, airborne, UAS and in-situ platforms, multi-platform and/or multi-sensor data integration, Cal/Val activities.

• Data Processing and Infrastructure: approaches for managing and processing large TIR datasets, data fusion techniques, advanced algorithms for atmospheric correction and temperature and emissivity separation.

Multi-disciplinary studies and contributions from Early Career Scientists are especially welcome.

Invited Speaker: Sabine Chabrillat, Helmholtz Centre for Geosciences (GFZ).

Ground-based networks allow for an increased understanding of natural physical processes that can reduce their uncertainty at local and regional scale as well as extend the observational record to support the development of longer-term datasets. Additionally, real-time monitoring can inform decisions and rapid response. Monitoring networks serve an important role within the research community, providing a backbone of data to support modelling, satellite data product validation, and short-term measurement campaigns. Ongoing collaboration, communication, and promotion of monitoring network developments and data products is necessary to fully leverage benefits from such networks.
This session is based on the advancements and applications of the ground-based monitoring networks and sensors in the several frameworks of the Earth Science, among which:
- Seismology and Solid Earth Geophysics;
- Volcanology;
- Hydrology and Hydrogeology;
- Geomorphology;
- Meteorology and Climatology;
- Oceanography and Coastal Dynamics;
- Glaciology and Cryosphere;
- Space physics.
This session also aims at exploring how ground-based monitoring networks can be utilised to: promote cross-network and -discipline engagement, develop and test new technologies and sensors, expand quality assurance methods and techniques, support modelling and satellite data products.
Contributions related to multi-platform network and sensor integration are welcome. ECS members are also encouraged to participate.

This session aims at presenting current and forthcoming novel uses of spaceborne hyperspectral imagery acquired over several scales, especially the EnMAP mission, for geosciences and environmental applications. EnMAP provides high quality spectral data at 30 m spatial resolution covering the visible, near- and shortwave infrared regions with nearly global coverage and some regional time-series achieved after 5 years in orbit. Abstracts are solicited toward the characterization and quantification of geo- and bio-physical surface properties related to but not limited to soil and soil health, soil pollution, plastics, critical metals and minerals detection, carbon content in soils, hazards, volcanology, snow and ice properties, as well as vegetation, marine and atmospheric studies.

Environmental and lithospheric processes are driven by complex interactions among geological, geophysical, hydrological, climatic, and anthropogenic factors acting across multiple spatial and temporal scales. Recent advances in Earth Observation technologies, sensor development, autonomous platforms, and computational methods are enabling unprecedented capabilities for monitoring, modeling, and forecasting natural processes and hazards.
This session welcomes contributions presenting innovative approaches for the observation, characterization, and modeling of environmental and lithospheric systems through the integration of satellite, airborne, UAV-borne, ground-based, and proximal sensing technologies. Particular emphasis is placed on multi-platform and multi-sensor strategies that combine Earth Observation data with geophysical, geodetic, environmental, and in situ measurements to improve process understanding and hazard assessment.
The session aims to highlight recent developments in advanced instrumentation, monitoring networks, real-time and near-real-time acquisition systems, and intelligent data processing workflows. Contributions addressing machine learning, deep learning, data fusion, data assimilation, anomaly detection, and automated interpretation of large and continuous datasets are especially encouraged. Special attention will be given to approaches supporting rapid decision-making, forecasting, and early-warning applications.
We particularly welcome studies focused on monitoring and modeling volcanic activity, active tectonics, seismic and crustal deformation, land subsidence, slope instability, floods, coastal dynamics, climate-related processes, energy exploitation, and other natural or anthropogenic hazards. Contributions employing advanced analytical and numerical approaches, including inverse modeling, multiphysics simulations, and finite element methods, are also encouraged.
The session brings together researchers from geophysics, geology, seismology, geodesy, remote sensing, environmental sciences, geomatics, engineering, artificial intelligence, and data science to foster interdisciplinary collaboration. The objective is to advance the next generation of integrated Earth Observation systems, intelligent monitoring frameworks, and predictive modeling approaches for environmental and lithospheric processes.

The exploitation of sustainable monitoring strategies can have a high impact in degraded and remote areas, in terms of safety and security, and improve people’s social and economic benefits. Urban regions, including informal settlements, poorly monitored districts, and degraded areas, are vulnerable to environmental hazards, climate change, infrastructure deterioration, and public health risks. Furthermore, remote areas in low-income countries lack the technology for monitoring, which is key for preventing and mitigating natural and anthropogenic risks and for crisis management.
This session aims to bring together researchers, practitioners, and policymakers to address gaps in observation data. New observational strategies and sensing techniques will be discussed, which provide a more accurate and inclusive reflection of these underrepresented areas to bridge these gaps.
We welcome contributions on:
• Advanced Remote Sensing Technology: optical, SAR, LiDAR, thermal and hyperspectral methods, including use of UAVs and drones, for a synoptic observation of the territory;
• Remote Sensing Applications for Natural Hazards: monitoring strategies and organisational solutions for natural hazards (e.g. earthquakes, volcanic activities or landslides, droughts, etc);
• Integrated Sensing Networks: low-cost in-situ sensors (e.g., GNSS, meteorological, Tiltmeter, dynamic gravity) as complements to satellite observations;
• Data Science and AI: data fusion and machine learning approaches to enhance monitoring reliability, resolution and coverage;
• Immersive Technologies for Monitoring and Engagement: application of AR/VR/MR to visualise geospatial data, simulate hazard scenarios, and support participatory planning in underserved urban areas.
• Citizen Science: participatory sensing initiatives that improve observational data in underserved communities;
• Sociotechnical Applications: sustainable observational strategies for monitoring and protecting critical infrastructures (water, energy, transport), and promoting sustainable resource use and social inclusion;
• Geoscience for Social Good: applications for urban climate resilience, public health, pollution exposure, urban heat islands, green infrastructure, and disaster risk reduction, with a focus on big cities and remote areas.
Our goal is to facilitate integrating diverse sensing techniques into operational frameworks that promote resilience, inclusivity, and sustainable development for marginalised populations.

The global energy transition is driving unprecedented demand for critical and strategic raw materials such as copper, lithium, nickel, cobalt, graphite and rare earth elements. In Europe, the Critical Raw Materials Act (CRMA) sets ambitious targets for domestic extraction and processing, and requires Member States to establish national exploration programmes, including geoscientific surveys, mineral mapping and geochemical campaigns. These programmes are generating a new wave of regional and national-scale geophysical datasets and renewing interest in the reinterpretation of legacy data. With most near-surface deposits in well-explored terranes already discovered, future discoveries will increasingly depend on our ability to image deeper, covered and geologically complex targets.
This session invites contributions on all aspects of geophysics applied to mineral exploration, from national to deposit scale. We welcome studies using potential field (gravity, magnetics), electromagnetic, magnetotelluric, induced polarisation, seismic, radiometric and borehole methods, as well as airborne, drone-based and passive-source approaches.
Contributions presenting results, strategies and lessons learned from national exploration programmes, as well as work on low-impact exploration, social acceptance, and secondary resources such as mine waste and tailings, are also welcome. The session aims to bring together academia, geological surveys and industry to discuss how geophysics can support a secure and sustainable supply of raw materials in Europe and worldwide.

Recent advances in geophysical observations, numerical simulations, remote sensing products, and high-performance computing are generating large and increasingly complex multidimensional datasets. Interpreting these datasets requires advanced processing and modelling together with effective methods for visualisation and integration.
This session focuses on the development and application of advanced visualisation methods for geophysical data. We particularly welcome approaches that improve the exploration and interpretation of multidimensional, multiscale, and time-dependent datasets, and that can improve geophysical interpretation and its practical application.
We welcome contributions addressing interactive visualisation, 3-D and 4-D representations, data fusion, and the integration of observational, experimental, and modelled data. Contributions combining geophysical data with artificial intelligence and machine learning, including AI-assisted visual analytics and interactive approaches for navigating large and heterogeneous datasets, are particularly encouraged.
The session will also consider practical applications of advanced visualisation across different operational and scientific scenarios. Examples include geophysical applications in engineering and infrastructure monitoring, environmental protection and assessment, natural hazard monitoring and early-warning systems, and the management and interpretation of large and heterogeneous datasets. We also welcome applications to planetary exploration, where observations acquired by spacecraft, landers, rovers, and orbital platforms need to be integrated with geological, geophysical, topographic, and numerical datasets to investigate planetary environments.
By bringing together developments across geophysics, engineering, environmental sciences, natural-hazard research, data science, and planetary exploration, the session will provide a forum to discuss how advanced visualisation can improve the interpretation of complex geophysical datasets and support more effective, transparent, and interactive approaches to understanding the Earth and planetary geophysics.

The Earth’s shallow and deep subsurface requires the integration of complementary investigation of geophysical methods capable of resolving structures and processes across different spatial and depth scales. This session will host contributions presenting geophysical approaches for the characterization of the surface and subsurface, with applications focused on geological and geotechnical characterization, ranging from the identification of underground cavities, the study of landslides and groundwater resources, to mineral exploration, saline intrusion phenomena, archaeological sites and tectonic structures. We encourage studies which combine geophysical methods, geological observations, remote sensing, and geochemical or degassing datasets, with particular emphasis on integrated workflows or multi-method interpretation, advanced processing and imaging techniques, and innovative approaches for better understand shallow and deeper geological targets. Contributions addressing methodological developments, field applications, monitoring, and case studies are particularly welcome.

The growth of urbanisation in areas exposed to multiple geological hazards, coupled with the rapid increase in extreme climate-related events, makes novel approaches to the geophysical monitoring of urban areas necessary. This scenario will present unprecedented challenges to the infrastructure and lifeline systems that are already overstressed and support urban centres. Programmes that promote the sustainability and resilience of cities and lifeline infrastructures require the development of methodologies for non-destructive or minimally invasive geophysical exploration and monitoring of surface and the subsurface. This session will present and discuss recent technological and methodological advances in geophysics, including multi-sensor, multi-resolution, and multi-scale approaches to the geophysical investigation of urban subsurface and strategic infrastructures. The focus will be on novel and effective geophysical methods, innovative sensors (e.g. fibre optics and MEMS) for dense and distributed network arrays, and AI-based algorithms and machine learning methods for processing and analysing geophysical data. Furthermore, we welcome and encourage presentations on case studies concerning the monitoring of urban areas and infrastructure, the developing of innovative systems for sharing and visualising digital data, and the use of digital twins of the urban subsurface. The session will also provide an opportunity for applied geophysicists, geologists, and engineers to share their expertise and discuss issues. Finally, the session will promote the activities of early career scientists in addressing open challenges in applied geophysics in programmes for the sustainability and resilience of tomorrow's cities.

Sustainability and resilience have become mainstream goals of political agendas globally, contrasting the causes of climate change and mitigating its effects, respectively. Built environment issues, infrastructure maintenance and rehabilitation, urbanisation and environmental impact are pushing for broader-scale goals, like climate change assessment and natural disaster prediction and management. In this context, Non-destructive testing (NDT) and Earth Observation (EO) methods lend themselves to be instrumental at developing new monitoring and maintenance approaches.
Despite the technological maturity reached by NDT and EO, important research gaps on standalone technologies and their integration are still unexplored. One challenging issue is the development of monitoring systems based on the integration of sensing technologies with advanced modelling, ICT and position/navigation topics up to IOT and the new concept of citizen engineer. The goal is to provide stakeholders with handy and user-friendly information to support maintenance and controlling major risks.
This Session primarily aims at disseminating contributions from state-of-the-art NDT and EO methods, promoting stand-alone technology and their integration for the development of new investigation/monitoring methods, applications, theoretical and numerical algorithms, and prototypes for sustainable and resilient infrastructure and built environments.
The followings are areas of interest and priority for this Session:
- Sensor types, systems and working modes (acoustic/electric/electromagnetic/nuclear/radiography/thermal/optical/vibration sensors; remote and ground-based, embedded sensing systems; stand-alone and integrated multi-source sensing modes);
- Advanced processing methods and information analysis techniques (multi-dimensional signal processing; image processing; data processing and information analysis; inversion approaches, AI);
- Multi-sensor, multi-temporal and multi-modal data fusion and integration (image fusion; spatio-temporal data fusion; AI and machine learning for data fusion and integration);
- ICT for spatial data infrastructure, distributed computing and decision support systems;
- Citizens as “sensors” for defect detection and data collection;
- New NDT applications and EO missions for downstream implementations;
- NDT and EO for new standards, policies and best practices;
- Case studies relevant to built environment diagnostics and monitoring.

Non-invasive geophysical prospection has become a fundamental component of archaeological research, providing high-resolution information on buried remains and landscape evolution while minimizing disturbance to cultural heritage. Advances in sensor technologies, UAVs, autonomous platforms, and computational methods are extending archaeological geophysics beyond site detection towards the quantitative characterization and three-dimensional reconstruction of subsurface features. This session welcomes contributions presenting innovative geophysical approaches for archaeological investigations based on single- and multi-method datasets acquired from ground-based, UAV-borne, and other mobile platforms. Particular emphasis is placed on the integration of magnetic, gravimetric, electromagnetic, ground-penetrating radar (GPR), electrical, seismic, and multi-sensor observations for the investigation and characterization of archaeological sites. The session aims to highlight recent developments in the detection, mapping, and quantitative interpretation of buried archaeological remains through advanced methodologies for data processing, imaging, inversion and joint inversion, data fusion, machine learning, artificial intelligence, and digital reconstruction. Special attention will be given to approaches enabling high-resolution 3D and 4D visualization of subsurface archaeological structures and landscapes, including the integration of geophysical datasets with excavation data, remote sensing products, photogrammetry, LiDAR, digital twins, and archaeological information systems.
A further focus concerns innovative acquisition strategies and instrumentation, including UAV-mounted sensors, autonomous and robotic platforms, mobile mapping approaches, dense-array surveys, and real-time processing workflows. Contributions addressing challenging environments such as urban areas, dense vegetation, rugged terrains, coastal and wetland settings, and protected heritage sites are particularly encouraged.

Archaeological research and the study, conservation and risk assessment of cultural heritage increasingly rely on non-invasive technologies operating across different spatial scales, depths and temporal dimensions. Recent advances in sensing technologies, acquisition platforms and data processing have transformed the way archaeological landscapes and sites, historic buildings, monuments and artefacts are discovered, investigated, documented and monitored.
The rapid development of Uncrewed Aerial Vehicles (UAVs) has filled the observational gap between satellite/airborne remote sensing and ground-based investigation, enabling very-high-resolution and multi-temporal acquisitions using RGB, multispectral, hyperspectral, thermal and LiDAR sensors. Advances in Earth Observation, SAR/InSAR, terrestrial and mobile LiDAR, close-range imaging, geophysical prospection and in situ diagnostics are providing complementary information on surface and subsurface features.
Their integration opens new perspectives for multi-scale, multi-temporal and multi-depth investigation, from the detection and interpretation of archaeological landscapes and buried remains to the characterization of monuments, structures, materials and artefacts. Emerging frontiers include autonomous sensing, multimodal data fusion, 3D/4D reconstruction and change detection, sensor networks, AI-assisted interpretation and digital twins, supporting a shift from individual observations towards integrated, dynamic and predictive approaches.
This session welcomes methodological advances and case studies involving satellite and UAV remote sensing; LiDAR; terrestrial and close-range sensing; archaeological and near-surface geophysics, including GPR, ERT, magnetic and electromagnetic methods; thermal, multispectral and hyperspectral imaging; non-invasive analytical techniques; sensor networks; multi-sensor data integration; AI and machine learning; and 3D/4D modelling and digital twins.
Particular attention will be given to approaches integrating observations from space, air, surface and subsurface to generate new archaeological knowledge, characterize heritage assets and their transformations, and support research, monitoring, conservation and risk management.

A wide range of geo-electromagnetic methods, including natural source magnetotelluric, time-domain, and frequency-domain controlled source EM, as well as DC resistivity and induced polarization are uniquely sensitive to the earth’s electrical properties and are capable of probing from shallow depths near the surface to even hundreds of kilometers into the Earth's crust. They are invaluable for revealing subsurface structures, fluid distributions, mineral resources, tectonic features, and even engineered infrastructure. Traditionally essential in resource exploration, geo-electromagnetic methods are now becoming increasingly relevant in addressing new global challenges related to energy systems, the impacts of climate change, environmental problems, and urban development and resilience.

This session serves as an annual platform for showcasing the latest advancements in geo-electromagnetic research. We encourage contributions from a broad range of topics, including methodological breakthroughs, novel field observations, theoretical advancements, and case studies. This year, we particularly welcome submissions that highlight innovative uses of geo-electromagnetic methods in emerging areas—whether through state-of-the-art instrumentation, unconventional applications, or studies with significant societal or environmental relevance.

Remote sensing measurements from ground, UAV, aircraft and satellite platforms have increasingly become established technologies to study and monitor Earth’s surface, to perform comprehensive analysis and modeling, with the final goal of supporting decision making. The spectral, spatial and temporal resolutions of remote sensors have been continuously improving, making environmental remote sensing more accurate and comprehensive than ever before. Such progress enables understanding of multiscale aspects of high-risk natural phenomena and development of multi-platform and inter-disciplinary surveillance monitoring tools. The session welcomes contributions focusing on present and future perspectives in environmental remote sensing, from multispectral/hyperspectral optical and thermal sensors. Applications are encouraged to cover, but not limited to, the monitoring and characterization of environmental changes and natural hazards from volcanic and seismic processes, landslides, and soil science. Specifically, we are looking for novel solutions and approaches including the topics as follows: ecosystem assessment and monitoring, land use/cover changes, coastal environments and climate change, techniques for data fusion (spectral, spatial and temporal), disaster monitoring, new sensors and platforms for environmental studies.

Cosmic rays carry information about space and solar activity, and, once near the Earth, they produce isotopes, influence genetic information, and are extraordinarily sensitive to water. Given the vast spectrum of interactions of cosmic rays with matter in different parts of the Earth and other planets, cosmic-ray research ranges from studies of the solar system to the history of the Earth, and from health and security issues to hydrology, agriculture, and climate change. Although research on cosmic-ray particles is connected to a variety of disciplines and applications, they all share similar questions and challenges regarding the physics of detection, modelling, and the influence of environmental factors.
The session brings together scientists from all fields related to monitoring and modelling cosmogenic radiation. It will allow the sharing of expertise amongst international researchers as well as showcase recent advancements in their field. The session aims to stimulate discussions about how individual disciplines can share their knowledge and benefit from each other.
We solicit contributions related but not limited to:
- Health, security, and radiation protection: cosmic-ray dosimetry on Earth and its dependence on environmental and atmospheric factors
- Planetary space science: satellite and ground-based neutron and gamma-ray sensors to detect water and soil constituents
- Neutron and Muon monitors: detection of high-energy cosmic-ray variations and its dependence on local, atmospheric, and magnetospheric factors
- Hydrology and climate change: low-energy neutron sensing to measure water in reservoirs at and near the land surface, such as soil, snowpack, and vegetation
- Cosmogenic nuclides: as tracers of atmospheric circulation and mixing; as a tool in archaeology or glaciology for dating of ice and measuring ablation rates; and as a tool for surface exposure dating and measuring rates of surficial geological processes
- Detector design: technological advancements in the detection of cosmic rays and cosmogenic particles
- Cosmic-ray modelling: advances in modelling of the cosmic-ray propagation through the magnetosphere and atmosphere, and their response to the Earth's surface
- Impact modelling: How can cosmic-ray monitoring support environmental models, weather and climate forecasting, agricultural and irrigation management, and the assessment of natural hazards

Monitoring high-latitude regions, active volcanic areas, high-altitude alpine zones, and extreme terrestrial analogues poses severe logistical and technological challenges. Severe thermal ranges, high electromagnetic interference, strict weight and power constraints, and remote accessibility demand highly resilient instrumentation and innovative operational workflows.
This session brings together researchers, engineers, and platform developers to explore cutting-edge technological advancements designed for environmental observation in extreme contexts. Primary focus is given to autonomous and unmanned platforms, such as tailored UAVs, USVs, and rovers, equipped with specialized geophysical and remote sensing payloads including thermal cameras, magnetometers, LiDAR, and ground-penetrating radar optimized for terrain-following and harsh operating conditions.
The session also addresses developments in ruggedized sensing hardware, low-power miniaturized electronics, and multi-platform data fusion methodologies that integrate satellite imagery, airborne surveys, and in-situ sensor networks. Furthermore, emphasis is placed on field-testing campaigns in terrestrial analogue environments that serve to benchmark instruments for future planetary exploration missions, as well as edge-computing and machine learning workflows for real-time data inversion and rapid anomaly detection in remote deployments. Submissions spanning sensor design, field calibration, campaign results, and integrated geophysical modeling are welcome.