Understanding where people live, how populations and visitors are distributed across space, and how these patterns shift over time is central to planning in an era of climate change, natural hazards, and mounting pressures on natural environments. This session focuses on data-driven approaches that connect advances in gridded population and socio-demographic datasets with the management of nature-based tourism and outdoor recreation across rural communities, destinations, and protected landscapes. Emphasis is placed on methodological progress in building, validating, and integrating spatial population and human-activity data—along with assessing spatial accuracy, uncertainty, and data fusion methods for future projections under alternative scenarios. The session also focuses on real-world applications that translate these data products into actionable planning and governance, including climate change adaptation, disaster risk management, sustainable land-use planning, and destination resilience. Key thematic areas include geoscience methods for tourism and recreation; the role of biodiversity, geodiversity, and ecosystem services; natural hazards and risk communication; strategic decision-making and stakeholder trust in data; participatory and citizen approaches; and the use of local knowledge to support sustainable development and mitigation. Overall, the session highlights how robust spatial evidence can support transparent, impactful decisions for communities and environments under uncertainty.

This session is dedicated to the award division ceremony and medal lecture of the Christiaan Huygens Medal (2026 edition), conferred by the Geosciences Instrumentation and Data Systems (GI) Division of the European Geosciences Union.
The Christiaan Huygens Medal was established by the GI Division to recognise outstanding and sustained contributions to the advancement of geoscientific instrumentation, observational methodologies, and data systems within the scope of the Division. The medal is awarded for a major innovation, development, or discovery that has had a significant and lasting impact on its field, or for a coherent body of work carried out over an extended period that has led to substantial scientific and technological progress.

The Division on Geosciences Instrumentation and Data Systems (GI) intends to be a forum for developments in instrumentation, technology, methods and data handling used in any field of the various geosciences. By promoting the discussion between specialists from widely diverse fields, advances in instrumentation made in one field might be utilised in other areas also and encourage co-operation, thereby saving separate development work and making new approaches possible, which otherwise might still have to wait for years or even decades.

As nearly every other field of geosciences is related to one or the other instrumentation strategy, many of the GI-sessions are co-organized with sessions from other divisions. Potential contributors to any session are encouraged to evaluate the benefits of a multi-disciplinary discussion versus the specific interest of the own target group.
in this context, the GI Division Meeting is the annual appointment where the geoscience community gathers to discuss the work carried out and future perspectives.

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.

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.

Ground-based networks for monitoring of atmospheric chemical composition and meteorology improve our understanding of local, regional, and continental scale atmospheric events and long-term trends, and inform decisions critical to air quality, climate change, weather forecasting, and human health. Monitoring networks serve an important role within the research community, providing a backbone of data to support modeling, satellite data product validation, and short-term measurement campaigns. Ongoing collaboration, communication, and promotion of monitoring network developments and data products is necessary in order to fully leverage benefits from such networks. This session explores how ground-based atmospheric monitoring networks can be utilized to:
- promote cross-network and -discipline engagement
- develop and test new technologies and sensors
- expand quality assurance methods and techniques
- support modeling and satellite data products

Reliability in water research depends on two key aspects: the availability of robust observational data and the rigorous selection and validation of model frameworks. This session highlights the importance of data acquisition, quality control, and curation in supporting reliable methodologies across hydraulic and hydrologic engineering.
In hydraulics, flume experiments provide controlled, high-quality datasets but are resource-intensive and limited in scalability. Numerical modeling offers greater flexibility to simulate diverse flow conditions, yet its accuracy is highly sensitive to parameterization, boundary conditions, and discretization schemes. In hydrology, sparse and uncertain field data further complicate model calibration and validation.
Recent advances in artificial intelligence (AI) and machine learning (ML) allow researchers to analyze large and heterogeneous datasets. However, risks arise when dataset adequacy, representativeness, or validation are overlooked, leading to ambiguous outcomes. These issues intensify when experimental, numerical, and AI-driven approaches are not cross-validated or integrated, weakening robustness and transferability.
This session aims to strengthen understanding of data curation and model selection as critical, though often overlooked, components in solving water resource challenges. Topics of interest include:
1. Strategies for data acquisition, handling, and curation across laboratory, field, numerical, and AI/ML approaches.
2. Best practices in optimization, calibration, and hyper-parameterization to improve model performance.
3. Frameworks for integrating laboratory, field, and computational datasets for consistency and cross-validation.
4. Data curation methods that enhance efficiency, reproducibility, and reliability in modeling.
Through interdisciplinary dialogue, the session seeks to generate methodological insights and practical guidelines that enhance accuracy in data handling and model selection. The overarching goal is to advance high-quality, validated, and context-relevant outcomes that strengthen resilience and reliability in water research.

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 and 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.

By combining long monitoring data (> halftime of Cesium 137 after the Chernobyl Accident in 1986, 15 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, as part of the decommissioning of the Fukushima Daiichi Nuclear Power Station, the discharge of ALPS-treated water is being carried out, which has attracted international attention. The discharge rate is published in real time and monitoring is being conducted, providing a valuable opportunity for analyzing the behavior of radionuclides in the ocean. In addition, past nuclear contamination events and other data sets 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).

Modern challenges in climate risk management, disaster response, public health, resource management, and logistics demand robust spatiotemporal analysis of increasingly complex geospatial datasets. Recent studies, however, highlight significant challenges when applying ML and AI to spatial and spatio-temporal data along the entire modelling pipeline, including reliable accuracy assessment, model interpretation, transferability, and uncertainty assessment. This gap has been recognised and led to the development of new spatiotemporally aware strategies and methods in response to the promise of improving spatio-temporal predictions, the treatment of the cascade of uncertainties, decision making and facilitating communication.

This session focuses on the strategic integration and application of artificial intelligence (AI) and machine learning (ML) to address these challenges. We welcome contributions that explore novel methods, software tools, and infrastructures designed to improve spatiotemporal predictions, manage cascading uncertainties, and support decision-making. Emphasis will be placed on interpretability, transferability, and reliability across the modelling pipeline, as well as on the communication of results to diverse stakeholders. Case studies, theoretical advances, and cross-disciplinary approaches are encouraged.

frequent and impactful weather-related disasters. Conversely, declines in water availability make monitoring surface water dynamics, including seasonal water body variations, wetland extent, and river morphology changes crucial for environmental management, climate change assessment, and sustainable development. Remote sensing is a critical tool for data collection and observation, especially in regions where field surveys and gauging stations are limited, such as remote or conflict ridden areas and data-poor developing nations. The integration of remotely-sensed variables—like digital elevation models, river width, water extent, water level, flow velocities, and land cover—into hydraulic models offers the potential to significantly enhance our understanding of hydrological processes and improve predictive capabilities.
Research has so far focused on optimising the use of satellite observations, supported by both government and commercial initiatives, and numerous datasets from airborne sensors, including aircraft and drones. Recent advancements in Earth observation (EO) and machine learning have further enhanced the monitoring of floods and inland water dynamics, utilising multi-sensor EO data to detect surface water, even in densely vegetated regions. However, despite these advancements, the update frequency and timeliness of most remote sensing data products are still limited for capturing dynamic hydrological processes, which hinders their use in forecasting and data assimilation. This session invites cutting-edge presentations on advancing surface water and flood monitoring and mapping through remotely-sensed data, focusing on:
- Remote sensing for surface water and flood dynamics, flood hazard and risk mapping including commercial satellite missions and airborne sensors
- The use of remotely-sensed data for calibrating or validating hydrological or hydraulic models
- Data assimilation of remotely-sensed data into hydrological and hydraulic models
- Enhancements in river discretization and monitoring through Earth observations
- Surface water and river flow estimation using remote sensing
- Machine learning and deep learning-based water body mapping and flood predictions
- Ideas for developing multi-satellite data products and services to improve the monitoring of surface water dynamics including floods
Early career and underrepresented scientists are particularly encouraged to participate.

In recent years, the field of geostatistics has seen significant advancements. These methods are fundamental in understanding spatially and temporally variable hydrological and environmental processes, which are vital for risk assessment, input for other models, and management of extreme events like floods and droughts.
This session aims to provide a comprehensive platform for researchers to present and discuss innovative applications and methodologies of geostatistics and spatio-temporal analysis in hydrology and related fields. The focus will be on traditional approaches and the assessment of uncertainties, whereas Machine Learning approaches have their specific and other dedicated sessions.

We invite contributions that address the following topics (but not limited to):

1. Spatio-temporal Analysis of Hydrological and Environmental Anomalies:
- Methods for detecting and analyzing large-scale anomalies in hydrological and environmental data.
- Techniques to manage and predict extreme events based on spatio-temporal patterns.

2. Innovative Geostatistical Applications:
- Advances in spatial and spatio-temporal modeling.
- Applications in spatial reasoning and data mining.
- Reduced computational complexity methods suitable for large-scale problems.

3. Geostatistical Methods for Hydrological Extremes:
- Techniques for analyzing the dynamics of natural events, such as floods, droughts, and morphological changes.
- Utilization of copulas and other statistical tools to identify spatio-temporal relationships.

4. Optimization and Generalization of Spatial Models:
- Approaches to optimize monitoring networks and spatial models.
- Techniques for predicting regions with limited or unobserved data e.g., using physical-based model simulations or using secondary variables.

5. Uncertainty Assessment in Geostatistics:
- Methods for characterizing and managing uncertainties in spatial data.
- Applications of Bayesian Geostatistical Analysis and Generalized Extreme Value Distributions.

6. Spatial and Spatio-temporal Covariance Analysis:
- Exploring links between hydrological variables and extremes through covariance analysis.
- Applications of Gaussian and non-Gaussian models in spatial analysis and prediction.

We are experiencing a revolution in earth and environmental data. Satellites, genetic sequencing, long-term in situ sensors, model results and reanalyses, digitized collections, social media, and citizen science are producing massive datasets, requiring students in earth and environmental sciences to learn scientific computing skills to use them. However, many educational institutions are not meeting this need -- most students in these degree programs report only learning these essential skills informally from peers and mentors if they learn them at all. In this session, we invite researchers and educators with innovative solutions to this gap in earth and environmental science education to share their successful programs, courses, and interventions. We are particularly interested in highlighting initiatives with a proven track record of targeting and including intersectional identities traditionally under-represented among earth, environmental and/or computer scientists.

Motivation

Although in some communities (e.g., meteorology, climate science) the tradition of software writing has a long history, most scientists are not trained software engineers. For early-stage scientific software projects, which are typically developed within small research groups, there is often little expectation that the code will (1) be used by a larger community, (2) be further developed or extended by others, or (3) be integrated into larger projects. This can lead to an “organic” evolution of code bases that result in challenges related to documentation, maintainability, usability, reusability, and the overall quality of the software and its results.

The wider availability of large computing resources in recent decades, along with the emergence of large datasets and increasingly complex numerical models, has made it more important than ever for scientific software to be well-designed, documented, and maintainable. However, (1) established practices in scientific programming, (2) pressures to produce high-quality results efficiently, and (3) rapidly growing user and developer communities, can make it challenging for scientific software projects to

- follow a common set of standards and a style,
- are fully documented,
- are user-friendly, and
- can be maintained, easily extended or reused.

Session content and objectives

We invite developers or users of software projects to prepare presentations about the challenges and successes in the following topics

- Good practices for developing scientific software
- Modularization
- Documentation
- Linting
- Version control
- Open source and open development
- Automatization of quality checks and unit testing
- Planning new projects
- User requirements and the user-turned-developer problem
- Painless and energy-efficient programming solutions across computing architectures
- Modularization and reliability vs performance and multiplatform capacity
- Large-dataset compression and storage workflows

These presentations will show how different projects across geoscientific fields tackle these problems. We can discuss new strategies for bettering scientific software development and raising awareness within the scientific community that robust and well-structured software development enables meaningful and reproducible results, supports researchers —especially doctoral and post-doctoral students— in their work, and accelerates advances in data- and modelling-driven science.

Data imperfection is a persistent and multi-faceted challenge in hydrology and more broadly in geosciences. Researchers and practitioners regularly work with datasets that are incomplete, imprecise, erroneous, heterogeneous, or redundant—whether originating from in-situ measurements, remote sensing, modelling outputs, or participatory sources.
While traditional statistical methods have long been used to address these limitations, the growing complexity and diversity of hydrological and environmental data have created new demands—and opportunities—for innovation. Advances in artificial intelligence, data fusion, knowledge representation, and reasoning under uncertainty now allow for more robust integration and interpretation of heterogeneous information.
This session aims to gather contributions that explore how we can move from imperfect, fragmented data toward coherent and actionable hydrological and environmental knowledge. We welcome abstracts on:

• Applications and case studies in hydrology or other domains, addressing missing data imputation, model inversion, uncertainty propagation, or multi-source integration—using time series, spatial data, imagery, videos, etc. The case studies may focus on hydrological and natural hazards (floods, droughts, earthquakes, landslides, marine submersion, etc..) or resources management (water supply, treatment, etc…)
• Methodological developments in data fusion, completion, uncertainty quantification, and AI-based knowledge extraction from heterogeneous data.
• Cross-disciplinary approaches that connect geosciences, and specifically hydrological sciences, with AI, data mining, and knowledge systems, including citizen science, crowd-sourced data, or opportunistic sensing.
• Experimental contributions in hydrology and geosciences relying on AI, such as novel models and algorithms, explainable methods, and comparative studies on domain-specific datasets.
• Feedback from data integration initiatives into domain specific or cross disciplinary repositories.

We particularly encourage contributions that highlight novel practices or conceptual frameworks for dealing with imperfect and multi-source data in complex environmental systems.

Earth System Sciences (ESS) datasets, particularly those generated by high-resolution numerical models, are continuing to increase in terms of resolution and size. These datasets are essential for advancing ESS, supporting critical activities such as climate change policymaking, weather forecasting in the face of increasingly frequent natural disasters, and modern applications like machine learning.

The storage, usability, transfer and shareability of such datasets have become a pressing concern within the scientific community. State-of-the-art applications now produce outputs so large that even the most advanced data centres and infrastructures struggle not only to store them but also to ensure their usability and processability, including by downstream machine learning. Ongoing and upcoming community initiatives, such as digital twins and the 7th Phase of the Coupled Model Intercomparison Project (CMIP7), are already pushing infrastructures to their limits. With future investment in hardware likely to remain constrained, a critical and viable way forward is to explore (lossy) data compression & reduction that balance efficiency with the needs of diverse stakeholders. Therefore, the interest in compression has grown as a means to 1) make the data volumes more manageable, 2) reduce transfer times and computational costs, while 3) preserving the quality required for downstream scientific analyses.

Nevertheless, many ESS researchers remain cautious about lossy compression, concerned that critical information or features may be lost for specific downstream applications. Identifying these use-case-specific requirements and ensuring they are preserved during compression are essential steps toward building trust so that compression can become widely adopted across the community.

This short course is designed as a practical introduction to compressing ESS datasets using various compression frameworks and to share tips on preserving important data properties throughout the compression process. After completing the hands-on exercises, using either your own or provided data, time will be set aside for debate and discussion to address questions about lossy compression and to exchange wishes and concerns regarding this family of methods. A short document summarising the discussion will be produced and made freely available afterwards.

To learn more about recent advances in data compression, please also join the ESSI2.2 oral and poster sessions.

During the past 75 years, radiocarbon dating has been applied across a wide range of disciplines, including, e.g. archaeology, geology, hydrology, geophysics, atmospheric science, oceanography, and paleoclimatology, to name but a few. Radiocarbon analysis is extensively used in environmental research as a chronometer (geochronology) or as a tracer for carbon sources and natural pathways. In the last two decades, advances in accelerator mass spectrometry (AMS) have enabled the analysis of very small quantities, as small as tens of micrograms of carbon. This has opened new possibilities, such as dating specific compounds (biomarkers) in sediments and soils. Other innovative applications include distinguishing between old (fossil) and natural (biogenic) carbon or detecting illegal trafficking of wildlife products such as ivory, tortoiseshells, and fur skins. Despite the wide range of applications, archives, and systems studied with the help of radiocarbon dating, the method has a standard workflow, starting from sampling through the preparation and analysis, arriving at the final data that require potential reservoir corrections and calibration.

This short course will provide an overview of radiocarbon dating, highlighting the state-of-the-art methods and their potential in environmental research, particularly in paleoclimatology. After a brief introduction to the method, participants will delve into practical examples of its application in the study of past climates, focusing on the 14C method and how we arrive at the radiocarbon age.
Applications in paleoclimate research and other environmental fields
Sampling and preparation
Calibration programs
We strongly encourage discussions around radiocarbon research and will actively address problems related to sampling and calibration. This collaborative approach will enhance the understanding and application of radiocarbon dating in the respective fields.

Deciphering the formation and evolution of planetary bodies requires a comprehensive investigation of both their surfaces and internal structures. Seismic data provide the most direct constraints on interiors, but such measurements remain scarce across the Solar System. In their absence, gravity and magnetic field observations have become fundamental for inferring the structure and dynamics of interiors of planetary bodies, spanning the Earth, Moon, and terrestrial planets to giant planets, their moons, and small bodies.
The scientific return of these geophysical datasets is greatly enhanced when combined with complementary surface observations, laboratory experiments, and numerical modeling. Multi-spectral and hyperspectral imaging, together with experimental analyses, link remote sensing data to mineralogical and physical properties, offering insights into the composition of outer and internal layers. Altimetry measurements provide independent constraints on tidal responses and rotational dynamics, complementing gravity and magnetic data and offering key insights into the rheology and differentiation of the deep interior. Joint analyses of gravity and topography provide information regarding the thickness, density, and elastic properties of interior layers, while thermochemical evolution models connect present-day structures through space and time to long-term geophysical and geological processes. Together, these approaches provide a more integrated understanding of how planetary bodies formed, differentiated, and evolved.
This session will focus on the instruments, measurement techniques, modeling approaches, and laboratory studies that enable robust constraints on the evolution of surfaces and interiors of planetary bodies. Contributions are invited that address both achievements and limitations of current methods, as well as innovative strategies to overcome existing challenges or combine disparate methodologies. Results from past, ongoing, and forthcoming missions, integrative analyses across multiple datasets, and forward-looking exploration concepts are all welcome. By bringing together diverse perspectives, the session aims to provide a broad and technically rigorous overview of the methods by which we can infer the processes shaping planetary bodies and to outline pathways for major discoveries in the coming decades.

The session Multidisciplinary Planetary Studies and Exploration Network promotes an international and cross-disciplinary dialogue on the new frontiers of planetary sciences. We welcome contributions addressing:
a) multiscale investigations of soils and subsurfaces aimed at identifying water, ice, mineral resources, and potential natural shelters, as well as studying surface and deep dynamic processes and the landscape evolution of terrestrial planets;
b) atmospheric analyses focused on assessing environmental conditions compatible with human or microbial life;
c) astrobiological studies of extremophiles and plants in extreme environments and in simulated planetary conditions;
d) examples of innovative instrumentation and research infrastructures for the analysis of soils, atmospheres, and biological systems in planetary or analogue contexts.
The overall goal is to advance our understanding of planets as integrated and living systems, in which geological, atmospheric, chemical, and potential biological processes interact within a unified picture, and to foster dialogue across disciplines. Contributions from Early Career Scientists, as well as results from experimental campaigns carried out at terrestrial analogue sites, are particularly encouraged.

This session aims to provide a comprehensive platform for discussing the latest advancements in lunar science, exploration, and sustainable utilization.
We will cover critical aspects of lunar science, including the deep interior, subsurface structure, surface morphology, up to atmospheric dynamics and the solar wind interaction. Such studies can make use of lunar mission data, lunar samples, meteorites, terrestrial analogues, laboratory experiments, and / or modeling efforts.
Furthermore, highlighting results from past and current space missions, this session seeks to explore innovative ideas for future exploration, including insights on forthcoming space missions and instrumentation aiming to greatly advance our understanding of the Moon in the next decades. In addition, the session will focus on identifying strategic knowledge gaps crucial for the safe and sustainable exploration of cis-lunar space and the lunar surface by astronauts.
We welcome all relevant contributions — spanning theoretical models, observational data, and experimental findings — from experts of different fields including science and engineering. As such, the session aims to foster a comprehensive dialogue on the status and future of lunar exploration.

It has become more than evident by now that the increasing complexity and resource intensiveness of performing state-of-the-art Earth System Science (ESS), be it from a modeling or a pure data collection and analysis perspective, requires tools and methods to orchestrate, record and reproduce the technical and scientific process. To this end, workflows are the fundamental tool for scaling, recording, and reproducing both Earth System Model (ESM) simulations and large-volume data handling and analyses.

With the increase in the complexity of computational systems and data handling tasks, such as heterogeneous compute environments, federated access requirements, and sometimes even restrictive policies for data movement, there is a necessity to develop advanced orchestration capabilities to automate the execution of workflows. Moreover, the community is confronted with the challenge of enabling the reproducibility of these workflows to ensure the reproducibility of the scientific output in a FAIR (Findable, Accessible, Interoperable, and Reusable) manner. The aim is to improve data management practices in a data-intensive world.

This session will explore the latest advances in workflow management systems, concepts, and techniques linked to high-performance computing (HPC), data processing and analytics, the use of federated infrastructures and artificial intelligence (AI) application handling in ESS. We will discuss how workflows can manage otherwise unmanageable data volumes and complexities based on concrete use cases of major European and international initiatives pushing the boundaries of what is technically possible and contributing to research and development of workflow methods (such as Destination Earth, DT-GEO, EDITO and others).

On these topics, we invite contributions from researchers as well as data and computational experts presenting current scientific workflow approaches developed, offered and applied to enable and perform cutting edge research in ESS.

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.

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.

Thermal remote sensing is an increasingly popular technique employing passive sensors to detect Earth’s surface properties from the emitted radiation in the Thermal Infrared (TIR) domain. The main focus of TIR remote sensing is the evaluation of the thermal state of an object or surface, and its associated surface temperature and emissivity. These properties are widely relevant in several frameworks for geological, environmental, climate, agricultural, biological, and engineering purposes.

Recent technological advancements have supported the development of the TIR remote sensing, as satellite sensor and data infrastructure systems are now able to collect and manage a large amount of high-fidelity TIR data with different spatial and temporal resolutions. Further, beside the airborne- and ground-based measurement systems, the Unmanned Aerial Systems (UAS) and drones are increasingly considered as versatile platforms concerning the temporal resolution ensuring high spatial resolution.

This session aims to deal with the main emerged and still emerging research directions of TIR remote sensing, as well as discussing the next challenges for this community. Examples of welcome contributions are the 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 the Early Career Scientists are welcome.

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, modeling, and the influence of environmental factors.

The session brings together scientists from all fields of research that are related to monitoring and modeling of 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 modeling: advances in modeling of the cosmic-ray propagation through the magnetosphere and atmosphere, and their response to the Earth's surface
- Impact modeling: How can cosmic-ray monitoring support environmental models, weather and climate forecasting, agricultural and irrigation management, and the assessment of natural hazards

Fibre optic based techniques allow probing highly precise point and distributed sensing of the full ground motion wave-field including translation, rotation and strain, as well as environmental parameters such as temperature at a scale and to an extent previously unattainable with conventional geophysical sensors. Considerable improvements in optical and atom interferometry enable new concepts for inertial rotation, translational displacement and acceleration sensing. Laser reflectometry on commercial fibre optic cables allows for the first time spatially dense and temporally continuous sensing of the ocean’s floor, successfully detecting a variety of signals including microseism, local and teleseismic earthquakes, volcanic events, ocean dynamics, etc. Significant breakthrough in the use of fibre optic sensing techniques came from the new ability to interrogate telecommunication cables to high temporal and spatial precision across a wide range of environments. Applications based on this new type of data are numerous, including: seismic source and wave-field characterisation with single point observations in harsh environments such as active volcanoes and the seafloor, seismic ambient noise interferometry, earthquake and tsunami early warning, and infrastructure stability monitoring.

We welcome contributions on developments in instrumental and theoretical advances, applications and processing with fibre optic point and/or distributed multi-sensing techniques, light polarization and transmission analyses, using standard telecommunication and/or engineered fibre cables. We seek studies on theoretical, instrumental, observation and advanced processing across all solid earth fields, including seismology, volcanology, glaciology, geodesy, geophysics, natural hazards, oceanography, urban environment, geothermal applications, laboratory studies, large-scale field tests, planetary exploration, gravitational wave detection, fundamental physics. We encourage contributions on data analysis techniques, novel applications, machine learning, data management, instrumental performance and comparison as well as new experimental, field, laboratory, modelling studies in fibre optic sensing studies.

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.

he Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) satellite, launched in May 2024, is an ESA-JAXA mission, designed to improve our understanding of clouds, aerosols and their role in modifying radiant energy fluxes. To achieve its objectives, EarthCARE employs a suite of coincident active and passive sensors to provide an unprecedented view of the three-dimensional structure of clouds, precipitation and aerosols along with collocated observations of solar and terrestrial radiation.
 
EarthCARE, provides co-registered observations from a suite of four unique instruments located on a common platform: (1) ATmospheric LIDar (ATLID), (2) Cloud Profiling Radar (CPR), (3) Multi- Spectral Imager (MSI) and (4) BroadBand Radiometer (BBR). EarthCARE global observations include vertical profiles of natural and anthropogenic aerosols, the vertical contribution of ice and liquid water content, the cloud mesoscale distribution, precipitation microphysics, estimates of particle size, convective vertical air motions, as well as atmospheric radiative heating and cooling profiles. In addition to providing novel measurements for a better understanding of processes shaping Earth’s weather and climate, EarthCARE continues the heritage measurements of CloudSat, CALIPSO, Aeolus and CERES.
 
This session invites contributions on EarthCARE science themes related to the exploitation of mission data. These include instrument characterization, new active and passive retrieval techniques; cloud and precipitation microphysics, process studies related to the effects of clouds, aerosol, and aersol-cloud interactions on Earth’s radiant energy budget; as well as synthesis with other methodological approaches including ground-based, air- or ship-borne field campaigns and modelling studies. A special focus will be on the synergy with modeling activities exploiting the next generation of km-scale climate models, as in ECOMIP and within the global km-scale hackathon, and observational studies in combination with Organized Convection and EarthCARE Studies over
the Tropical Atlantic (ORCESTRA).

This session concentrates on extreme rainfall events, surface water dynamics, and flood events, exploring innovative remote sensing, AI, and digital twin technologies for real-time monitoring, risk assessment, and mitigation. It invites submissions on advanced data integration, modeling approaches, early warning systems, and decision-support tools to improve understanding, forecasting, and management of flooding and related surface water hazards.

The integration of AI with digital twin improves the analytical and operational capabilities of geospatial systems, which through the analysis of historical data and the integration of real-time information (IoT) are able to highlight even “hidden patterns” in the data, identifying new models capable of improving forecasts with greater control over the quantification of uncertainty and the variability of the phenomenon analysed.

This session aims to focus on flood hazard and risk assessment, monitoring, and management. This Topic invites the submission of articles focused on, but not limited to, the following areas:
• Monitoring of extreme rainfall events and flood hazards for risk assessment and communication.
• Digital twins (DTs)/prototypes of DTs in flood hazard forecasting, early warning, monitoring, and supporting tools for urban governance.
• DSSs to extract meaningful information in the artificial intelligence era, eventually serving to reduce risk and provide support tools to mitigate flood hazards.
• The role of AI and digital twins to assess the economic impacts of flood hazards and the cost-effectiveness of various mitigation strategies.
• Novel techniques to analyse big data coming from Earth observation platforms, drones, and other geospatial data in order to provide timely information related to the extend, exposure, and impacts of flood hazards.

The rapid increase in extreme natural events related to climate change makes novel approaches to the geophysical monitoring of urban areas, industrial sites, and civil infrastructures, which are crucial elements of the modern society, essential in the context of environmental and energy transitions. Strategic programmes for the sustainability and resilience of cities, the environmental monitoring of industrial sites (e.g. power plants, old mines, CO2 storage fields), and the surveillance of civil infrastructures (e.g. bridges, dams, lifelines) are driving the development of methodologies for non-destructive and not, or minimally, invasive surface and subsurface geophysical exploration and monitoring methodologies. The session aims at presenting and discussing recent technological and methodological advances in geophysics, including multi-sensor, multi-resolution, and multi-scale approaches for geophysical investigations. Particular attention will be given towards novel and effective seismic and electromagnetic methods, innovative sensors (e.g. fibre optics, MEMS) for dense and distributed geophysical network arrays, the use of AI-based algorithms and machine learning methods for processing and analysing geophysical data and monitoring approaches based on augmented vision strategies. Furthermore, presentations of interesting case studies are welcome and encouraged. The session will also provide an opportunity for applied geophysicists, geologists, and engineers to share expertise and discuss issues. Finally, the session will promote the activities of Early Career Scientists (ECS) in addressing open challenges in applied geophysics in the context of twin transitions (climate and environment).

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.

Sediment transport in geophysical flows spans diverse environments including mountainous regions, rivers, estuaries, coasts, deserts, and engineered settings on Earth, and also shapes planetary surfaces such as Mars, Titan, and Venus. Understanding how sediments move remains a central challenge in hydrological, geomorphological, and planetary sciences. These processes operate across multiple spatial and temporal scales—from the movement of individual particles to landscape evolution—directly affecting geomorphology as well as ecological and biological functions in terrestrial environments, and influencing the structural resilience of built infrastructure.

Critical feedbacks between fluid motion, sediment dynamics, and particle interactions—such as size sorting—drive surface process variability, with implications ranging from hydraulic engineering and hazard risk management to predicting landscape and ecosystem responses.

A) Particle-Scale Interactions and Transport Mechanics:
-Entrainment mechanics in both fluvial and aeolian flows
-Turbulent energy and momentum transfer to particles
-Statistical approaches to upscaling stochastic sediment movement
-Dynamics of granular flow in dry and submerged scenarios
-Effects of grain morphology on sediment and granular transport
-Interactions among mixed-size sediment grains and segregation processes
-Discrete element modeling and upscaling into continuum frameworks

B) Reach-Scale Fluvial and Geomorphic Dynamics:
-Relationships among flow hydraulics, sediment transport, bedform development, and stratigraphy
-Equation development and solution for multiphase flows in rivers and air
-Shallow-water hydro-sediment-morphodynamic modelling
-Characterizing complex, unsteady flows including flash floods and granular mass movements
-Extreme event impacts: flood waves, debris flows, landslides

C) Engineering Applications and Earthcasting hazards:
-Dam failure processes (natural and engineered) and cascading hazards
-Coastal sediment transport (long-shore, cross-shore) and shoreline evolution
-Reservoir management and sediment process interactions
-Hydraulic structure design (e.g., fish passes, spillways) with consideration of sediment impacts
-Maintenance and management of waterways: dredging, regulation in large river systems
-Calibration and validation methodologies for Earth's surface hazards forecasts.

Imaging the Earth’s surface and reconstructing its topography to study the landscape and (sub-)surface processes has advanced rapidly over the past two decades, sometimes separately within different geoscience disciplines. New generations of satellites, Uncrewed Aerial Vehicles (UAVs), LiDAR systems, Structure-from-Motion (SfM) methods, ground-based systems, and deep learning approaches have made 2D, 3D, and 4D (time series) data acquisition easier, cheaper, and more precise. The spatial, temporal, and spectral resolutions of the measurements cover wide ranges of scales, offering the opportunity to study the evolution of the ground surface from local to regional scale with unprecedented detail. Equipped with optimized workflows ranging from digitizing analogue data – such as historical aerial photographs – to processing near-continuous records of topographic change, geoscientists now have a variety of tools to better understand our rapidly changing environments and disentangle anthropogenic from natural drivers.

However, challenges still exist at both methodological and application levels. How to properly acquire images and 3D data in harsh, remote or non-ideal environments? How to process unknown, damaged and/or poorly overlapping digitized analogue photographs? How to assess measurement precision and incorporate this uncertainty in the results and interpretation? How to model complex camera distortions and/or the resulting systematic error? How to deal with large, heterogeneous time series and multi-modal data sets? These questions exemplify situations commonly faced by geoscientists.

In the present session, we invite contributions from a broad range of geoscience disciplines (geomorphology, glaciology, volcanology, hydrology, soil sciences, etc.) to share perspectives about the opportunities, limitations, and challenges that modern 2-4D surface imaging offers across diverse processes and environments. Contributions can cover any aspect of surface imaging and mapping, from new methods, tools, and processing workflows to precision assessments, time series constructions, and specific applications in geosciences. We especially welcome contributions that cover 1) novel data acquisition and processing approaches (including image matching, camera distortion correction, complex signal/image and point cloud processing, and time series construction), 2) data acquisition in complex and fast-changing environments, and 3) innovative applications in geosciences.

Soil and vadose zone processes, including water, energy, and solute transport, occur over a wide range of spatial and temporal scales, from pores to watersheds. A key challenge in vadose zone hydrology is understanding how small-scale processes control and constrain large-scale system responses. Environmental variability and human activities shape soils’ physical, chemical, mechanical, and hydraulic properties, from saturated wetlands and coastal zones to arid and semi-arid landscapes.
This session focuses on the measurement and modeling of soil properties and processes across landscapes, from the pore scale to the field or watershed scale. Organized in collaboration with the International Soil Modeling Consortium (ISMC), the session invites contributions that:
• Measure soil physical and chemical properties in the lab, field, or watershed using tools such as micro-scale imaging, in-situ soil sensors, drones, geophysical methods, radars, and remote sensing platforms.
• Model soil processes using analytical, empirical, statistical, or numerical approaches that link processes across scales, including upscaling and downscaling strategies to address heterogeneity in infiltration, evaporation, salinity dynamics, gas transport, and subsurface mass and energy fluxes.
• Investigate spatiotemporal changes in vadose zone properties at different scales through measurement or modeling campaigns, focusing on natural variability or human-driven changes such as climate variability, sea level rise and salinity intrusion, droughts, freeze-thaw cycles, heavy agricultural machinery impacts, and land management practices in forests, agricultural fields, wetlands, coastal zones, grasslands, deserts, urban soils, and mountainous regions.

Radar is a prominent tool for studying ice on Earth and is becoming widespread on other planetary bodies. In this session, we hope to bring together all those interested in radar data and analysis to showcase their work, take inspiration from each other and develop new (interdisciplinary) collaborations. We aim for this session to encompass various targets, instruments and applications, such as:

- Targets: snow, firn, land ice, sea ice, lake ice, river ice and permafrost on Earth as well as the surfaces and interiors of Mars, Europa, Ganymede, The Moon, Titan, Venus, Small bodies, etc.
- Instruments: airborne and spaceborne sounders, altimeters, SAR and passive microwave radiometers as well as drones, GPR, ApRES, pRES and other radars.
- Acquisition and processing: hardware, passive measurements, datasets, algorithm development, etc.
- Analysis and interpretation techniques: reflectometry, interferometry, thermometry, specularity, EM simulations, inversion, etc.
- Applications: investigations in surface-, englacial, subglacial and proglacial areas, scattering interfaces, roughness, hydrology, geothermal heat flux, material properties, fabric, modelling/supporting lab work, Earth and extraterrestrial analogues/synergies, etc.

We especially encourage the participation of Early Career Researchers and those from underrepresented groups.

Observing soil moisture at the ground is essential to assess plant available water, manage water resources and calibrate, validate satellite products and conduct climate impact studies. Unfortunately, the availability of in situ observations is very limited in space and time. Whereas the spatial distribution is biased towards the global North, the temporal availability of soil moisture time series is on average 10 years as can be seen from the largest archive of in situ soil moisture, the International Soil Moisture Network (ISMN). Apart of the data availability issues, a substantial amount of the in situ observations face data quality issues that might result from sensor deployment, sensor calibration, data processing or other error sources.
This session will address issues in the development and deployment of state-of-the-art soil moisture observation networks, the financing of their long-term operation, data quality assurance, data imputation, and data scaling as well as sensor deployment and assessments of differences between these deployments. We further encourage contributions presenting developments of novel measurement techniques including citizen science initiatives and studies utilizing (primarily) in situ soil moisture to understand and assess hydrological processes, water availability, land-atmosphere feedbacks and soil moisture dependent hazards.

This session covers all methods and case histories related to measuring, processing and modeling potential field anomalies for geological, environmental and resources purposes. It will concern gravity and magnetic data from satellite missions to airborne and detailed ground-based arrays. Contributions presenting the theoretical, mathematical and computational progress of data modelling techniques as well as new case studies of geophysical and geological interest are welcome. This session will also encourage presentations on compilation methods of heterogenous data sets, multiscale and multidisciplinary approaches for natural resources exploration and geological gas storage purposes, and other environmental applications. Potential field applications in exploration and geological interpretation of magnetic anomalies, jointly with other geodata, are warmly welcome.

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.

Earth’s cryosphere demonstrates itself in many shapes and forms, but we use similar geophysical and in-situ methods to study its wide spectrum: from ice-sheets and glaciers, to firn and snow, sea ice, permafrost, and en-glacial and subglacial environments.
In this session, we welcome contributions related to all methods in cryospheric measurements, including: advances in radioglaciology, active and passive seismology, geoelectrics, acoustic sounding, fibre-optic sensing, GNSS reflectometry, signal attenuation, and time delay techniques, cosmic ray neutron sensing, ROV and drone applications, and electromagnetic methods. Contributions can include field applications, new approaches in geophysical or in-situ survey techniques, or theoretical advances in data analysis processing or inversion. Case studies from all parts of the cryosphere, including snow and firn, alpine glaciers, ice sheets, glacial and periglacial environments, alpine and arctic permafrost as well as rock glaciers, or sea ice, are highly welcome.
This session will give you an opportunity to step out of your research focus of a single cryosphere type and to share experiences in the application, processing, analysis, and interpretation of different geophysical and in-situ techniques in these highly complex environments. This session has been running for over a decade and always produces lively and informative discussion. We have a successful history of PICO and other short-style presentations - submit here if you want a guaranteed short oral!

The urgency, complexity, and economic implications of greenhouse gas (GHG) emission reductions demand strategic investments in science-based information for planning, implementing, and tracking emission reduction policies and actions. An increasing number of applications succeed by combining activity-based emissions data with atmospheric GHG measurements and analyses – this hybrid approach can yield additional insights and practical information to support mitigation efforts at different scales. Inspired by this potential, the Integrated Global Greenhouse Gas Information System (IG3IS) of the World Meteorological Organization works to identify and document good practice guidelines for informing decisions, while promoting scientific advances and facilitating two-way linkages between practitioners and stakeholders in the policy realm, tailoring research actions to meet policy needs.
Since EGU18, this session continues to showcase how scientific data and analyses can be transformed into actionable information services and successful climate solutions for a wide range of user-communities. Actionable information results from data with the required spatial and temporal granularity and compositional details able to explicitly target, attribute and track GHG emissions and reductions where climate action is achievable.
This session seeks contributions from researchers, inventory compilers, government decision and policy makers, non-government and private sector service providers that show the use and impact of science-based methods for detecting, quantifying, tracking GHG emissions and the resulting climate mitigation. We especially welcome presentations of work guided by IG3IS good practice research guidelines at urban and national scale and for specific economic sectors. The scope of the session spans measurements of all GHGs and from all tiers of observation.

The growing global resource scarcity along with the criticality of high-tech-relevant raw material, poses immense challenges for the sustainable development of our society. Reducing the environmental footprint of mineral exploration and extraction requires sustainable solutions that are socio-economically viable. In this context, an accurate and effective resource characterization is essential not only for supporting economic resilience but also for mitigating environmental impacts and advancing the transition toward sustainable, semi-circular economic models. Emerging technologies, from autonomous robotic explorers to real-time data analytics, are redefining what is possible in mineral exploration and production. These innovations open opportunities to re-evaluate previously “non-economical” deposits, including abandoned sites, ultra-deep reserves, and small-scale resources, and to optimize recovery processes and footprints.

This session targets innovative tools and methodologies that are redefining raw material exploration and characterization. We emphasize multi-scale, multi-source and multi-disciplinary approaches that integrate advanced sensing, modelling, automation and data-driven solutions. The session focuses in particular on method innovations in the field of remote sensing, geophysics, geochemistry, raw material processing, as well as on recycling processes.

We encourage interdisciplinary studies which use a combination of methods to solve challenges as diverse as, but not limited to:
• Next-generation sensing and imaging: non-destructive techniques, core scanners, and airborne/ground-based sensors for high-resolution, accurate, precise, and efficient resource identification.
• Smart field and analytical approaches: geophysical and geochemical mapping, isotope dating, and novel sampling workflows for multi-scale ore body understanding.
• Digital modelling and simulation: advanced conceptual models and quantification methods for deposits and mineral systems.
• Automation and real-time decision-making: AI-driven, automated data processing that enhances resource management, mining selectivity, and recycling efficiency.
• Information integration and visualization: innovative platforms for merging data streams from diverse sensors to improve accuracy and reduce uncertainty.
• Data-driven discovery: machine learning, geostatistics, data fusion, and computational advances unlocking new insights in mineralogy and geochemistry.