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.
Orals: Wed, 6 May, 10:45–15:45 | Room -2.15
The use of high spatial resolution orbital thermal infrared (TIR) data for certain geoscience applications has been possible for the past four decades. Satellites having one or two TIR spectral bands were able to detect the spatial patterns and temporal baselines of surface temperature; however, they do not provide any information on emissivity variation (essential for mapping critical minerals), and less accurate temperatures than multispectral TIR systems. In 2000, ASTER (the first multispectral TIR sensor with sub 100 m spatial resolution) was launched and has acquired data for over 25 years but will be decommissioned in 2026. A similar instrument (ECOSTRESS) was launched to the International Space Station (ISS) in 2018 and is still functioning, but it will be retired in 2030 with the ISS leaving a gap in US multispectral TIR capability. Multispectral TIR data expanded what was possible in the geosciences, providing compositional information such as surface mineralogy, thermal inertia, and particulate mapping, together with more accurate and refined uses of surface temperatures. Several countries/space agencies are planning high spatial, high temporal resolution multispectral TIR missions in the near future that will provide continuity and greatly expand possible applications with much higher repeat times. One of these, the Surface Biology and Geology (SBG-TIR) mission would provide MIR (3–5 μm) and TIR (8–12 μm) image data at ~ 60 m spatial resolution every 1-3 days. SBG-TIR is a joint-endeavor between NASA and ASI in Italy with planned geoscience data products such as surface mineralogy and volcanic activity, whereas the other planned missions do not have this geological focus. The TIR spectral resolution was increased to six bands for SBG-TIR, which vastly improves the capability of discriminating feldspar and clay mineralogy mapping as well as aerosol detection in sulfur dioxide rich plumes. The global mapping of the major rock-forming minerals and their weight percent silica together with the detection of subtle thermal and compositional changes at volcanoes will be possible for the first time with SBG-TIR. As part of the mission development, our work examined prior ASTER and airborne MASTER TIR data to test both the mineral mapping and precursory thermal volcanic eruption signal detection possible with SBG-TIR. ASTER provides the long time series to quantify low-level anomalies and small eruption plumes over long periods, whereas the airborne MASTER provides the spectral resolution necessary to identify minerals. The findings of the surface mineralogy and volcanic activity algorithm development will be presented and compared to those from the other planned TIR missions with lower spectral resolutions. Critically however, the SBG-TIR mission’s future is now uncertain due to recent budgetary reductions by the United States federal government. While the other European multispectral TIR mission move ahead, NASA is in danger of permanently losing its advantage in this technology space. This looming high resolution, multispectral TIR gap will reduce science outcomes and render others such as mineral mapping impossible.
How to cite: Ramsey, M., Hook, S., and Thompson, J.: Advancing the geosciences with thermal infrared orbital data: Future possibilities or a looming data gap? , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14391, https://doi.org/10.5194/egusphere-egu26-14391, 2026.
The Committee on Earth Observation Satellites (CEOS) produces and maintains a series of Analysis Ready Data (CEOS-ARD) Product Family Specifications (PFS) across Earth observation technologies. Each PFS provides a mandated list of specifications for pre-processing, metadata, and documentation, providing value for interoperability, benchmarking, procurement, and user confidence.
This submission presents an overview and update on the CEOS-ARD Surface Temperature (ST) PFS Version 6.0, which recognises and accommodates the evolving user base, technology, and applications of space-based infrared data from public and commercial sector missions. The ST PFS applies to designers and deployers of missions operating in the thermal infrared (TIR and MWIR) and microwave wavelengths at all scales.
New metadata requirements are being introduced to support the varying types of surface temperature products: land surface temperature, surface brightness temperature, and water surface temperature. The PFS also features updates in line with the Future CEOS-ARD Strategy, with modifications to requirements on data quality, radiometric stability, and other general metadata while also providing better support for higher level applications and harmonisation between CEOS-ARD PFS. The CEOS-ARD Oversight Group invites feedback, contribution, and early adoption.
More information on CEOS-ARD can be found at ceos.org/ard.
How to cite: Khalsa, S. J., Jones, H., Steventon, M., Strobl, P., Sarelli, A., and Wong, J.: CEOS Analysis Ready Data Surface Temperature Product Family Specification V6.0, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13110, https://doi.org/10.5194/egusphere-egu26-13110, 2026.
The inversion of the radiative transfer equation to retrieve both the surface temperature (LST) and emissivity (LSE) values from top-of-atmosphere (TOA) radiances in the thermal infrared (TIR) domain (8-14 µm) is a not straightforward problem. Marcq et al. (2023) proposed the algorithm DirecTES to invert LST using a spectral library of emissivity of various materials, to be applied on several TIR channels. The algorithm consists in inverting the radiative transfer equation for the LST, for each material of the library. A threshold criterion selects materials of the library for which the standard deviation of retrieved LST across different TIR channels is below 3K. The final LST and LSE are the median of the values retrieved for the selected materials. However, a constant threshold is problematic because sometimes no material in the library may match the criterion and thus the LST may not be retrieved on some pixels of a satellite image. Moreover, DirecTES’s original spectral library (SAIL179) is only composed of vegetation and arid surface materials and performs poorly on desertic surface pixels.
This study focuses on optimizing DirecTES in the TIR channels of the upcoming TRISHNA instrument conjointly developed by CNES (France) and ISRO (India). A new universal spectral library of emissivity that could be applied to any type of observed land surface of the globe is built with 150 emissivity spectra from the CAMEL database, categorized into four main classes (arid, desert, snow-covered or vegetated). In most cases, the category of the observed surface in a satellite image pixel is not known. We propose an optimization of DirecTES’s criterion that consists in selecting from the spectral library only the 10 materials with the lowest LST standard deviation between TIR channels. This new approach efficiently selects materials in the appropriate emissivity category on any surface, thus reducing the bias and RMS error on the retrieved LST and LSE. In addition, this new approach corrects the limitation of the original DirecTES criterion and can retrieve the LST and LSE on every pixel of the processed image.
The performances of the new DirecTES criterion and spectral library are evaluated, using TOA radiances simulated from the CAMEL emissivity database and the TIGR atmospheric database. LST is retrieved with a biais < 0.1K and a RMSE < 0.6K on vegetated surfaces and < 0.8K on arid and desert surfaces. LSE is retrieved with a RMSE < 0.02 for all TRISHNA TIR wavelengths. For desertic areas, performances are further improved when adding a few more emissivities from these specific regions to the spectral library used by DirecTES, while not affecting the performances on the other regions.
Finally, DirecTES is validated with match-up data of TOA radiances measured by ECOSTRESS and LST ground measurement at La Crau, France. For 53 match-ups dates of 2023, the LST is retrieved with a bias < 0.15K and RMSE < 0.9K.
How to cite: Farin, M., Marcq, S., Delogu, E., Ramon, D., and Elias, T.: Optimization of DirecTES thermal infrared land surface temperature and emissivity separation algorithm for the upcoming TRISHNA mission, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1526, https://doi.org/10.5194/egusphere-egu26-1526, 2026.
Ground-based hyperspectral longwave infrared (LWIR) images of 90 soil samples from the legacy soil spectral library of Israel were acquired with the Telops Hyper-Cam sensor. Mineral-related emissivity features were identified and used to create indicants and indices to determine the appearance and content of quartz, clay minerals, and carbonates in the soil in a semi-quantitative manner—from more to less abundant minerals. The resultant most abundant mineral(s) fit the results of the XRD analysis in most (90%) of the soil samples. The full mineralogy, including the relative amounts of the less abundant minerals, of most (75%) of the soil samples fit the XRD analysis results.
These hyperspectral LWIR images were resampled to the multispectral LWIR configurations of the airborne sensor Airborne Hyperspectral Scanner (AHS) and present and future spaceborne sensors—Land Surface Temperature Monitoring (LSTM), ECOSTRESS and Thermal Infra-Red Imaging Satellite for High-resolution Natural Resource Assessment (TRISHNA). The emissivity spectrum of each soil sample was calculated and then spectral indicants were created, for each spectral configuration, to determine the content of quartz, clay minerals and carbonates in each soil. The resulted mineral classification, in all spectral configurations, of the most abundant mineral(s) fit the XRD analysis results in most (90-80%) of the soil samples. However, identifying the less abundant minerals in each soil, and determining the mineralogy, from more to less abundant, using multispectral-based created indicants, was enabled only with the AHS configuration.
How to cite: Ben-Dor, E. and Notesko, G.: Spectral indicants to determine the most abundant mineral(s) in soil samples,using LWIR hyper- and multi- spectral configurations., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1662, https://doi.org/10.5194/egusphere-egu26-1662, 2026.
Understanding the surface energy balance is essential for studying land-atmosphere interactions and their impact on weather, climate, and hydrology. Accurate estimation of sensible and latent heat fluxes is critical for applications like hydrological modelling and climate studies, but traditional methods like eddy covariance are limited in spatial coverage. Remote sensing technologies, particularly models like the Two-Source Energy Balance (TSEB), address these limitations by partitioning energy fluxes between soil and vegetation using spatially distributed observations such as surface temperature and vegetation indices. Advances in TSEB include refined resistance networks for modelling soil-canopy interactions and improved disaggregation of surface temperatures into soil and canopy components, with iterative algorithms enhancing flux partitioning. Challenges remain in accounting for vegetation clumping and accurate modelling in water-limited ecosystems. In this study, the potential of three thermal data providers, Ecostress and Landsat from NASA and Sentinel-3 from ESA, in estimating evapotranspiration using TSEB was assessed. Other data, like meteorological, is the same for both simulations. We want to see how the quality of the thermal data, resolution and accuracy, affects the result of TSEB. This study is necessary to determine the minimum requirements of a thermal imagery dataset, suitable for this use-case. The final aim is to improve water productivity and improve yield by early detection of water stress in crops, before it becomes visible.
How to cite: Yadollahi, S. and Tychon, B.: The effect of thermal image quality on the estimation of Crop Evapotranspiration, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18886, https://doi.org/10.5194/egusphere-egu26-18886, 2026.
Drought-induced stress of crops increasingly threatens agricultural yields and consequently food production security, which becomes even more challenging due to growing climatic instability. Consequently, the early detection of water-stress-related responses in crops is important to administer precise irrigation as well as for identifying varieties resilient to drought.
While multi- and hyperspectral remote sensing in the visible, near-, and short-wave infrared (VNIR/SWIR, 0.4–2.5 µm) is an established and robust tool for spatially assessing and monitoring vegetation vitality, less focus has been given to high-resolution spectral data covering the long-wave infrared (LWIR) so far. However, advancements in airborne sensors close this gap and allow for capturing detailed spectral information of vegetation components that are sensitive to water stress and show their fundamental vibrational features in the LWIR. Against this background, this case study evaluates the potential of airborne hyperspectral LWIR emissivity and temperature data to differentiate crop species and varieties.
The experimental setup is located at the Strenzfeld agricultural test site close to Bernburg, Central Germany, and comprises 32 plots, each approximately 67 x 9 m. The study includes three crop species (peas, winter wheat, and summer barley) with two varieties each, planted in four replicates, alongside eight bare soil plots. Hyperspectral LWIR data (7.4–11.8 µm, spectral resolution 6 cm-1, spatial resolution 0.77 x 0.77 m) were recorded on 6 May 2025 using a Telops Hyper-Cam Airborne Mini. Data preprocessing, including geometric corrections and data cube mosaicking, was conducted using Reveal Airborne Mapper, while temperature-emissivity separation was employed via Reveal FLAASH-IR. Additionally, UAV-based broadband thermal data and RGB orthomosaics were acquired with DJI Zenmuse XT2 and DJI Zenmuse H20T sensors to coincide with the aircraft overpass.
Emissivity spectra and temperature data were analysed at the plot-level to identify crop-specific spectral features and assess inter- and intra-class variations. Principal Component Analysis (PCA) was used to explore clustering within the spectral data. To account for differences in vegetation cover and the background soil signal, (partial) unmixing approaches exploiting vegetation and bare soil emissivity spectra were used as well as spectral indices. Furthermore, an inter-comparison of the temperature values derived from the Hyper-Cam Airborne Mini and the DJI Zenmuse XT2 was performed.
The findings of this case study contribute to a better understanding of LWIR emissivity signatures of different crops and their variability. Initial results show that in addition to crop-specific traits, vegetation cover and thus the soil signal distinctively impact the observed emissivity and temperature values. This highlights the importance of selecting optimal phenological windows for data acquisition. A planned follow-up study will incorporate multi-temporal airborne LWIR data acquisitions and controlled irrigation experiments in order to identify crop varieties with increased drought-resilience.
This research is funded by the German Research Foundation (DFG, grant number: 514067990) and by the Federal Ministry of Agriculture, Food and Regional Identity (BMLEH, grant number: 28DE205A21).
How to cite: Denk, M., Sander, B., and Knauer, U.: Analysis of crop species and varieties using airborne long-wave infrared hyperspectral imaging: a case study at Bernburg-Strenzfeld, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18259, https://doi.org/10.5194/egusphere-egu26-18259, 2026.
Imaging Fourier-Transform Infrared (FTIR) spectroscopy in the long-wave infrared (LWIR) domain (7–14 µm) offers unique capabilities for the identification and mapping of surface materials based on their distinct spectral emissivity signatures. While laboratory applications are well-established, airborne deployment for complex urban environments remains a developing field. This study presents initial results from a recent test campaign conducted on the 20th of May 2025 in Dessau, Germany by utilizing the Telops Hyper-Cam Airborne Mini. The objective of this research was to evaluate the sensor's capability to detect and discriminate common urban surface materials such as concrete, asphalt, roofing tiles, and potentially polymers and metals under real-world flight conditions. The hyperspectral data cubes were acquired over an industrial urban area at an altitude of around 800 meters above ground resulting in a resolution of 60 cm per pixel with a spectral resolution of 6.5 wavenumbers. The airborne measurements were validated through comparison with a laboratory-based spectral reference library acquired under controlled conditions. The comparison with laboratory spectra provides critical insights into the reliability of airborne FTIR data. In particular, we utilized a spectral library developed by King’s College London as a reference standard, consisting of representative material samples collected from the London area. We performed a comparative analysis between the atmospherically corrected airborne emissivity spectra (processed by FLAASH-IR) and the laboratory emissivity reference signatures. The results demonstrate a strong correlation between the airborne data and the laboratory measurements. Specifically, the system showed high proficiency in distinguishing between silicate-based materials and metal due to their characteristic absorption and emissivity features in the LWIR region. However, challenges remain in classifying asphalt, solar panels, and roofing materials due to surface conditions and low spectral contrast as well as the problem of spectral mixing. This study highlights the potential of the Telops Hyper-Cam Airborne Mini for hyperspectral urban material mapping and addresses challenges that need to be solved in the future. Our findings contribute to a better understanding of urban surface heterogeneity and support the planning of future airborne campaigns for urban planning and environmental monitoring applications.
This research is funded by the German Research Foundation (DFG, grant number: 514067990).
How to cite: Palmer, J. W., Sander, B., Marković, M., and Pause, M.: Characterization of Urban Surface Materials using Airborne Imaging FTIR Spectroscopy: First Results from a Campaign in Dessau, Germany, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12526, https://doi.org/10.5194/egusphere-egu26-12526, 2026.
Deriving land surface temperatures (LST) from aerial thermography requires surface emissivity information, which is typically assumed uniform despite considerable variation across urban materials. We present PROMETHEUS, a workflow that uses a fine-tuned Large Vision Model (LVM) to produce city-scale material classification at airborne resolution. This classification enables emissivity-based LST estimation following the GRAZ method, which uses three-dimensional Monte Carlo sampling to determine view factors for reflected thermal radiation and models elevation-dependent atmospheric transmittance, upwelling and downwelling radiation. We applied this workflow to a 100×100 km area centred on Klagenfurt, Austria, where thermal infrared imagery at 1 m resolution was acquired on August 10-11, 2024 during a summer heat period, with daytime and nighttime flights at 1600 m altitude. A team of 12 surveyors collected concurrent in-situ land and water surface temperatures across 13 stations throughout the city. Using existing 5 cm RGB and near-infrared orthoimagery combined with photogrammetric building segmentation, expert annotators labelled rooftop materials across 30 classes via a collaborative platform with a standardized material guide. These labels were used to fine-tune an LVM that then classified materials across the full study area. The output was merged with municipal land cover data and converted to emissivity values using a look-up table derived from spectral libraries. Atmospheric parameters were obtained from ECMWF profiles. Comparison with in-situ measurements shows improved LST retrieval relative to uniform emissivity assumptions, particularly for low-emissivity surfaces such as metal roofing. This workflow demonstrates a practical approach for scaling limited expert annotations to city-wide material mapping.
How to cite: Tiede, D., Sudmanns, M., Aragon, M., Gomez, J., Arellano, C., Rüdisser, D., Klaußner, S., and Koren, G.: PROMETHEUS: City-scale material mapping with large vision models for emissivity-based airborne thermography, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22977, https://doi.org/10.5194/egusphere-egu26-22977, 2026.
Thermal InfraRed (TIR; 10-12 μm) remote sensing provides a robust means to quantify Earth’s emitted radiation, enabling the characterisation of surface thermal state and properties. In volcanic environments, these parameters are directly linked to subsurface processes, energy transfer mechanisms, and eruptive dynamics. However, continuous ground-based monitoring is often impractical, especially in remote or inaccessible regions, due both to logistic constraints and hazardous conditions. As a result, satellite-based thermal observations frequently represent the only viable source of systematic, long-term monitoring.
Volcanic heat flux constitutes a fundamental constraint on volcanic processes and eruption dynamics, yet its estimation from space remains incomplete. Current satellite-based retrievals are largely biased toward Mid-InfraRed (MIR; 3.5-4.5 μm) channels, which are well suited for detecting high-temperature eruptive phenomena. When applied to moderate- and low-temperature volcanic processes, however, MIR-based methods underestimate radiative outputs by up to ~90%, limiting their ability to characterise and quantify hydrothermal activity, unrest, eruptive state transitions, and post-eruptive dynamics.
Recent advances in TIR sensor performance, data availability, and processing capabilities have renewed interest in the TIR domain, demonstrating that TIR observations are not merely complementary to MIR data but essential for capturing a wider spectrum of volcanologically relevant parameters.
Here, we illustrate the advantages of TIR-based approaches for volcano monitoring and present recent methodological advances in TIR data processing, from the use of a dedicated hotspot detection algorithm (TIRVolcH) to retrieve spatially resolved quantitative information, to the application of the recently proposed TIR-based Volcanic Radiative Power (VRPTIR) for quantifying energy release from selected targets and assessing their behaviour. We then show that the synergistic integration of TIR and MIR observations enables discrimination among volcanic features and processes, timely detection of eruptive state transitions, and revision of global volcanic radiative budgets by a factor of 2-20.
How to cite: Aveni, S., Ganci, G., and Coppola, D.: TIR Remote Sensing of Volcanic Systems: Recent Advances and Future Perspectives, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19440, https://doi.org/10.5194/egusphere-egu26-19440, 2026.
Image Super-Resolution (SR) models are advanced image processing techniques designed to increase the spatial resolution of digital images by reconstructing fine details from low-resolution inputs while preserving essential characteristics of the original data. SR methods are particularly valuable when high spatial detail is needed but not directly available, enhancing the interpretability of degraded or coarse imagery.
In satellite thermal observations, SR is especially relevant. Thermal Infrared (TIR, 8–14 µm) images, used to measure surface thermal radiation, generally exhibit low spatial resolution and higher noise than optical imagery. These limitations hinder the identification and quantification of fine-scale thermal features, including localized hotspots, small eruptive vents, and narrow lava flows.
Here, we propose a super-resolution method for multispectral thermal images based on advanced artificial intelligence, implemented through a deep Residual Neural Network (ResNet) architecture. Trained on paired low- and high-resolution thermal datasets, the model learns the complex non-linear relationships required to recover high-frequency spatial information typically lost in coarse TIR imagery. Residual learning allows the network to focus on reconstructing missing fine-scale structures, improving training stability and enhancing subtle thermal gradients. The architecture mitigates vanishing-gradient issues and enables deeper networks capable of extraxùcting thermally meaningful features without amplifying noise.
The resulting model reconstructs fine thermal structures—such as narrow lava flows and localized hotspots—producing coherent and physically interpretable thermal maps. ResNet-based SR enables the integration of the broad coverage offered by low-resolution sensors with the detail provided by high-resolution platforms.
From a volcanic monitoring perspective, thermal SR improves the detection and tracking of eruptive features, providing more precise and timely information on volcanic activity. Overall, applying advanced SR techniques to satellite thermal imagery enhances active volcano surveillance and contributes to a more accurate understanding of volcanic thermal processes.
How to cite: Di Bella, G. S., Corradino, C., and Del Negro, C.: Advanced Volcanic Monitoring: AI Super-Resolution for Thermal Satellite Images, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1250, https://doi.org/10.5194/egusphere-egu26-1250, 2026.
In volcanic regions, Thermal InfraRed (TIR) remote sensing is a well-established technique for detecting ground thermal anomalies. The analysis of thermal properties, particularly of Land Surface Temperature (LST) time series, represents a valid tool to achieve a rapid characterization of the shallow thermal field, supporting ground-based surveillance networks in the monitoring of volcanic activity, especially in areas that are inaccessible due to high volcanic hazard.
However, in complex active volcanic and hydrothermal settings, the coexistence of processes of different natures that interact and mutually interfere can significantly affect the distribution of the LST parameter, making it challenging to interpret its spatio-temporal variations. In this context, the extraction of the main thermal patterns of volcanic areas from satellite-derived LST time series represents a further step for a more detailed characterization of the shallow thermal field.
In this study, the extraction of the main thermal patterns from satellite-derived LST time series is addressed through decomposition techniques such as the Independent Component Analysis (ICA) and the Dynamic Mode Decomposition (DMD). ICA is a statistical method aimed at identifying a linear transformation of the data that maximizes the statistical independence between its components, defining the signal’s independent components (ICs). DMD is a data-driven technique aimed at decomposing spatio-temporal data for the extraction of coherent features, defining a set of dominant dynamic modes (DMs).
The investigated area is the Campi Flegrei caldera (southern Italy), a well-known complex volcanic system. The LST time series is retrieved from cloud-free nighttime TIR images acquired by Landsat-8 and Landsat-9 missions (L8 and L9) during the 2018–2025 time interval. Specifically, the LST parameter is estimated through the Radiative Transfer Equation (RTE) applied to a single thermal band (Band 10 for both L8 and L9) and with known information on the surface emissivity and atmospheric conditions of the investigated area. Subsequently, the application of ICA and DMD methods allowed the identification of the main components, revealing the dominant thermal patterns influencing the LST distribution and providing insights into the endogenous and exogenous processes characterizing the volcanic site.
How to cite: Mercogliano, F., Barone, A., Castaldo, R., D'Auria, L., Silvestri, M., Marotta, E., Peluso, R., and Tizzani, P.: Extracting Thermal Patterns in Volcanic Areas from Thermal Infrared Satellite Data: A Case Study at the Campi Flegrei Caldera , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19538, https://doi.org/10.5194/egusphere-egu26-19538, 2026.
The next generation of thermal infrared (TIR) sensors will provide higher spatial and temporal resolution data than currently available. These include the ISRO-CNES’s Thermal infraRed Imaging Satellite for High-resolution Natural Resource Assessment (TRISHNA), ESA’s Land Surface Temperature Monitoring (LSTM), and NASA-ASI’s Surface Biology and Geology (SBG) missions. The near-daily coverage at ~60m spatial resolution will be invaluable for volcano monitoring but introduces new challenges. The large and complex data volumes from these missions require new advanced analytical approaches for effective detection of volcanic unrest. The 25-year archive of 90 m spatial resolution TIR data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) has accurately detected both large surface temperature variations during eruptive activity and subtle anomalies (1-2K) associated with degassing and precursory summit activity. Preliminary studies on eruption forecasting potential used ASTER data to constrain models of magmatic and geothermal processes, both crucial for improving hazard mitigation. A machine learning (ML) version of the Automated Spatiotemporal Thermal Anomaly Detection (ASTAD) algorithm, a CNN-based model specifically designed for ASTER data, achieved improved detection rates. CNN models are well suited for extracting spatial and thermal features as well as identifying subtle anomalies. The combination of ASTER’s spatial resolution and ASTAD-ML’s pattern recognition capabilities allows us to retrospectively test the approach globally in preparation for future missions. Here, we show the capability of ASTAD-ML by designing a global cloud-based AI platform populated with ASTER data. We applied the ASTAD-ML model to 100 representative volcanoes spanning a wide range of thermal, morphological, and volcanological activity types. The model includes both day and night data, as well as scenes typically discarded due to cloud cover or partial data loss/stripping. We evaluated both pixel-based and event-based performance, achieving BF1 and F1 high scores of 0.80 and 0.89, respectively. The ASTAD-ML model's pattern recognition capabilities both expanded the usable dataset and improved the accuracy of automatic early volcanic unrest detection. The methodology is highly adaptive, and further testing is ongoing in preparation for these future high spatial resolution TIR sensors, enabling significantly improved monitoring of global volcanic activity.
How to cite: Corradino, C., Pailot-Bonnétat, S., Ramsey, M. S., Thompson, J. O., and Collins, E.: Monitoring precursory volcanic activity: Applying convolutional neural networks to the decades-long ASTER archive, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14953, https://doi.org/10.5194/egusphere-egu26-14953, 2026.
Lava tubes are key targets for planetary exploration due to their potential to preserve biosignatures and could serve as human habitats on the Moon. These caves form when lava flows solidify, leaving behind a tube-like void once the lava drains. Their stability is determined mainly by the thickness of the roof, a parameter that is challenging to estimate using current remote sensing methods, as visible imagery alone cannot discern the physical properties of the subsurface. Accurate characterization of roof thickness is crucial for future exploration efforts, as stable roofs are more likely to preserve potential biosignatures within the cave interior and provide safer environments for human exploration. Remote sensing is currently the primary method for studying lava tubes on other planetary bodies and in remote regions of Earth. Previous work has identified potential subsurface voids on the Moon and Mars using thermal infrared (TIR) imaging by analyzing the area's thermal inertia and temperature differences between lava tubes and surrounding terrain. Thermal inertia is an intrinsic material property that determines the material's resistance to changes in temperature and is affected by subsurface voids, which disrupt heat transfer. This study aims to constrain the maximum roof thickness that a lava tube can have to be detected with TIR remote sensing data, which can help estimate the roof thickness of lava tubes on Earth and other planetary bodies.
We present field, remote sensing, and numerical results of the thermophysical properties of lava tubes on Earth at two sites: Pisgah Crater, California, and Tabernacle Hill, Utah, with a total of 38 skylights and lava tube entrances surveyed. Satellite TIR images were acquired and compared with in-situ drone-based TIR images, both of which were used to calculate the thermal inertia of the area. To validate these observations, we utilized numerical heat transfer models to simulate thermal diffusion through basaltic roofs of varying thicknesses. The known lava tube locations were mapped, and their thermal inertia value was averaged to calculate the thermal inertia difference from the rest of the void-free terrain. These values were compared with in-situ measurements of roof thickness at each cave entrance.
Our study reveals a distinct decrease in the thermal difference from the background with increasing roof thickness, suggesting that thicker roofs behave more like the surrounding terrain. The observed data suggest that a roof thickness of at most 2 meters is required for potential detection in an Earth environment. This research helps establish a critical detection threshold, where TIR anomalies may be diagnostic of thin, potentially unstable roofs, while roofs thicker than 2 meters are likely stable but thermally indistinguishable from the background. Thermal anomalies are more distinct than visible data alone for identifying skylights in rough terrains, but larger and more stable roofs may be more challenging to detect than smaller roofs. This research reinforces the utility of TIR in identifying skylights in rough terrains. It establishes an essential constraint for the detectability and stability of lava tubes, providing a valuable framework for planetary remote sensing and future mission planning.
How to cite: Sostre-Cortes, J., Rivera-Hernandez, F., and McKeeby, B.: From Anomaly to Detectability: Roof Thickness Threshold for Remote Detection of Lava Tubes Using Thermal Infrared Datasets, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14996, https://doi.org/10.5194/egusphere-egu26-14996, 2026.
Evaluation of Improved Hyperspectral Gas Detection Algorithms Using Hyper-Cam Airborne Nano Airborne Data
Hyperspectral remote sensing enables the accurate characterization of gases from a distance, providing a safe and efficient means to identify gas releases for research, industrial monitoring, and threat assessment of unknown substances. Recent advances in airborne hyperspectral imaging systems—such as Telops’ Hyper-Cam Airborne Nano, a compact long-wave infrared (LWIR) hyperspectral imager—illustrate the growing capability to acquire spatially and spectrally resolved infrared measurements from aerial platforms. Telops hyperspectral systems have long been at the forefront of gas detection, identification, and quantification using thermal infrared imaging. However, improving the spectroscopic accuracy of hyperspectral imaging systems while maintaining spatial resolution remains a challenge, particularly when compared to the high spectral resolution of one-dimensional instruments. The work presented here showcases ongoing efforts to enhance hyperspectral gas analysis through the development of a new detection and identification (D&I) algorithm designed to improve multiple stages of the detection process.
D&I Algorithm Improvements
The updated algorithm builds on the original GLRT (Generalized Likelihood Ratio Test) which is good for detecting spectral anomaly that correlates with a given spectrum, but which is often non-specific. Within the new algorithm, the GLRT-detected pixels are then grouped together according to their spatial connection to get a list of plumes to investigate. The spectral radiance of the whole datacube is then separated in clusters of similar pixels. Using principal component analysis (PCA), the background behind the plume of interest is estimated. Using the background, the plume spectral transmittance is estimated. The spectral transmittance is then compared to the theoretical signature to get a similarity value (correlation) for each investigated plume. A threshold is applied to eliminate all plumes which are considered as false alarm. Throughout the work, it was mandatory to have fewer false alarms compared to the old algorithm, maintain real-time detection and identification performances and good performances for ground based and airborne operations.
Results
The dataset used to evaluate the new algorithm consists of several controlled gas release experiments conducted under varied conditions for both ground-based and airborne configurations. A portion of the results presented here is derived from a recent airborne data collection campaign performed using the Hyper-Cam Airborne Nano hyperspectral imaging system. Algorithm performance was quantified using Receiver Operating Characteristic (ROC) curves (true positive rate versus false positive rate) to compare the new algorithm against the previous implementation. The selected performance metric—the integral of the ROC curve between 0 and 0.1 false positive rate—increased from 0.0279 for the original algorithm to 0.0623 for the updated version, representing more than a twofold improvement (Figure 2). These results demonstrate a significant reduction in false alarms for common objects (e.g., vehicle windshields, clothing, quartz), unrelated gaseous signatures, and motion-induced artefacts, while maintaining robust detection performance.
How to cite: Gagnon, J.-P., Larivière-Bastien, M., and Dumont, A.: Evaluation of Improved Hyperspectral Gas Detection Algorithms Using Hyper-Cam Airborne Nano Airborne Data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3614, https://doi.org/10.5194/egusphere-egu26-3614, 2026.
Studying the thermal behavior of structures in outdoor conditions, using thermal infrared thermography coupled with local temperature and heat flux probes, is a multidisciplinary field of research and development. It requires to address: system design, informatics, infrared radiometry, signal and image processing, heat transfer and inverse problems domains. In the present study, we present an instrumentation solution system developed in our team to address the remote monitoring of structures in outdoor conditions and its data management. Online infrared measurement corrections, for instance due to variable atmospheric conditions at ground level, are made by using a local weather station equipped with a pyranometer. In case of failure, alternative opportunistic solutions were investigated (Toullier and Dumoulin, 2024), and various strategies of measurements corrections were studied. Comparison of surface temperature measured by infrared thermography and local probes requires to identify the emissivity of materials in the spectral bandwidth used. Such measurements can be made in laboratory but also, when studied surfaces are accessible, by using a portable emissometer. Preliminary results obtained with a 4 spectral band portable emissometer prototype, on a hybrid solar road mock-up deployed in outdoor conditions, will be presented and discussed. To complete, management of acquired data will be presented and discussed in a long term monitoring view. Conclusions on results obtained with a focus on uncooled thermal infrared data will be proposed. Perspectives will address both monitoring system but also recent progress in uncooled infrared sensors (see for instance https://project-brighter.eu/) and temperature emissivity separation algorithms (Toullier et al., 2025) for ground based monitoring systems.
References
- Toullier, J. Dumoulin, "Bias and bottlenecks study in outdoor long term thermal monitoring by infrared thermography: Leveraging opportunistic data for temperature estimation", Infrared Physics & Technology Journal, Volume 141, August 2024, 105471. https://doi.org/10.1016/j.infrared.2024.105471
- Toullier, J. Dumoulin, L. Mevel "New joint estimation method for emissivity and temperature distribution based on a Kriged Marginalized Particle Filter: Application to simulated infrared thermal image sequences", Science of Remote Sensing (2025), doi:. https://doi.org/10.1016/j.srs.2025.100209
Acknowledgments
The authors thank ANR (French National Research Agency) for supporting part of this work under Grant agreement ANR-21-CE50-0029-23 and BRIGHTER project. BRIGHTER project has received funding from the Chips Joint Undertaking (Chips JU) under grant agreement N°101096985. The JU receives support from the European Union’s Horizon Europe research and innovation program and France, Belgium, Portugal, Spain, Turkey
How to cite: Dumoulin, J., Toullier, T., and Manceau, J.-L.: Remote monitoring of structures by uncooled thermal infrared thermography coupled with local probes and a data management supervisor, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20500, https://doi.org/10.5194/egusphere-egu26-20500, 2026.
Context & Objectives: The northern lands are experiencing a generalised increase in soil temperature, resulting in permafrost thaw and subsequent fast changes on water, heat and matter fluxes in these areas. This triggers many important consequences, including infrastructures destabilisation and release of greenhouse gases. Spaceborne thermal imaging can provide extensive and high-resolution information about the temperature of the arctic continental surfaces. Providing subsurface temperature maps at the scale of a catchment and understanding its interactions with the surface conditions is highly needed for studies of the climate warming induced arctic changes, including permafrost thawing.
Methods: In this study, we used downscaled meteorological data from Nordic Gridded Climate Dataset (NGCD), topographic maps, a land cover map of the region derived from Sentinel-1 and Sentinel-2 data and downscaled Sentinel-3 Land Surface Temperature (LST) images. These surface conditions were combined through a regression model with ten stations of in situ soil-temperature and water content observations positioned along an altitudinal gradient across the Miellajokka watershed, Abisko, Northern Sweden.
Results: We generated soil temperature surface maps for the Abisko region, covering an area of about 52 km² at 300 m spatial resolution. We studied the behaviour of top-layer soil temperature according to climatic conditions, water content, soil properties and surface vegetation.
Conclusion: The developed methodology aims at allowing using satellite images, as thermal observations, for deriving key information about soil thermal regime in the Arctics. By developing this kind of approach, the arctic science community may get tremendous benefit from the future launching of high-resolution TIR observation missions such as TRISHNA and LSTM, for instance for permafrost modelling and climate change impacts assessment.
How to cite: Carry, R., Orgogozo, L., ElKhanoussi, Y., Lundin, E., and Roujean, J.-L.: Estimating for Subsurface Temperature in the Arctic: Study Case in the Miellajokka Catchment, Northern Sweden, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19941, https://doi.org/10.5194/egusphere-egu26-19941, 2026.
Switzerland is among the regions experiencing the strongest warming trends in Europe, with air temperatures increasing well above the global mean. This amplified warming leads to heat stress across terrestrial, aquatic, and cryospheric ecosystems, affecting water availability, ecosystem functioning, and land–atmosphere energy exchange. Capturing these processes requires observations that directly resolve surface temperature dynamics at high spatial and temporal resolution.
Thermal Infrared (TIR) remote sensing has emerged as a key approach to address this need, particularly in light of upcoming satellite missions such as ESA LSTM, CNES/ISRO TRISHNA and NASA SBG-TIR. Over the past four years, different ecosystems in Switzerland have served as testbeds for advancing TIR-based ecosystem research within the ESA PRODEX-funded TRISHNA – Science and Electronics Contribution (T-SEC) project.
This contribution synthesises scientific insights gained from T-SEC, highlighting recent methodological and instrumental advancements in thermal remote sensing. Key developments include modelling of thermal directionality, advances in calibration and validation strategies, and the use of field campaigns and laboratory measurements to better quantify uncertainties in TIR observations at different spatial, temporal, and spectral scales.
The presented work spans a range of contrasting ecosystems, including Swiss forests, alpine glaciers and permafrost sites, and perialpine and alpine lakes. Together, these case studies illustrate the potential and challenges of TIR remote sensing for monitoring ecosystem heat stress, water status, and energy fluxes – always with a particular focus on complex terrain. The results underline the importance of multi-scale, multi-sensor approaches to accurately retrieve surface temperature information. Such information is crucial for understanding ecosystem responses to a rapidly warming climate and for fully exploiting the capabilities of next-generation thermal satellite missions.
How to cite: Naegeli, K., Adams, J. S., Bramati, G., Damm, A., Odermatt, D., Irani Rahaghi, A., Rietze, N., Schaepman-Strub, G., and Schaepman, M.: Warming ecosystems in complex terrain – insights from four years of thermal infreared research in the Swiss bio-hydro-cryo spheres , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20029, https://doi.org/10.5194/egusphere-egu26-20029, 2026.
Posters on site: Wed, 6 May, 16:15–18:00 | Hall X4
High-frequency Thermal Infrared (TIR) observations are essential for characterizing surface temperature anomalies in areas exposed to natural and anthropogenic hazards. However, in densely urbanized regions like Southern Italy, airspace restrictions often delay UAS deployments, hindering real-time data collection during evolving crises. This study explores the integration of UAS within the U-space ecosystem—including network identification and geo-awareness—as a transformative enabler for advanced thermal remote sensing.
We present multidisciplinary case studies in the Campania Region where TIR payloads on UAS platforms were successfully employed for: 1) identifying thermal anomalies in the Campi Flegrei caldera; 2) detecting persistent soil moisture and flood causes in agricultural areas; and 3) assessing fire ignition risks in illegal waste disposal sites; 4) definition of susceptibility maps for the triggering of anthropogenic sinkholes. By overcoming "no-fly zone" limitations through Unmanned Traffic Management (UTM) experiments, we demonstrate how rapid TIR data acquisition provides crucial decision-making tools for risk management.
To bridge the gap between research, monitoring, and operational continuity, we will launch, in agreement with ENAC, an initial U-Space test on the island of Ischia (characterized by volcanic and hydrogeological multi-hazards) since it currently has fewer airspace restrictions.
How to cite: Avvisati, G., Marotta, E., Colucci, O., Menicucci, S., and Barone, A.: Enabling Rapid Thermal Infrared (TIR) Monitoring in Restricted Airspaces: U-space Integration for and Environmental Assessment, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21467, https://doi.org/10.5194/egusphere-egu26-21467, 2026.
Thermal Infrared (TIR) remote sensing from Unmanned Aerial Systems (UAS) has revolutionized the monitoring of volcanic and hydrothermal environments, providing a critical link between ground-based observations and satellite data. In coastal volcanic settings, the identification of hydrothermal discharge points—such as hot springs and fumaroles—is often challenged by their intermittent nature and the dynamic interface between the terrestrial and marine domains.
This study presents a high-resolution thermal mapping survey conducted along the Sant'Angelo beach on the island of Ischia (Gulf of Naples, Italy). By leveraging the flexibility and high spatial resolution of UAS-mounted TIR sensors, we successfully identified and characterized localized thermal anomalies that are otherwise undetectable through conventional field surveys or lower-resolution satellite imagery. A key finding of this work is the detection of a distinctive submarine-to-intertidal fumarolic vent that emerges on the shoreline exclusively during low-tide conditions.
From a hydrogeological perspective, the ability to precisely map these "transient" thermal signatures provides crucial insights into the structural control of fluid migration and the spatial distribution of the hydrothermal system’s discharge zones. These thermal features act as preferential pathways for pressurized fluids, and their characterization is fundamental for refining the hydrogeological conceptual model of the Ischia volcanic system. Our research indicates that UAS-TIR mapping has the potential to enhance coastal hydrogeology in volcanic regions by detecting ephemeral thermal targets and enhancing the assessment of geothermal potential and volcanic unrest indicators. This approach offers a cost-effective and non-invasive methodology for monitoring hydrothermal activity at the land-sea interface, with significant implications for both environmental management and geohazard mitigation.
How to cite: Fabbrocino, S., Marotta, E., Avvisati, G., Belviso, P., Avino, R., Bellucci Sessa, E., Carandente, A., Di Meglio, E., and Peluso, R.: Hydrogeological insights from UAS thermal remote sensing. Case study at Sant'Angelo (Ischia, Italy), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22290, https://doi.org/10.5194/egusphere-egu26-22290, 2026.
The July 4–12, 2024 eruption of Stromboli volcano produced significant effusive activity, pyroclastic density currents and a paroxysmal explosion on July 11, resulting in rapid and substantial morphological changes along the Sciara del Fuoco slope and the summit crater terrace. In this work, we present a quantitative assessment of erupted volumes and associated geomorphological modifications derived from multi-temporal Unmanned Aircraft System (UAS) surveys acquired before, during and after the eruptive sequence.
High-resolution Digital Surface Models (DSMs) and co-registered visible and thermal infrared (TIR) orthomosaics, collected between October 2022 and July 2024, were analysed to reconstruct the evolution of lava flows, erosional features and collapse structures. The integration of TIR data proved essential for identifying active eruptive vents and discriminating cooling lava flows from the complex background of the Sciara del Fuoco. Lava volumes were estimated through a combination of DSM differencing and cross-sectional analyses along the main lava channel, integrating pre-eruptive (May 2024), syn-eruptive (11 July 2024) and post-eruptive (18 July 2024) datasets. TIR surveys provided the thermal constraints necessary to isolate distinct contributions from multiple eruptive vents were quantified, allowing a precise separation of early short-lived lava flows from sustained effusive activity preceding and following the paroxysmal explosion.
Results indicate a total subaerial lava volume of approximately 1.3 × 10⁶ m³ (±20%), with the largest contribution associated with lava emitted from vents located within the central channel. A substantial fraction of this volume formed a lava delta at the coastline, implying the presence of an equivalent or larger submerged deposit. DSM comparisons and thermal anomalies also reveal major erosional processes, including the re-excavation of a pre-existing canyon with an estimated material removal of up to ~5 × 10⁶ m³, and a summit area collapse producing a depression of 70–90 m and a missing volume of ~1.9 × 10⁶ m³.
These results highlight the effectiveness of rapid multi-sensor UAS-based surveying for near-real-time volume estimation and morphodynamic analysis during volcanic crises. This approach provides key constraints for mass balance assessments, hazard evaluation and coastal instability monitoring at active volcanoes such as Stromboli.
How to cite: Famiglietti, N. A., Marsella, M., Coltelli, M., Marotta, E., Memmolo, A., Castagnozzi, A., Cagnizi, M., D’aranno, P. J. V., Lodato, L., and Vicari, A.: Multi-sensor UAS surveys for rapid volume estimation and geomorphological mapping: the July 2024 eruptive crisis at Stromboli volcano, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21199, https://doi.org/10.5194/egusphere-egu26-21199, 2026.
The 2021 Tajogaite eruption on La Palma (Canary Islands, Spain) generated extensive lava flows that still exhibit measurable residual surface heat several years after the eruption. Understanding the spatial distribution and persistence of this heat is essential for characterizing post-eruptive cooling processes and for supporting reconstruction activities in affected areas.
An integrated geospatial workflow was implemented to combine high-resolution UAV-based thermal imagery with lava-thickness models across two sectors affected by the eruption: LPAgricultura (surveyed in February 2024) and LPUrban (surveyed in June 2025). Drone-based radiometric infrared imagery was processed to produce georeferenced thermal mosaics, with emissivity correction (ε = 0.95), and resampled to match the spatial resolution of the corresponding lava-thickness datasets. All data were aligned within a common spatial reference system (REGCAN95 / UTM zone 28N) to ensure pixel-level correspondence.
Thermal anomalies were defined as surface temperatures equal to or exceeding 30 °C. Lava-thickness values were extracted separately for thermally anomalous and non-anomalous areas, enabling a consistent spatial comparison between the two conditions. Statistical analyses were conducted independently for each sector to evaluate the relationship between residual heat and flow thickness.
Results reveal a clear,statistically significant association between elevated surface temperatures and thicker lava deposits across the Tajogaite lava field. In the LPUrban sector, characterized by thicker lava accumulations (mean thickness = 21.5 m; maximum = 57.1 m), thermally anomalous areas have a mean thickness of 31.3 m, compared with 21.3 m in non-anomalous zones (p < 0.001). In contrast, the LPAgricultura sector, dominated by thinner flows (mean thickness = 9.2 m; maximum = 51.5 m), shows mean thickness values of 20.6 m in anomalous areas versus 10.0 m elsewhere (p < 0.001). These patterns indicate that residual heat is preferentially concentrated within the thickest portions of the lava flows, where cooling is constrained by reduced surface-to-volume ratios and enhanced thermal insulation. The adoption of relative thickness thresholds (≥ 20 m in urban areas and ≥ 10 m in agricultural areas) captures approximately 95% of the total surface area of detected thermal anomalies, ensuring consistent sensitivity across both sectors.
The combined use of UAV thermography and lava-thickness models enables a robust characterization of post-eruptive thermal persistence, with direct implications for the assesing lava-flow cooling behavior in complex volcanic terrains.
How to cite: de los Rios-Díaz, H., Afonso-Falcón, D., Ortega-Ramos, V., Álvarez-Hernández, A., González-de-Vallejo, L., Pérez, N. M., and Hernández, P. A.: Revealing heat patterns of lava flows: a spatial data analysis approach using UAV thermography, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10084, https://doi.org/10.5194/egusphere-egu26-10084, 2026.
The 2021 eruption of the Tajogaite volcano (La Palma, Canary Islands) produced a new volcanic cone whose post-eruptive thermal evolution and structural adjustment remain active processes of considerable scientific interest. Characterising how surface temperature patterns evolve over time and how they relate to morphological changes is essential for understanding the stabilization phase of newly formed volcanic edifices.
This study aims to provide a preliminary assessment of the post-eruptive thermal evolution of the Tajogaite cone and to explore its potential relationship with volcano-structural settling.
The analysis integrates multi-temporal UAV-derived thermal imagery and digital elevation models (DEMs). Four thermal UAV surveys acquired at different post-eruptive stages were processed and homogenized in terms of spatial reference, resolution, and alignment to ensure temporal comparability. Two representative periods were selected to analisechanges in surface temperature distribution, while DEMs from two different dates were used to assess morphological variations. Data pre-processing included reprojection, resampling, and quality control procedures, whose reliability was evaluated through statistical comparisons and profile-based analyses. Thermal difference maps and elevation change analyses were subsequently generated.
The results reveal spatially coherent thermal patterns and detectable differences between the analysed periods, consistent with an overall cooling tendency and localized morphological adjustments. These patterns suggest a spatial relationship between surface temperature evolution and structural changes of the volcanic cone, although the magnitude and significance of these relationships require further investigation.
Although preliminary, the results indicate that the combined use of UAV-based thermal data and DEMs is a suitable approach for monitoring post-eruptive volcanic cones. The proposed workflow provides a reproducible methodological framework that may support future, more detailed analyses of cooling dynamics and volcano-structural evolution in newly formed volcanic landforms.
How to cite: Afonso-Falcón, D., de los Ríos-Díaz, H., Ortega-Ramos, V., Rodríguez-Rodríguez, Ó., M.Pérez-Rodríguez, N., DÁuria, L., and Antonio-Hernández, P.: Post-eruptive thermal evolution of the Tajogaite volcano and its relationship with volcano-structural settling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10190, https://doi.org/10.5194/egusphere-egu26-10190, 2026.
Thermal infrared (TIR) remote sensing is an increasingly used technique for studying various natural and anthropogenic processes by evaluating the thermal state of the Earth’s surface. Technological advancements have supported the development of thermal cameras for ground-based, airborne, and satellite platforms. Additionally, Unmanned Aerial Systems (UAS) are increasingly regarded as versatile platforms due to their flexible observation scales.
In a volcanic framework, TIR remote sensing enables the study of ground temperature and the identification of thermal anomalies caused by hot fluid discharge (e.g., gas and lava) or surface heating due to fluid migration in the subsoil during unrest phases, which modify the pressure and temperature conditions of the crust. TIR remote sensing is therefore an essential tool for monitoring and surveillance of active volcanoes, although the spatial coverage and resolution of planned surveys can sometimes be inadequate for emergency management. Indeed, ground-based measurements do not guarantee extensive spatial coverage, while satellite data lack flexibility regarding spatial and temporal resolutions. Finally, airborne measurements are challenging to organize operationally during emergencies and are inherently risky. In this scenario, UAS platforms represent a reasonable trade-off in terms of spatial coverage, resolution, and logistics.
Here, we present a case study of multiplatform (satellite and UAS) TIR remote sensing as part of the monitoring activities at the Campi Flegrei caldera by INGV – OV. This active volcanic system is characterized by complex interactions between magmatic and hydrothermal reservoirs, causing frequent unrest with ground deformation, seismicity, gas emissions, and surface temperature anomalies. Among the latter, we focus on the most significant anomalies located near the Solfatara – Pisciarelli hydrothermal system.
Satellite measurements consist of nighttime images acquired by the Landsat-8 and Landsat-9 satellites from May 2018 to August 2025, with a 100 m spatial resolution, processed to retrieve an approximately monthly distribution of Land Surface Temperature (LST). Conversely, UAS data consist of images acquired monthly by INGV – OV with a 10 cm spatial resolution at flight altitudes ranging from 45 to 70 m. For logistical reasons, the Pisciarelli dataset spans from September 2019 to May 2025, while images of Solfatara were only acquired during the first halves of 2024 and 2025.
The results show that satellite data can detect a single anomaly at the Solfatara – Pisciarelli hydrothermal system without revealing significant temporal variations in temperature. On the other hand, UAS data identify multiple anomalies for both the Solfatara and Pisciarelli sites, highlighting surface heating in Pisciarelli starting around September 2021. This trend is consistent with analyzed seismicity and ground deformation datasets.
This study demonstrates the role of multiplatform TIR data integration in improving monitoring and surveillance activities at active volcanoes.
How to cite: Marotta, E., Barone, A., Peluso, R., Avvisati, G., Mercogliano, F., Vitale, A., Silvestri, M., Sessa, E. B., Belviso, P., Buongiorno, M. F., and Tizzani, P.: Multiplatform TIR remote sensing for monitoring and surveillance of the Campi Flegrei caldera., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13700, https://doi.org/10.5194/egusphere-egu26-13700, 2026.
A long, high-quality, and temporally continuous high spatiotemporal resolution air temperature (Ta) dataset plays a crucial role across various domains, particularly in areas such as human health, disease prediction and control, and energy utilization, where extreme temperatures (daily maximum and minimum temperatures) hold significant value. However, due to the instability of extreme temperatures influenced by various factors like topography, altitude, climate, and underlying surfaces, coupled with sparse meteorological station coverage, traditional methods struggle to accurately capture and produce high-quality, temporally continuous temperature dataset products. In this study, the four-dimensional spatiotemporal deep forest (4D-STDF) model was utilized, based on daily meteorological station temperature data from 2003 to 2022, along with seamless daily LST, meteorological, radiational, land use, topographic and population data encompassing 12 parameter factors and 6 spatiotemporal factors, three high-quality daily Ta datasets were constructed and generated. These datasets cover mainland China, featuring high spatial resolution (1km), long temporal sequences (2003-2022), and increased accuracy. The datasets include maximum (Tmax), minimum (Tmin), and mean (Tmean) temperatures from January 1, 2003, to December 31, 2022, as well as monthly and yearly synthesized Tmax, Tmin, and Tmean values, presented in GeoTIFF format with WGS84 projection, and the data unit is in 0.1 degrees Celsius (°C). The overall RMSE values are 1.49°C, 1.53°C, and 1.18°C for daily estimates, 1.38°C, 1.65°C, and 0.52°C for monthly, and 1.28°C, 1.83°C, and 0.41°C for annual, respectively. These datasets reasonably capture the spatial and temporal heterogeneity of Ta and effectively capture the intensity of heatwaves and cold spells. These new datasets are of significant value for studying extreme climates and contribute to assessing their impact on human health, infrastructure, and energy demands.
How to cite: Luan, Q.: Estimation of all-sky daily air temperature with high accuracy from multi-sourced data in China from 2003 to 2022, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4371, https://doi.org/10.5194/egusphere-egu26-4371, 2026.
Pre-eruptive, long-term, large-scale thermal anomalies detectable in 1 km resolution MODIS Thermal Infrared (TIR) radiance data have been consistently observed at long-dormant volcanoes years before eruptions. However, the physical mechanisms driving these signals remain unresolved. This study addresses a critical question: is the large-scale thermal anomaly primarily governed by localized high-temperature conduit heating or by spatially distributed, low-intensity heat release from diffuse magmatic degassing along volcanic flanks? Resolving this mechanism is vital for interpreting TIR data and for understanding heat and volatile transport during volcanic unrest.
We investigate this question at Augustine Volcano during its 2006 eruption, where summit conduit warming preceded the large-scale thermal anomaly by approximately three months. To explain this temporal offset, we adopt a conceptual model following Zhan et al. (2022), based on magma ascent followed by conduit sealing. We simulate surface thermal evolution under two scenarios: (1) an area-integrated signal including both the conduit and flanks, and (2) a conduit-excluded signal (near-vent area, ~150 m radius removed) dominated by flank degassing. The simulations show that including the conduit produces rapid warming synchronous with summit heating, whereas conduit-excluded simulations yield a delayed warming that reproduces both the timing and magnitude of the observed large-scale anomalies.
The strong agreement between conduit-excluded simulations and satellite observations provides robust evidence that the pre-eruptive thermal anomaly at Augustine was predominantly controlled by diffuse flank degassing rather than conduit heating. More broadly, our study establishes a physically-based framework for interpreting satellite thermal anomalies as indicators of evolving degassing pathways and subsurface permeability changes during prolonged volcanic unrest. This significantly enhances the utility of TIR monitoring for understanding volcanic heat transport processes and the state of unrest. Furthermore, we plan to apply this framework to a wide range of volcanoes to evaluate the generality of these findings.
How to cite: Chenyan, W. and Zhan, Y.: Diffuse Flank Degassing as the Dominant Source of the Large-Scale Thermal Anomaly Preceding the 2006 Augustine Eruption, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8833, https://doi.org/10.5194/egusphere-egu26-8833, 2026.
Land Surface Temperature (LST) is an important climate variable that helps us understand surface heat processes and environmental change. This study focuses on identifying scales at which LST can be reliably modeled using high-resolution RGB and near-infrared (NIR) data as the main input predictors. The approach is based on the well-known negative correlation between the Normalized Difference Vegetation Index (NDVI) and LST, while vegetation indices represent only one component of the surface energy balance. The study frames LST modeling as a data-driven emulation problem, where surface properties derived from RGB–NIR imagery are combined with concurrent atmospheric and environmental conditions. Several machine learning methods are tested, including Random Forest, XGBoost, LightGBM, and Convolutional Neural Networks, to build an LST emulation framework that links spectral surface information with observed thermal patterns under varying environmental conditions.
The study area is located in the Šumava Mountains in the Czech Republic, a mountain peatland with high ecological value and sensitivity to climate change. Data was collected using a UAV platform between 2025 and 2026, equipped with two sensors: an RGB–NIR camera for surface characterization and a thermal camera used as reference data for surface temperature. These paired multispectral and thermal UAV data form the training basis for the machine-learning models. To ensure the reliability of the models, UAV-derived LST was validated using multiple independent data sources, including in-situ Thermal Infrared (TIR) measurements, near-ground air temperature and humidity monitoring, or air temperature measurements from nearby weather stations.
In addition to spectral variables, the models include several environmental factors that influence surface temperature, such as solar angle, air humidity, soil moisture, wind speed, and canopy height, which act as physical controls on the modeled LST. A key goal of the study is to test the potential of transfer learning by training the models on data from the Šumava Mountains and evaluating their performance when applied to data from a different season, thereby assessing the temporal robustness of the emulation approach under changing atmospheric and surface conditions.
How to cite: Hordiienko, O. and Langhammer, J.: UAV-Based Modeling of Land Surface Temperature Using Machine Learning Methods, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14067, https://doi.org/10.5194/egusphere-egu26-14067, 2026.
In order to utilize satellite observations to address the climate change concerns, a concept of benchmark measurement is defined, and finally lead to the SI-Traceable Satellites (SITSat) missions. Traceability refers to the ability to track a measurement to a known standard unit (such as the Système Internationale (SI) standards) within a given measurement uncertainty. The SI-traceable observations can better withstand measurement-data gaps, and reduce uncertainties in long-term instrument calibration drifts while in orbit. Besides, The SITSat can serve as a space metrology lab to calibrate other space instruments and convert them into a climate benchmarking system with excellent global coverage. Now, there are several SITSat missions are under development by some space agencies, including the TRUTHS developed in ESA, and the CLARREO developed in NASA. In 2014, China Ministry of Science and Technology initiated and funded the Chinese Spaced-based Radiometric Benchmark (CSRB) project, with the ultimate goal of launching a flight unit of SITSat named LIBRA.
As a part of the LIBRA mission, an infrared sounder (LIBRA-IRS) based on a Michelson interferometer is designed to have a spectral range from 600-2700 cm-1, with a spectral sampling of 0.5 cm-1. To maintain the SI traceability of IR radiance, a high emissivity blackbody source is used as the onboard absolute calibration source, which uses multiple phase-change cells to provide an in-situ standard with absolute temperature accuracy.
In the other hand, achieving ultra-high accuracy of 0.1 K (k=3) also depends on a well-designed instrument (IRS) and an accurate absolute calibration model. In order to identify and evaluate the uncertainty contributions in calibrated radiance, and thereby improve the traditional calibration approach, an end-to-end instrument simulator is developed in conjunction with IRS instrument development and testing.
The simulator is a computer software written in MATLAB, and can be regarded as a numerical abstraction of the physical sounder. It takes atmospheric or calibration scene radiance as well as instrument parameters as inputs, then converts them into interferograms through Fourier transformation and adds errors and noise. Finally, it generates sampled interferograms through an analog-to-digital converter (ADC). The atmospheric radiance is calculated by the Line-By-Line Radiative Transfer Model (LBLRTM) with a spectral sampling less than 0.01 cm-1. As for the instrument model, it includes all FTS relevant optical, mechanical, electronic and thermal physics such as: optical transmittance, interferometer modulation, moving mirror speed fluctuations and time-dependent tilt, polarization of optics, background thermal flux, self-apodization due to the extension of field of view, optical and electronics noise, detector spectral responsivity and response non-linearity, sampling laser wavelength, electronic signal chain and ADC quantization, etc. Subsequently, the simulated interferogram data of atmospheric and calibration scenes are input into the radiometric calibration model to produce the calibrated radiance. This simulator is helpful for understanding the instrument, analyzing the system performance, improving the instrument design through end-to-end error analysis, and providing proxy data for calibration algorithms and software development.
How to cite: Lee, L., Ding, L., and Gu, M.: The Instrument Simulator for Infrared Sounder onboard Chinese SI-Tracable Satellite, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18480, https://doi.org/10.5194/egusphere-egu26-18480, 2026.
Urban heat islands and extreme heat events are intensifying due to climate change, especially in densely built environments. Remote sensing of land surface temperatures (LST) offers valuable insights for analyzing and mitigating urban heat risks. However, a major limitation of satellite-derived LST data is the trade-off between spatial and temporal resolution. High-resolution products such as those from Landsat provide fine spatial detail but suffer from low temporal coverage, limiting their usefulness for time-critical analyses.
In this study, multiple machine learning approaches are presented to reconstruct high-resolution urban LST data in sub-daily time steps by bridging temporal gaps using observations from the ECOSTRESS sensor on board the ISS. Using Madrid as a case study, random forest, gradient boosting, and artificial neural network models were trained on ECOSTRESS LST data together with a comprehensive set of explanatory variables, including local weather and radiation measurements, ERA5 reanalysis data, and Sentinel-2 surface reflectance indices.
Results show that the different model architectures exhibit varying strengths and weaknesses. The precision of the reconstructions varies with land use; urban areas tend to be reconstructed more accurately than non-built-up, sparsely vegetated areas. Comparing each model’s strengths and weaknesses highlights the potential use of data-driven methods to overcome observational limitations and generate continuous, high-resolution thermal datasets across the diurnal cycle.
By investigating the use of machine learning techniques for the reconstruction of Madrid’s land surface temperature, this work shows a potential pathway to overcome data gaps in high-resolution data on a broader scale. Therefore, it contributes a step toward continuous land surface temperature data, which may help improve the understanding of local heat waves and possible adaptation strategies.
How to cite: Richter, E. and Leuchner, M.: Reconstructing Urban Surface Temperatures: A Machine Learning Approach to Bridging Temporal Gaps in High-Resolution Data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20806, https://doi.org/10.5194/egusphere-egu26-20806, 2026.
Posters virtual: Wed, 6 May, 14:00–18:00 | vPoster spot 1b
EGU26-3363 | ECS | Posters virtual | VPS22
In-situ Thermal Infrared Monitoring in an Urban Area: A Case Study of Micro-scale Thermal Transitions during Hot Weather Conditions in Athens, Greece.Wed, 06 May, 14:21–14:24 (CEST) vPoster spot 1b
Characterizing the thermal state of urban surfaces is fundamental for mitigating the impacts of the Surface Urban Heat Island (SUHI) effect. This study presents an intensive in-situ thermal infrared monitoring campaign in the high-density urban core of Athens, Greece. Utilizing a calibrated handheld TIR sensor (7.5–14 μm), surface temperatures were recorded across strategic locations in the center of Athens during hot weather conditions. The methodology emphasizes the critical role of material-specific parameterization, where thermographic data were post-processed to account for emissivity (ε) variations and surface temperature, ensuring high-fidelity measurements.
Experimental results reveal extreme thermal stress, with maximum surface temperatures reaching 56.0°C on conventional paving materials, while the mean ambient air temperature was close to 35.0°C during peak solar hours (13:00–18:00LT). Spatial analysis and visualization of the results were performed using QGIS, correlating thermal signatures with urban geometry, shading conditions, and vegetation density. The aim of this study was to highlight the significant cooling potential of specific urban materials and nature-based solutions.
How to cite: Gkountaras, O., Georgakis, C., Velissaridis, T., and Assimakopoulos, M. N.: In-situ Thermal Infrared Monitoring in an Urban Area: A Case Study of Micro-scale Thermal Transitions during Hot Weather Conditions in Athens, Greece., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3363, https://doi.org/10.5194/egusphere-egu26-3363, 2026.
EGU26-13611 | ECS | Posters virtual | VPS22
Evaluating the combined potential of VSWIR and Thermal Infrared data for soil characterisation.Wed, 06 May, 14:24–14:27 (CEST) vPoster spot 1b
Quantifying soil properties such as Soil Organic Carbon (SOC), texture, and Calcium Carbonate (CaCO3) is essential for assessing soil health and ensuring food security. While Visible, Near Infrared, and Short Wave Infrared (VSWIR) remote sensing is a standard operational tool, the Longwave Infrared (LWIR, 8-14 μm) offer complementary information on mineralogy and moisture that are still not yet fully explored for this specific application. This study investigates the synergy between VSWIR and LWIR data that will be available with future hyperspectral satellite missions. Among them, the European Space Agency's Copernicus Expansion missions that will add to the EO capacity the Hyperspectral Imaging Mission for the Environment (CHIME) and Land Surface Temperature Monitoring (LSTM) mission. Alongside are the NASA's Surface Biology and Geology (SBG and SBG-TIR) missions.
The research focuses on Jolanda di Savoia (Italy), an agricultural landscape resulting from land reclamation projects in the late 19th century. Ground truth data were collected during a field campaign on June 22, 2023, providing 59 topsoil samples further analysed for SOC, texture, and CaCO3. Field campaign was coincident with an airborne survey carried out with the LWIR Hyperspectral Thermal Emission Spectrometer (HyTES) sensor. HyTES captured data across 256 spectral bands from 7.5 to 11.5 μm, providing a pixel size of approximately 2.3 meters.
To evaluate the multi-frequency potential, we developed a workflow combining a soil composite from PRISMA (VSWIR) satellite time-series with simulated SBG-TIR (LWIR) data. The SBG-TIR simulation chain included as input a surface emissivity map derived from the airborne HyTES survey. To cover the LWIR wide spectral range (up to 12 µm), the emissivity spectrum was extended using an autoencoder neural network procedure trained on the ECOSTRESS Soil Spectral Library. Top-Of-Atmosphere (TOA) radiance was then simulated using the Radiative Transfer for the TIROS Operational Vertical Sounder (RTTOV-14) model, incorporating the optical depth and cloud/aerosol optical properties coefficients specific to SBG-TIR. Furthermore, these simulated data were atmospherically corrected to produce the target satellite emissivity products according to the TES algorithm.
Soil properties prediction models were developed using supervised machine learning algorithms. We benchmarked two scenarios: 1) the proposed combined approach using PRISMA and the simulated SBG-TIR L2 emissivity product; and 2) a VSWIR-only approach using PRISMA. A quantitative assessment by 10-fold cross-validation using common literature metrics (R², RMSE, RPD) highlighted the benefits of the multi-sensor approach. For SOC retrieval, the standalone VSWIR (PRISMA) model yielded an R2 of 0.55 (RPD = 1.5), while the synergistic integration of PRISMA with simulated SBG-TIR data improved the retrieval accuracy, reaching an R2 of 0.65 and increasing the RPD to 1.69. This work indicates that, on the agricultural test site of Jolanda di Savoia, the combined use of SVWIR and LWIR spectral range slightly improves the SOC retrieval. Further validation across diverse agricultural scenarios will be essential to test the real advantage of combining next-generation imaging spectroscopy missions.
How to cite: Rossi, F., Casa, R., Marrone, L., Mirzaei, S., Pascucci, S., and Pignatti, S.: Evaluating the combined potential of VSWIR and Thermal Infrared data for soil characterisation., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13611, https://doi.org/10.5194/egusphere-egu26-13611, 2026.
