Volcanic heat flux offers a direct window into subsurface magmatic processes and eruption dynamics, yet its quantification from space remains incomplete. Current satellite-based assessments are largely restricted to high-temperature eruptive activity, resulting in the systematic omission of moderate- and low-temperature sources. We integrate Mid-InfraRed (MIR; 3.5-4.5 μm) and Thermal-InfraRed (TIR; 10-12 μm) satellite observations into a unified analytical framework capable of resolving the full range of volcanic thermal emissions.
We introduce the Total Volcanic Radiative Power (VRPTot), defined as the combined contribution of MIR- and TIR-derived radiative power (VRPMIR + VRPTIR). This approach yields temperature-robust radiative power estimates (within ±20%) over 273-1500 K interval, whereas single-band methods exhibit systematic errors exceeding 90% when applied beyond their operational temperature thresholds. To further characterise thermal behaviour, we define VRPRatio, a dimensionless indicator of volcanic thermal structure that effectively distinguishes hydrothermal, dome-forming, open-vent, and effusive regimes within a common parameter space.
Application of this framework to representative volcanoes demonstrates that inventories relying solely on MIR observations underestimate total thermal output by factors of 2-20 for moderate-temperature systems, indicating that global volcanic heat fluxes may be substantially higher than previously recognised. At Sabancaya volcano, temporal variations in VRPRatio reveal changes in thermal structure several months prior to the November 2016 eruption, signals that are undetectable using single-wavelength approaches.
This transferable methodology enables more accurate assessments of global volcanic heat budgets and enhances the early identification of eruptive transitions, representing a significant advance in satellite-based volcano monitoring. Furthermore, these results resolve long-standing biases in volcanic heat-flux inventories, enhance real-time monitoring capabilities, and have broad implications for volcanology, climatology, and planetary science.
How to cite: Aveni, S., Laiolo, M., and Coppola, D.: A Synergistic Thermal Framework to Classify, Quantify, and Monitor Volcanoes from Space, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13139, https://doi.org/10.5194/egusphere-egu26-13139, 2026.
Over the last 25 years, five volcanoes have erupted on mainland Ecuador, generating eruptive columns, pyroclastic density currents, lava flows, etc. Currently, El Reventador and Sangay are erupting and are being monitored by the Instituto Geofísico of the Escuela Politécnica Nacional (IGEPN) using different techniques, including thermal surveillance. The volcanic products emitted by these volcanoes are identified through thermal and visual image analysis. The timely identification of these products can greatly influence decision-making by authorities and the response of vulnerable populations.
This study presents a novel approach to the automated classification of volcanic states using thermal imagery from multiple Ecuadorian volcanoes, acquired by the IGEPN. We developed a Multi-Branch Convolutional Neural Network architecture that processes three-dimensional tensor representations of thermal data to distinguish between clear conditions, cloudy conditions, emission events, and lava flow events. The system processes raw FLIR camera images (.fff format) through a pipeline that includes metadata extraction, thermal analysis, and classification. Our architecture utilizes three parallel branches processing base thermal information, edge detection features, and volcano-specific thermal thresholds simultaneously.
The model was trained and validated on a dataset of more than 10,000 thermal images from two active Ecuadorian volcanoes: Cotopaxi (7,024 images) and Reventador (3,536 images). The dataset encompasses four volcanic states: cloudy conditions, emission events, clear conditions, and lava flow events. Our multi-volcano approach incorporates volcano-specific thermal threshold parameters, recognizing the distinct thermal characteristics of different volcanic systems. The model achieved robust performance with 94.74% validation accuracy and 94.58% training accuracy across all volcanic states and locations. Per-class validation performance demonstrates excellent discrimination capability: <95% for clear conditions, <96% for cloudy conditions, <94% for emission events, and <90% for lava flow events. The confusion matrix reveals minimal inter-class confusion, indicating the model's ability to distinguish between complex volcanic phenomena. This approach addresses key challenges in manual analysis of thermal imagery while providing a scalable framework that can be adapted to different volcanic systems and integrated into existing monitoring networks.
How to cite: Vallejo, S., Mosquera, D., Gallegos, F., Merino, P., Naranjo, F., and Pino, G.: Multi-Branch Convolutional Neural Networks for Volcanic Activity Classification Using Thermal Imagery (Cotopaxi and El Reventador volcanoes), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22137, https://doi.org/10.5194/egusphere-egu26-22137, 2026.
Recent advances in satellite remote sensing, particularly high-resolution and high-temporal-frequency SAR systems, provide new opportunities for capturing rapidly evolving deformation. X-band InSAR (Interferometric Synthetic Aperture Radar) data from the COSMO-SkyMed (CSK) and COSMO-SkyMed Second Generation (CSG) constellations offer dense temporal sampling and high spatial resolution, making them particularly valuable for monitoring complex, rapidly evolving deformation signals. However, the short wavelength of X-band data can make phase unwrapping – the step required to convert wrapped interferometric phase into continuous surface displacement – challenging when the signal has a large footprint, large deformation gradients and surface discontinuities.
Here we present an enhanced X-band InSAR monitoring framework applied to the 2024-2025 Fentale-Dofen dyke intrusion in Ethiopia. The dyke measured ~50 km in length and produced complex surface deformation spanning ~10,500 km², with InSAR line-of-sight displacements up to ~3 m over ~60 days. Monitoring dyke intrusion-related deformation is important for understanding magma movement, assessing volcanic hazards, and supporting rapid response during period of unrest. We address limitations of conventional phase unwrapping in areas of complex deformation, including dense fringes caused by dyke-opening and discontinuous deformation within the graben. By integrating pixel-offset tracking with interferometric phase, we develop a reliable offset-supported unwrapping strategy that allows robust recovery of surface displacement associated with both dyke opening and graben subsidence, with consistency evaluated by loop-closure tests. The resulting deformation products provide a consistent basis for InSAR time-series analysis using dense CSK observations, allowing the temporal evolution of intrusion-related deformation to be resolved at high spatial and temporal resolution. Ongoing work extends this framework toward integrated deformation modeling, combining geodetic observations with physics-based representations of dyke-driven magma transport to better constrain subsurface processes.
This study demonstrates how advanced InSAR processing strategies and multi-technique data integration can unlock the full potential of high-resolution X-band SAR data for environmental hazard monitoring. The proposed framework contributes to the development of robust remote sensing tools for deformation analysis, supporting both near-real-time monitoring and post-event assessment of volcanic and other hazards.
How to cite: Zheng, W., Biggs, J., Way, L., Lazecky, M., and Grandin, R.: Towards reliable X-band InSAR monitoring of complex deformation: Insights from the 2024-2025 Fentale-Dofen Magma Intrusion, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7807, https://doi.org/10.5194/egusphere-egu26-7807, 2026.
Remote sensing from ground, UAV, aircraft and satellite platforms is increasingly central to monitoring Earth’s surface and supporting decision making. However, robust interpretation of multispectral/hyperspectral observations still depends on consistent links between satellite products and high-quality reference spectra measured in the field and laboratory, plus workflows that make these data interoperable across sensors, spatial scales and acquisition conditions. We present the INGV Spectral Library, a web-based, GIS-integrated platform designed to operationally connect in situ spectroscopy with airborne/satellite imaging spectroscopy, enabling reproducible pre-processing, cross-sensor harmonization, and geospatial querying of spectral datasets for environmental monitoring applications.
The platform provides standardized spectral analytics commonly required in monitoring pipelines: continuum removal and absorption-feature characterization, derivative-based enhancement to emphasize diagnostic features, and sensor-aware resampling using Spectral Response Functions (SRFs) to harmonize high-resolution field spectra to specific sensors (e.g., Sentinel-2 and spaceborne hyperspectral missions). This “sensor-to-field” alignment enables direct comparability and supports spectral–spatial data fusion, where field-based endmembers and satellite reflectance/emissivity products can be jointly analysed. A key component is the GIS interface: spectra are linked to georeferenced samples and metadata and can be filtered by location, lithology/land cover context, acquisition conditions and spectral criteria, facilitating rapid exploration of spatial patterns and targeted selection of reference signatures for mapping and validation tasks.
Two use cases illustrate the relevance to environmental monitoring and hazard-related contexts. (i) In Sardinia (Sale ’e Porcus), curated VNIR–SWIR FieldSpec measurements are ingested as a controlled reference set to support multi-sensor consistency checks and calibration/validation activities for satellite imaging spectroscopy. (ii) In Oman, PRISMA Level-2D surface reflectance is analysed through spectral indices and Spectral Angle Mapper (SAM), using PRISMA-resampled endmembers derived from reference spectra to delineate spatially coherent alteration patterns and potential copper-related signals; the resulting maps support field planning and prioritization of sampling targets, with new samples intended to validate and refine satellite-based interpretations.
By combining standardized spectral pre-processing, SRF-based cross-sensor harmonization, and GIS-driven access to reference spectra, the INGV Spectral Library provides a practical platform for multi-scale environmental remote sensing, enabling more transparent, transferable and decision-oriented workflows for monitoring surface changes and hazard-relevant processes.
This study is carried out within two projects: the Space It Up project funded by the Italian Space Agency, ASI, and the Ministry of University and Research, MUR, under contract n. 2024-5-E.0 - CUP n. I53D24000060005 and PRIN2022_SH6_2022BTKA9Y-02 funded by Ministry of University and Research, MUR CUP D53D23000580006.
How to cite: Solinas, M., Musacchio, M., Silvestri, M., Buongiorno, M. F., Falcone, S., Melis, M. T., Casu, M., and Noli, S.: A GIS-enabled spectral library to disseminate field data for surface spectroscopy, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6614, https://doi.org/10.5194/egusphere-egu26-6614, 2026.
The Urban Heat Island (UHI) phenomenon is a critical concern, particularly in the context of global warming and rapid urbanization. UHIs are essentially urbanized areas that exhibit higher temperatures compared to their less or non-urbanized surroundings. This heat island effect is worsened by urbanization, largely due to the extensive use of asphalt and other impervious surfaces over green spaces, coupled with various human activities. The environmental conditions created by UHIs negatively impact the quality of life. These areas suffer from elevated temperatures, higher concentrations of pollutants, and a subsequent increase in the energy and economic costs associated with cooling buildings. Numerous studies have been carried out to tackle the growing issue of the UHI. These efforts concentrate on analyzing UHI features to equip environmental planners and decision-makers with vital instruments for mitigation and management. This work investigates the UHI phenomenon in the Catania area (Sicily, Italy), focusing on a specific urban section to highlight the contrast between densely built and greener spaces. The study employs remote sensing data from Landsat 8 and 9 satellite missions to calculate relevant indices, such as the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST), which are essential for UHI analysis. After generating a thematic map of UHIs for the area, the Land Use Land Cover (LULC) was analyzed. This LULC analysis facilitated the use of the QGIS MOLUSCE plug-in, a tool offering several algorithms for predictive LULC modeling. The available algorithms include neural networks (multilayer perceptron), logistic regression, weights of evidence, multi-criteria evaluation, and validation via kappa statistics. The model's results were validated by projecting them onto a year for which actual data was already available. Predictive LULC modeling enables the evaluation of UHI conditions at the time of the projection. This capability makes the tool valuable for environmental planners and decision-makers, aiding in the assessment of future urbanization impacts and their subsequent effects on the population's quality of life.
How to cite: Cavallaro, L., Mangiameli, M., and Mussumeci, G.: Predictive analysis of Urban Heat Islands using satellite data and neural network algorithms , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1312, https://doi.org/10.5194/egusphere-egu26-1312, 2026.
During natural hazards and other rapidly evolving crisis situations, the accessibility of evacuation routes and the delivery of emergency supplies strongly depends on road surface type. However, in many regions affected by environmental changes, conflicts, or population displacement, reliable information on road surface conditions is incomplete, outdated, or entirely unavailable, which limits effective disaster response and environmental monitoring. This study presents a satellite-based framework that classifies roads as either paved or unpaved using multispectral Sentinel-2 imagery and volunteered geographic information (VGI) from OpenStreetMap (OSM).
OSM road geometries are used to extract spectral samples from Sentinel-2 surface reflectance data, which is used to train a convolutional neural network (CNN) for road surface classification across diverse environmental settings. To improve spatial consistency and practical usability, classification results are aggregated at the road-segment level to produce coherent surface classifications aligned with real-world road infrastructure. The framework is designed to be transferable and applicable across regions with varying climates, land-cover characteristics, and degrees of urbanisation.
The approach has been evaluated across multiple target regions and demonstrates consistent performance beyond the training domain, which highlights its potential for cross-regional application. Due to the regular revisit time of Sentinel-2, the framework further supports multi-temporal analysis. This makes it possible to assess changes to the road surface before and after dynamic events, such as flood-induced degradation, sediment coverage or long-term urbanization. By combining freely available satellite data and open VGI, the proposed method provides a scalable tool for infrastructure monitoring, disaster response, and environmental assessment in data-scarce and rapidly changing regions.
How to cite: Halbgewachs, M., Wieland, M., Schneibel, A., Geiß, C., and Gähler, M.: Multi-Temporal Road Surface Classification from Sentinel-2 and OpenStreetMap Data Using Deep Learning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7537, https://doi.org/10.5194/egusphere-egu26-7537, 2026.
Monitoring environmental changes over time requires image time series with extensive historical depth. However, high spatial resolution images often lack such depth. Additionally, some remote areas may suffer from either insufficient satellite coverage or a lack of high-resolution or high-quality imagery. This study aims to investigate the impact of spatial resolution on image classification. Therefore, Landsat 8 and Sentinel-2 images from October to December 2020 were processed and classified using Random Forest regression in Google Earth Engine (GEE). Training samples were collected from Collect Earth Online (CEO) to train the model. In addition to the spectral bands available, vegetation indices were considered to optimize classification results. The study revealed differences in land cover areas estimated by the two sensors. These differences are statistically significant at p < 0.001, although they are small. The validation results showed that the RMSE from Sentinel-2 is slightly lower than that from Landsat 8. Although small, this difference is significant at p < 0.05. This highlights two key points: (i) that spatial resolution positively influences the accuracy of image classification, especially when dealing with Landsat 8 and Sentinel-2 imagery; and (ii) that the difference between Landsat 8 and Sentinel-2 sensors is not too substantial in the context of a fragmented landscape, since it ranged from 0.03% to 3.94% across land covers. Therefore, Landsat imagery and, by extension, medium-resolution satellite imagery can still yield satisfactory land cover maps, especially in a patchy landscape such as the southeastern part of Niger.
Keywords: Stratified random sampling; Google Earth Engine (GEE); Random Forest; Collect Earth Online (CEO); Niger
How to cite: Abdou Amadou, S., Lawali, D., Bastin, J.-F., Bogaert, J., Michez, A., and Meersmans, J.: Effect of Spatial Resolution on Land Cover Mapping in an Agropastoral Area of Niger (Aguie and Mayahi) Using Sentinel-2 and Landsat 8 Imagery within a Random Forest Regression Framework, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10620, https://doi.org/10.5194/egusphere-egu26-10620, 2026.
Historical monitoring of land-cover and land-use change provides a means to quantify anthropogenic and atmospheric processes (e.g., floods and droughts) that affect lagoon systems. The Chaschoc-Sejá lagoon system (SLCh-S), located in Tabasco, Mexico, is a natural complex dominated by interconnected lagoons and noted for its high biodiversity, including endemic species. The SLCh-S exhibits strong seasonal dynamics. During the rainy season, it behaves as an interconnected network of water bodies linked by meandering tributaries; extensive flooding occurs, vegetation cover declines, and water bodies display striking color variability. In contrast, during the dry season, interconnections disappear, sediments become exposed, and wet soils and flood-tolerant vegetation emerge along lagoon margins.
Although the SLCh-S is undergoing anthropogenic and environmental pressures, the magnitude of these impacts at the regional scale remains poorly understood. Land-use maps derived from remote sensing offer a key first step for large-scale monitoring. However, multiple mapping methods are available, and their performance depends strongly on the characteristics of each study area; therefore, testing is required to identify the most suitable approach.
The objective of this study is to evaluate and compare supervised classifiers applied to high-resolution (3 m) satellite imagery to determine which performs best in the region. Two PlanetScope images were analyzed, one from the dry season (March 2024) and one from the rainy season (September 2024). We implemented the traditional Maximum Likelihood Classification (MLC) method and three machine-learning classifiers: Random Forest (RF), Support Vector Machine (SVM), and Random Trees (RT). Classification accuracy was assessed using the Kappa index.
Kappa scores were 0.82 for MLC, 0.77 for RF, 0.68 for SVM, and 0.70 for RT. Results indicate that in flat terrain with homogeneous vegetation, agricultural areas, and well-defined water bodies, MLC can effectively classify land use and vegetation, outperforming the tested machine-learning algorithms. Nevertheless, all methods showed limitations in discriminating vegetation with high intra-class spectral variability. The moderate accuracies also highlight the need for post-classification refinement to improve final maps, a step that can be labor-intensive in high temporal-resolution monitoring. Integrating derived variables (e.g., NDVI/NDWI, texture) and complementing accuracy assessment with per-class ROC/AUC metrics (one-vs-rest) is recommended to better characterize class separability.
Overall, the study clarifies the strengths and limitations of common classifiers for high-resolution monitoring of tropical wetlands.
How to cite: Nieto, J., Ramírez Serrato, N. L., García Cruzado, S. A., Hernández Hernández, M. A., Peralta Carreta, C., Herrera Zamarrón, G., Olea Olea, S., Yépez Rincón, F. D., Cortés, A., and Hernández García, G.: Land-Use Classification in a Tropical Wetland: A Comparison of MLC and Machine-Learning Algorithms, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13192, https://doi.org/10.5194/egusphere-egu26-13192, 2026.
The coastal zone is one of the most dynamic and high energy systems on Earth, where wind, waves and tides cause geophysical processes such as erosion, deposition and flooding to occur. Monitoring shoreline position is crucial to manage and protect coastal region to safeguard economic and social interest as many people rely on coastal areas for tourism and their livelihoods. Satellite-derived shorelines change rate carried out the significant erosion and accretion along the beaches for assessing the long-term sustainability of coastal development and effective spatial planning. Earth observation (EO) utilisation has been provided moderate spatial resolution (10-30 m) for investigating shoreline movement at regional to global scales. In this study, we delineate the shorelines over three years period of 2022 to 2024 using two distinctive methods – coastsat environment and remote sensing techniques by applying Normalised Difference Water Index (NDWI) algorithm with Sentinel-2 satellite imagery data. Then, we assess the change rates of coastal erosion and accretion using Digital Shoreline Analysis System (DSAS) tool. The significance of shoreline extractions using coastsat environment represent the accretion patterns along the beaches with the average of change rates accounted for 2.54 m/year and 2.65 m/year for Linear Regression Rate (LRR) and End Point Rate (EPR) method, respectively. Meanwhile, the extracted shorelines using NDWI algorithm show the shoreline change rates of -0.87 m/year (LRR) and -0.46 m/year (EPR), which these rates are categorised as a coastal erosion. Moreover, the shoreline distance change rates are also different with the value of 5.27 m (coastsat) and -1.11 m (remote sensing technique), those values were calculated using Net Shoreline Movement (NSM) statistic method. This difference results in shoreline change position might be caused by different process, whereas extracting the shoreline using coastsat that implement the tidal correction and NDWI was applied without tide correction. The other limitation of optical-satellite imagery is the cloud cover can affect the shoreline results followed by its change rates.
How to cite: Pribadi, C., Briganti, R., and Psimoulis, P.: Comparison Assessment of Satellite-derived Shorelines using Coastsat Environment and Remote Sensing Technique, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15320, https://doi.org/10.5194/egusphere-egu26-15320, 2026.
Remotely sensed time-series data provide a powerful tool for environmental monitoring, particularly for assessing heterogeneous spatio-temporal vegetation dynamics in mining environments. Here, we present a new approach, SHABA (Seasonal Harmonic Anomaly Break Analysis), for remotely monitoring the effects of mines on vegetation. SHABA combines Seasonal and Trend decomposition (LOESS), Fast Fourier Transform-based seasonality (e.g., HANTS) and heuristic-based breakpoint detection to identify rapid and long-term vegetation changes. This allows us to quantify browning and greening intensity as deviations from local year-specific periodic and trend behaviour, and identify abrupt but potentially subtle changes (breakpoints). We apply this approach to MODIS EVI data from six mining sites (Aitik, Roșia Poieni, Trident, Lumwana, Carajás, and Vametco). Our results show spatially explicit, significant negative change magnitudes within primary mine footprints, reflecting vegetation loss driven by distinct phases of clearing for infrastructure expansion. Beyond operational boundaries (secondary footprints), change magnitudes are more subtle and exhibit heterogeneous greening–browning patterns, arising from either or a combination of direct mining effects and indirect land-use pressures associated with mine site establishment. SHABA workflow is transferable and can be applied globally to different mines to detect vegetation changes and, when interpreted, supports environmental reporting, impact assessment, and post-mining remediation.
How to cite: Nwazelibe, V., Kirsch, M., Thiele, S., Djeddaoui, F., Yu, W., Gloaguen, R., and Tolosana-Delgado, R.: Harmonic decomposition of vegetation indices time series for assessing mining impacts, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17640, https://doi.org/10.5194/egusphere-egu26-17640, 2026.
Soil contamination monitoring in industrialized regions requires accurate, spatially continuous assessments. We present an integrated remote sensing framework for predicting concentration of soil Potentially Toxic Elements (Cd, Be, V, Cr, As, Co), which were selected based on their significant correlations with hyperspectral and multispectral signatures observed in preliminary exploratory chemical analysis. The framework integrates PRISMA hyperspectral, Sentinel-2 multispectral, and DEM-derived topographic data, tested near the industrial area of Taranto (southern Italy), a priority site for environmental risk assessment.
Our methodology integrates heterogeneous satellite data through systematic preprocessing, spectral index computation, morphometric feature extraction, and spatially feature selection. A correlation-based selection algorithm with a spectral distance constraint (∆λ<30 nm) was specifically implemented to mitigate multicollinearity inherent in high-dimensional hyperspectral data, ensuring the selection of non-redundant predictors. Machine learning regression models were trained on laboratory measured soil samples and validated via stratified cross-validation and independent holdout data.
Results demonstrate differential model performance across PTEs: R² = 0.75–0.82 (training) and 0.58–0.68 (validation). Feature importance analysis revealed complementary contributions from hyperspectral bands, multispectral indices, and terrain morphology, with hyperspectral data providing the strongest discriminative power. Single-sensor approaches (Sentinel-2 only) yielded notably lower performance, confirming the value of data integration. High-resolution maps identified the most polluted areas, validating the framework's capability for spatial assessment of soil contamination hotspots.
How to cite: Binetti, M. S., Massarelli, C., Solórzano Villegas, J. V., Mas, J. F., Barca, E., and Uricchio, V. F.: Data-driven environmental monitoring of soil potentially toxic elements using multisource remote sensing and Machine Learning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3533, https://doi.org/10.5194/egusphere-egu26-3533, 2026.
Persian or English Walnut (Juglans regia L.) growing in Hungary faces significant challenges from complex biotic pathogens and abiotic climate stressors. While farmers possess the expertise to identify these pathologies, early diagnosis is often impeded by the physical inaccessibility of the upper canopy, where symptoms frequently manifest first. To overcome these limitations, this study proposes a multi-sensor Unmanned Aerial System (UAS) framework capable of acquiring high-resolution geospatial data to identify physiological features invisible to the human eye.
The research was conducted at two distinct sites in Central Hungary, representing contrasting management regimes. The first is a 2.4-hectare intensive commercial orchard utilizing rigorous irrigation and chemical protection. The second is a 4.2-hectare genetic archive owned by the HUALS Fruit Growing Research Center; this site contains diverse cultivars with varying management levels (including untreated controls), offering a higher probability of observing heterogeneous disease responses.
Data acquisition utilized a DJI Matrice M210 equipped with a 10-band MicaSense RedEdge-MX Dual system and a DJI Matrice M350 RTK with a Zenmuse L2 LiDAR sensor. To assess the impact of spatial resolution on disease identification accuracy, multispectral surveys were conducted at altitudes of 40, 57, and 72 m AGL, resulting in GSDs of 3, 4, and 5 cm/pixel, respectively. Surveys were conducted in June 2025 to establish baseline pre-symptomatic conditions and repeated in September 2025 during the pre-harvest period, when symptoms were clearly visible. LiDAR data was collected once to characterize stable structural parameters, such as tree height and crown complexity.
For tree-level analysis, precise individual tree crown delineation is essential. While point cloud-based segmentation was evaluated, a more robust delineation was achieved by integrating Deep Learning algorithms applied to RGB orthophotos. The 10-band spectral data facilitated the calculation of sensitive narrow-band indices (e.g., PRI, NDRE, Cl_RE) to detect changes in pigmentation and photosynthetic efficiency. Finally, the study applies multivariate statistical analysis to cluster trees by fusing 3D structural metrics derived from LiDAR with spectral indices. This approach aims to model species-specific stress responses and categorize cultivars based on their physiological and structural characteristics, providing a foundation for improved precision agriculture workflows.
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Issa Loghmanieh is funded by the Stipendium Hungaricum scholarship under the joint executive program between Hungary and Iran.
How to cite: Loghmanieh, I., Hamdan, A., Bujdosó, G., Vahdati, K., and Bertalan, L.: High-resolution multi-sensor UAS framework for individual tree health monitoring and structural analysis in walnut orchards, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7030, https://doi.org/10.5194/egusphere-egu26-7030, 2026.
Spatial sampling design is essential for accurately assessing land use and land cover (LULC) classification results from remote sensing data. When classification correctness exhibits spatial heterogeneity, spatial stratification can significantly improve spatial sampling efficiency by dividing the study area into heterogeneous strata. Three spatial stratification methods were introduced, respectively focusing on LULC types, the integration of multi-source classification products with different spatial resolutions, and pixel-level uncertainty analysis.
First, stratification by LULC types was employed because these categories directly relate to variations in classification accuracy. Second, although LULC products from different sources and resolutions were generated using diverse data and methods, their consistency and inconsistency could indicate potential misclassification. Thus, a stratification method that combined such multi-source products was developed for guiding accuracy assessment sampling. Third, a pixel-based stratification framework was proposed based on uncertainty indices, namely the maximum probability, fuzzy confusion index, and probability entropy.
The effectiveness of these methods was tested through a case study of LULC classification in Beijing, China. Results showed that the proposed stratification approaches could effectively distinguish spatial characteristics and improve sample representativeness, thereby optimizing the sampling for classification accuracy evaluation and enhancing its overall reliability.
How to cite: Dong, S., Liu, Y., Gao, Y., and Zhou, Y.: Spatial stratification method for the sampling design of remote sensing classification accuracy assessment, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9147, https://doi.org/10.5194/egusphere-egu26-9147, 2026.
Earth Observation data has become an increasingly indispensable resource in the field of volcanology, providing unprecedented capabilities for the high-resolution assessment of the timing, magnitude, and explosivity of active eruptive events. This work leverages a multi-sensor suite of satellite-derived products to meticulously document the February 2025 eruption of Mount Etna, Italy. This specific event holds particular significance as it represents the first major eruption fully monitored with the operational third-generation Meteosat satellite (Meteosat Third Generation - Imager, MTG-I), which offers a revolutionary advancement in mid-infrared spatial and temporal resolution for thermal monitoring.
Daily SkySat/PlanetScope imagery monitored effusive activity and lava flow dynamics, providing high-cadence data on flow evolution and areal expansion, yielding critical insights into flow propagation rates and the spatial distribution of the effusive material. Magma supply rates and thermal output were assessed by tracking eruption-related thermal anomalies using multi-sensor data (MODIS, SEVIRI, VIIRS, FCI aboard MTG-I), enabling the calculation of the volume of extruded magma per unit time. Eruptive plumes and volcanic gas monitoring, including TROPOMI SO₂ total mass estimates, analyzed the explosive component and atmospheric impact of the eruption. Finally, high-resolution Pléiades imagery acquired rapidly post-eruption allowed for generating an updated Digital Surface Model (DSM). DSM differencing with a pre-eruptive reference precisely estimated deposit thickness and total erupted volume.
This interdisciplinary work provides essential information for analyzing multi-temporal morphological changes and conducting comprehensive hazard assessment studies, thereby contributing significantly to efforts aimed at mitigating the impact of environmental hazards.
How to cite: Cappello, A., Ganci, G., Bilotta, G., Dozzo, M., Spina, F., Zuccarello, F., Cristofaro, R., and Spina, M.: Advancements in volcanological Earth observation: Documenting the February 2025 eruption of Mount Etna, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10170, https://doi.org/10.5194/egusphere-egu26-10170, 2026.
Large-scale and long-term satellite observations are essential for environmental monitoring and for detecting gradual ecosystem responses to climate variability and land-use change.
This study presents a remote sensing–based framework to characterize vegetation phenology and its stability across Europe over four decades (1982–2022), using the temporally consistent and cross-sensor-calibrated PKU GIMMS NDVI dataset. The framework integrates NDVI time-series analysis with a newly developed Phenology Variability Index (PVI), designed to assess phenological stability at climatic scales and to complement established methods. Monthly NDVI time series are analyzed using non-parametric statistical tests and long-term mean seasonal profiles to delineate phenologically coherent regions through spatial clustering. Land Surface Phenology (LSP) metrics and the Phenology Variability Index are subsequently derived to characterize seasonal timing, trends, and phenological stability within and across regions. In this way, we integrate spatially explicit, pixel-level NDVI statistics and PVI-based evaluations with analyses of phenologically homogeneous clusters, providing a comprehensive understanding of vegetation dynamics across ecosystems.
Five spatially coherent clusters were identified, each characterized by distinct seasonal signatures linked to major European eco-climatic zones. Results reveal pronounced spatial and temporal heterogeneity, with consistent greening trends in temperate, montane, and Mediterranean regions, weaker and seasonally constrained greening in semi-arid areas, and largely stable winter NDVI conditions in mountainous forests and continental regions. LSP metrics indicate shifts in the timing and duration of the growing season, reflecting combined effects of climate variability and land-use change. The PVI further highlights higher phenological stability in Mediterranean and semi-arid landscapes, contrasted with greater variability in temperate and montane ecosystems.
Overall, this study demonstrates how long-term, high-temporal-resolution satellite data can support ecosystem assessment and environmental monitoring across continental scales. The proposed framework provides a transferable and robust methodological basis for analyzing vegetation dynamics, contributing to remote sensing–driven environmental monitoring and climate change research.
Keywords:
Remote sensing; Environmental monitoring; Vegetation dynamics; NDVI time series; Europe; Phenology Variability Index (PVI); Monthly trend analysis; Land Surface Phenology.
How to cite: Samela, C., Imbrenda, V., Coluzzi, R., and Lanfredi, M.: Monitoring Long-term Vegetation Phenology across Europe Using Satellite NDVI Time Series (PKU GIMMS), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12633, https://doi.org/10.5194/egusphere-egu26-12633, 2026.
One of the main carbon fluxes characterizing terrestrial ecosystems and biodiversity is gross primary production (GPP), defined as the amount of carbon fixed by vegetation through photosynthesis, per unit area and unit time. GPP represents the potential carbon uptake of an ecosystem to produce food, wood, and fiber. Therefore, understanding its spatiotemporal variability under future climate change scenarios is essential for environmental management and global sustainable development. The temporal variability can be characterized by analyzing GPP time series, which exhibit non-stationary behavior driven by short-term, seasonal, and long-term variations.
In the last decade, significant advancements have been achieved in the development and production of operational long-term GPP series using Earth Observation (EO)-based data at regional and global scale. This is the case of the 10-day GPP product at 3.1 km (MGPP LSA-411) from geostationary SEVIRI/MSG data within the LSA SAF (Land Surface Analysis SAF) as part of the SAF (Satellite Application Facility) network of EUMETSAT. This product is freely available in the LSA SAF platform since 2018 for addressing near-real-time users’ climate and environmental applications. Currently, the possibility of improving this product using a new version of fAPAR, now under development, is being analyzed. This work aims to provide a 20-year assessment (2004–2023) of terrestrial ecosystem status based on the spatiotemporal analysis of 10-day GPP time series derived from MSG data, following the methodology of the operational MGPP product (LSA-411) but using a novel fAPAR as input based on a deep-learning approach.
In a first stage, the GPP time series is derived by computing daily GPP based on Monteith’s radiation use efficiency concept, which accounts for water stress effects to downregulate the maximum light use efficiency (optimal conditions). A suite of MSG products is used, including the daily downwelling shortwave radiation flux (DIDSSF, LSA-203), daily actual evapotranspiration (LSA-351 and LSA-312.3), and reference evapotranspiration (DMETREF, LSA-303). An evaluation of the derived 10-day GPP time series is performed at local scale using ground-based GPP estimates at 8 eddy covariance (EC) towers from the FLUXNET database. The assessment also includes the comparison with other operational EO-based products, such as the 8-day MODIS, 20-day GDMP and daily SMAP at the same EC towers. The results show high correlations (r > 0.70), between the MGPP and EC estimates, which are very similar to those obtained using MODIS, GDMP and SMAP products.
In a second stage, ecosystem monitoring is performed using the multi-resolution analysis (MRA) based on the wavelet transform (WT). MRA-WT provides a temporal decomposition of the original time series, allowing different signal component to be derived by removing the contribution of specific temporal scales. This approach has been extensively used over the past few decades across several applications. The results show a general greening in the central and eastern Sahel region, eastern Africa (Horn of Africa), eastern Spain and Turkey, which is associated with an increase in precipitation along the period. In contrast, localized negative changes are observed in the Senegal region and southern parts of Africa, mainly attributed to precipitation variability during the same period.
How to cite: Martinez, B., Gilabert, M. A., Sánchez-Ruiz, S., Campos-Taberner, M., Jiménez-Guisado, A., and García-Haro, F. J.: GPP from two decades of MSG data for terrestrial ecosystem monitoring over Europe and Africa, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12873, https://doi.org/10.5194/egusphere-egu26-12873, 2026.
How to cite: Furcic, N., Déchamps, S., Salameh, E., Bergsma, E., Frappart, F., and Laignel, B.: Comparing Waterline and Water occurrence approaches for satellite‑derived intertidal topography , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17478, https://doi.org/10.5194/egusphere-egu26-17478, 2026.
Monitoring cocoa farming within complex tropical agroforestry systems remains a significant challenge for Earth Observation, particularly given the EU Deforestation Regulation's requirement for supply chain verification of deforestation-free status at the farm level. In West Africa, distinguishing cocoa trees from forest shade canopies with standard multispectral satellite data is difficult because their spectral signatures are similar.
This study introduces a scalable approach that uses high-resolution hyperspectral imagery from the Wyvern Dragonette satellite constellation to identify cocoa within mixed agroforestry landscapes. The methodology uses Google Earth Engine as the cloud platform and integrates available hyperspectral images, which are limited by frequent cloud cover, with ground-truth data from Abeya’s smallholder supply chain network.The proposed methodology uses unique spectral 'forest fingerprints' from adjacent native forests to characterise the background canopy. Pixels within farm boundaries that deviate from these forest signatures but correspond to the spectral patterns of known cocoa plantations are identified. These cocoa-specific signatures are subsequently associated with multispectral Sentinel-2 data and pre-trained geospatial foundation models, facilitating cocoa tracking in regions lacking hyperspectral imagery.
This is achieved by utilising the high spectral dimensionality of the Wyvern Dragonette constellation, which captures 31 bands, to resolve sub-pixel mixing between cocoa and forest shade trees that multispectral sensors typically cannot disentangle. These high-fidelity insights are subsequently used to fine-tune pre-trained geospatial foundation models, effectively transferring hyperspectral intelligence to the broader spatial and temporal coverage of the Sentinel-2 archive. This approach demonstrates the potential for emerging satellite constellations to transition from experimental platforms to operational, interdisciplinary monitoring tools that support environmental policy and sustainable supply chain decision-making.
To support validation in data-sparse, smallholder contexts, the framework incorporates participatory field observations when available. Planned farmer questionnaires, aimed at estimating cocoa tree counts and farm-level planting characteristics, will be explored as a complementary source of reference information. These self-reported inputs are intended to provide an independent check on spatial cocoa predictions and help contextualise spectral patterns observed from space. While the availability and completeness of such data may vary, this approach highlights the potential of farmer-generated information to strengthen EO-based monitoring of agroforestry systems.
By focusing on operationally effective modelling choices rather than theoretical optimality, this work outlines a practical pathway for integrating emerging hyperspectral satellite constellations into scalable geospatial workflows. The proposed framework aims to support future assessments of cocoa traceability, EUDR compliance, and sustainable land-use monitoring in tropical agroforestry systems.
How to cite: Van den Dool, G., Malka, S., and Jones, E.: From Hyperspectral Unmixing to EUDR Compliance: Scalable Cocoa Traceability in West African Agroforestry Systems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17996, https://doi.org/10.5194/egusphere-egu26-17996, 2026.
Volcanic regions provide natural laboratories for studying interactions between surface materials, atmospheric aerosols, and radiative processes on Earth and other planetary bodies. This study combines ground-based gas monitoring, spaceborne hyperspectral imaging, and unsupervised machine learning to characterize the eruptive activity and plume properties of Mount Etna (Italy) and to develop analogs for volcanic processes in the atmosphere of Venus. Etna, Europe’s most active volcano, exhibited persistent activity from 2023 to 2025, dominated by Strombolian explosions, lava fountains, ash plumes, and small lava flows centered on the Southeast Crater Complex.
Key volcanic gas parameters are compiled from Istituto Nazionale di Geofisica e Vulcanologia (INGV) reports into a harmonized, machine-readable dataset. The time series include daily sulfur dioxide flux (SO₂), carbon dioxide flux (CO₂), mean partial pressure of CO₂ (pCO₂), and helium isotope development (He), all of which are fundamental indicators of the state of the magmatic system.
Concurrently, all available Earth Surface Mineral Dust Source Investigation (EMIT) hyperspectral images (350–2500 nm) acquired from the International Space Station over Etna during periods of unrest and eruption are used to characterize the detailed spectral behavior of volcanic surface materials. Radiance and reflectance data cubes are converted to spectral absorption wavelength images to isolate diagnostic absorption features directly related to specific minerals or material types. These products emphasize Fe-bearing silicates, oxides, alteration phases, and other mineral constituents of fresh/weathered lava flows, pyroclastic deposits and volcanic plumes. Unsupervised machine learning classification is then applied to the processed hyperspectral data to derive material and mineral maps without prior training data. For each class, representative spectra (average, minimum, maximum) are computed over the full spectral range to capture characteristic signatures and internal variability, allowing comparison with available spectral libraries (in-house, USGS, ECOSTRESS).
To derive land surface temperature concurrent ECOSTRESS data are selected and analyzed. The ECOSTRESS instrument is a multispectral thermal imaging radiometer that provides high-resolution measurements of surface thermal emission.
The integration of hyperspectral, gas, and thermal datasets provides a promising framework for characterizing volcanic plumes and lava flows. EMIT-based spectral information is combined with concurrent ECOSTRESS thermal observations to derive plume temperatures and discriminate plume types based spectral–compositional signatures. Unsupervised techniques successfully distinguish plumes from the background and identify different plume regimes. Preliminary results indicate that mineral particulates within plumes, including ferric iron (Fe³⁺) phases, can be detected, implying that both gaseous and mineralogical components of volcanic plumes are resolvable in space and time. This is particularly relevant for comparative planetology. The inferred mineralogical composition of terrestrial volcanic plumes may constrain plausible mineral particulates and aerosol types on Venus, yielding testable predictions for the composition and spectral behavior of Venusian volcanic aerosols and mineral dust.
Acknowledgement: The manufacturing of the VenSpec electronics and the preparation of spectral libraries for the EnVision mission in the Czech Republic are funded by ESA PRODEX under contract PEA4000147310.
How to cite: Kopackova-Strnadova, V. and Sedláčková, P.: Spectral properties catalogue of Earth-Venus analogues: Etna example, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18871, https://doi.org/10.5194/egusphere-egu26-18871, 2026.
Climate adaptation strategies for many cities include urban green infrastructure as nature-based solutions due to their potential to mitigate urban heat island effects and reduce surface runoff. However, rising temperatures, drought events and altered precipitation patterns are expected to impact plant phenology by shortening dormancy, resulting in earlier flowering and extended growing seasons. These changes can increase irrigation demand and susceptibility to damage, posing a risk to urban green infrastructure and its ecosystem functions.
In this study, we investigated changes in the growing cycles of urban green infrastructure in four European cities (Birmingham, Paris, Graz, Barcelona) from 1984 to 2024.
The approach is based on monitoring the Normalized Difference Vegetation Index (NDVI) from satellite images to assess long-term trends and analyze the potential effects of climate change on plant phenology in an urban environment. Although analyzing NDVI in urban environments is still relatively new, it is becoming more feasible due to the increased availability of long-term, high-resolution satellite images.
Monthly NDVI values were derived from pre-processed Landsat satellite images to analyze changes in urban green infrastructure and plant phenology. Raster-based pixel counts with an NDVI value above 0.3 were normalized to highlight intra-annual vegetation peaks and seasonal shifts. Temporal trends in vegetation activity were assessed using the non-parametric Mann-Kendall trend test to identify upward or downward trends in the time series. The Theil-Sen Slope Estimator was subsequently applied to determine the magnitude and direction of the detected trends.
The results showed that all four cities expanded their urban green spaces over the past 40 years, with Barcelona exhibiting a particularly substantial increase. Normalized NDVI values revealed an earlier occurrence of peak NDVI and decreases during summer months in certain years, indicating a possible link to drought events. Statistically significant increases in NDVI were observed in March, April, October, and November, indicating both an earlier onset and later offset of the growing season.
Overall the study shows the current changes in plant phenology and developments of urban green infrastructure under climate change. Integrating remote sensing of vegetation with urban water management can support more efficient and adaptive management strategies for irrigating urban green spaces.
How to cite: Kudaya, F. S., König, A., and Fuchs-Hanusch, D.: Analysing Changes in NDVI: A Long-Term Remote Sensing Approach to Monitor Trends in Plant Phenology of Urban Green Infrastructure, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19648, https://doi.org/10.5194/egusphere-egu26-19648, 2026.
Methane (CH₄) has a global warming potential approximately 20 times greater than that of carbon dioxide and is a major greenhouse gas emitted from rice paddies, making systematic emission management essential for achieving carbon neutrality. In South Korea, national greenhouse gas statistics are currently derived using IPCC Tier 1 and Tier 2 emission factor–based approaches, which have limitations in accounting for region-specific rice cultivation environments and management practices. Therefore, spatially detailed estimation of methane emissions is required. To address this need, this study first estimated methane emissions using time series unmanned aerial vehicle (UAV) observations and subsequently scaled up the approach using Sentinel-2 satellite time-series imagery to quantify methane emissions from rice paddies across South Korea.
Rice paddies were identified nationwide using a phenology-based classification approach derived from Enhanced Vegetation Index 2 (EVI2) time-series composites. Sentinel-2 data were processed on the Google Earth Engine platform as five-year averaged datasets from 2020 to 2024, with 15-20 day intervals during the rice growing season, considering cloud conditions to ensure image quality. Spatial and temporal consistency was achieved through cloud masking and median compositing. To reflect regional heterogeneity in rice growth processes, region-specific rice cultivar information and growth-stage timing at the municipal level were incorporated into the analysis.
For national-scale methane estimation, the temporal behavior of Sentinel-2 derived EVI2 was evaluated through comparison with UAV-derived EVI2 observations acquired at key rice growth stages. Quantitative agreement between satellite- and UAV-based EVI2 values was limited during the early growth stages, whereas consistent temporal trends were observed after the heading stage. Based on these findings, Sentinel-2 EVI2 variables observed after the heading stage were selected as explanatory variables for the methane emission model, and UAV-based methane flux estimates were used as reference data.
The resulting empirical regression model was applied to all identified rice paddies nationwide to estimate methane emissions at both the parcel and administrative unit scales. The spatial distribution of estimated emissions exhibited pronounced regional variability, reflecting differences in rice cultivation area and growth conditions. Absolute emission estimates were slightly lower than those reported in some previous studies, a result attributed to mixed-pixel effects inherent in moderate-resolution satellite imagery. Despite this difference, the spatial patterns of methane emissions and the relative ranking among regions were generally consistent with results from comparable rice methane studies and national statistics.
This study demonstrates the applicability of a Tier 2.5 level methane emission estimation approach that integrates rice growth information derived from satellite time-series data. The proposed framework provides a scalable and cost-effective pathway for improving national greenhouse gas inventories and supporting the development of region-specific mitigation strategies for methane emissions from rice cultivation.
Acknowledgement:
This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (RS-2021-NR060142), and by the BK21 FOUR program (Grant No. 4120200313708), funded by the National Research Foundation of Korea (NRF).
How to cite: Song, Y., Song, C., Choi, S.-E., and Lee, W.: National Scale Estimation of Methane Emissions from Rice Paddies in South Korea Using UAV and Sentinel-2 Time Series Data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21444, https://doi.org/10.5194/egusphere-egu26-21444, 2026.
Wetlands are unique ecological systems that exhibit both aquatic and terrestrial landscape characteristics. Wetland water quality is facing significant threats due to the combined impacts of climate variations and human activities. Therefore, analyzing the spatiotemporal dynamics of water clarity (Secchi disk depth, Zsd) across different types of wetlands, as well as revealing the relationship between driving factors and their responses, is crucial for monitoring and assessing variations in wetland water environments. The study utilizes Landsat data to analyze the spatiotemporal dynamics of Zsd and the response relationships of driving factors in typical flow-connected and non-flow-connected lake wetlands since 1984, focusing on Dongting Lake (DTL) and Poyang Lake (PYL) as examples of river-flow lake wetlands, and Baiyangdian Lake (BYD) and Hengshui Lake (HSL) as examples of non-flow-connected lake wetlands. The findings reveal that the variations in Zsd and the spatial variability in river-flow connected lake wetlands are greater than those in non-flow-connected lake wetlands, which may be attributed to abundant precipitation and frequent water exchanges. In terms of interannual scale, distinct stages of increase and decrease in Zsd are observed, characterized by peak values followed by declines. These interannual variations are driven not only by climate fluctuations but also by variations in landuse and landscape. Through quantitative analysis of the spatiotemporal dynamics of Zsd in various wetland types, the study further explores the driving mechanisms and the differing responses of climate factors, landuse, and landscape to Zsd variations in river-flow and non-flow-connected lake wetlands. The research provides a data foundation and scientific support for monitoring and managing water resources in large-scale wetlands under the dual impacts of climate variability and human activities.
How to cite: Zhao, Y.: Contrasting Drivers of Water Clarity: A Multi-Decadal Satellite Analysis of Two Types of Lake Wetlands, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-61, https://doi.org/10.5194/egusphere-egu26-61, 2026.
Scanning Array for hyper-Multispectral RAdiowave Imaging (SAMRAI) is a passive interferometric radiometer. It realizes ultra-wideband (1-41 GHz) and high-frequency-resolution (27 MHz) microwave spectrum measurement. We believe that SAMRAI is the world's first microwave hyperspectral radiometer.
JAXA has been operating the AMSR series of satellite-borne microwave radiometers for over 30 years, including AMSR3, which was launched in 2025.
However, because the design has remained largely unchanged over this time, various issues have become apparent. In particular, the radio frequency interference (RFI) contaminating the natural-origin signals is a serious problem, and we believe that microwave hyperspectral measurement is essential for identifying and isolating RFI signals. This was a big motivation for developing SAMRAI. In addition, microwave hyperspectral measurement must have new possibilities, such as making it possible to measure the frequency characteristics of the emissivity of the Earth surface.
Development of the satellite-borne SAMRAI is progressing toward launch by 2028. SAMRAI is required to receive natural-origin weak microwave power with high sensitivity over an ultra-wideband range, with an upper frequency limit more than 40 times the lower frequency limit. Furthermore, the microwave power amplified during the reception process must be precisely calibrated to the brightness temperature at the input to the antenna. Developing a new receiver that satisfies all of these requirements posed various challenges, but we have overcome them through design improvements.
Here we present the main design changes made to the SAMRAI ultra-wideband receiver since the start of development in 2021, and the performance improvements achieved through these design changes.
How to cite: Maeda, T., Kobayashi, Y., Trung, N. T., Takei, Y., Yano, T., and Tomii, N.: Current Development Status of Satellite-borne Scanning Array for Hyper-multispectral Radiowave Imaging (SAMRAI), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14628, https://doi.org/10.5194/egusphere-egu26-14628, 2026.
The increasing availability of multispectral, thermal and Synthetic Aperture Radar (SAR) observations from satellite and ground-based platforms has significantly enhanced the capability to monitor environmental processes and natural hazards. However, the effective exploitation of Earth Observation products strongly depends on their accessibility, usability and dissemination beyond specialized research environments. In this context, the MAPEOS project aims to develop a mobile application designed to facilitate access to and dissemination of remote sensing products for environmental monitoring, bridging the gap between advanced Earth Observation infrastructures and end users.
MAPEOS is conceived as a cross-platform mobile application that disseminates scientific data and value-added products generated by the Platform for Earth Observation from Space (PEOS), a national e-infrastructure developed within the Italian “Monitoring Earth’s Evolution and Tectonics” (MEET) project, funded by the National Recovery and Resilience Plan (PNRR – Next Generation EU). PEOS is coordinated within the activities of the INGV Center for Space Observations of Earth (COS) and contributes to the European Plate Observing System (EPOS) research infrastructure. While PEOS provides the processing, integration and management of heterogeneous remote sensing datasets, MAPEOS focuses on user-oriented delivery of selected products, emphasizing intuitive visualization, rapid access and effective communication of environmental information.
The mobile application supports the visualization of key Earth Observation products relevant to environmental monitoring and natural hazard assessment. These include ground deformation derived from SAR interferometry, surface temperature and environmental parameters from multispectral and thermal imagery, volcanic cloud properties and SO₂ and ash emissions, as well as a range of space weather products derived from satellite observations and ground-based measurements. In particular, MAPEOS aims to disseminate information related to geomagnetic activity, ionospheric disturbances and space weather conditions that can affect technological systems and the near-Earth environment, providing an integrated view of solid Earth, atmospheric and space-related processes.
A central aspect of MAPEOS is its ability to exploit harmonized and fused products generated within PEOS, integrating spectral, spatial and temporal information from multiple sensors and platforms. The application accesses data through standardized application programming interfaces (APIs) compliant with OGC standards, OpenAPI specifications and the EPOS-DCAT-AP profile, ensuring interoperability, scalability and alignment with FAIR principles. This architecture allows the mobile application to remain lightweight while providing near–real-time access to updated remote sensing and space weather products.
MAPEOS is designed for a broad range of users, including researchers, students, civil protection operators and the general public. By lowering technical barriers and enabling mobile access to Earth Observation and space weather information, the application supports knowledge transfer, outreach activities and increased awareness of environmental processes and natural hazards.
This contribution presents the design concept and current development status of the MAPEOS mobile application, highlighting the role of mobile technologies in enhancing the accessibility and societal impact of space-based remote sensing products for environmental monitoring and decision-support applications within national and European frameworks.
How to cite: Spina, M., Marcocci, C., Pica, E., Viola, M., and Guardo, R.: MAPEOS: a mobile application for accessing and disseminating space-based remote sensing products for environmental monitoring, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20279, https://doi.org/10.5194/egusphere-egu26-20279, 2026.
Monitoring water quality in areas affected by mining activities requires high-spatial and temporal resolution data, which remains a challenge for traditional satellite and ground-based methods. We present a novel, cost-effective instrumental setup for water surface reflectance measurements using a miniature light-weight Ocean Optics STS-VIS microspectrometer (40 x 42 x 24 mm; 337–823 nm; 1.2 nm spectral resolution; 1024 bands). The sensor was mounted on the DJI Phantom 3 Advanced UAV using a custom-developed, 3D-printed holder to ensure stability and precise nadir orientation. With a field of view (FOV) of 25° and an operational flight altitude of 3 m, the system achieved a spatial resolution (ground footprint) of 1.2 m per measurement point, allowing for precise targeting of narrow water bodies. The system was used across three diverse mining regions: the Chalkidiki Peninsula and Kirki (Greece), and Andalusia (Spain).
To derive accurate reflectance from raw intensity data, a standardized calibration protocol was established, involving dark spectrum subtraction and reference measurements using a Spectralon panel (Spectral Evolution; 100% reflectance). Flights were conducted manually to minimize propeller propeller-induced surface turbulence, following standardized patterns (longitudinal and diagonal for streams; sun-relative for water bodies).
The processing workflow addresses the high volume of raw data (up to 400 spectra per site). We implemented a smoothing pipeline and filtration:
(1) application of Savitzky-Golay filters (SGF) with a 2nd-degree polynomial and varying window sizes (66, 99, 132),
(2) statistical outlier removal based on +/-1.5 standard deviations,
(3) visual inspection eliminating interference from bank vegetation or rocks above the water.
Based on our analysis, the SGF window size of 99 was selected as optimal. While the window size 66 left significant residual noise and the window size of 132 caused the loss of critical spectral absorption features, the window size of 99 provided sufficient noise reduction while preserving the integrity of the spectral signal.
The final averaged spectra were correlated with water sample laboratory analyses using Partial Least Squares Regression (PLSR). Our results identified key wavelengths sensitive to specific mining-related water quality parameters. This study demonstrates that the proposed UAV-spectrometer integration provides a robust, flexible, and high-precision alternative for monitoring contaminated aquatic systems in logistically challenging environments.
The presented analysis was conducted under the support of the EC through the MultiMiner project, funded under the European Union’s Horizon Europe research and innovation programme (Grant Agreement No. 10109137474), and under the support of the MINEYE project, funded under the European Union’s Horizon Europe research and innovation programme (Grant Agreement No. 101138456).
How to cite: Kýhos, M., Jelének, J., Kořínková, B., Zabokas, G., López del Río, M., Tenorio Matanzo, S., and Kopačková-Strnadová, V.: A Lightweight UAV-based Spectroscopic System for Water Quality Monitoring in Mining-Impacted Environments: Setup, Data Processing, and Validation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20448, https://doi.org/10.5194/egusphere-egu26-20448, 2026.
Urban heat risk assessments increasingly require land surface temperature (LST) and near-surface air temperature at spatial scales that resolve microclimatic drivers such as material heterogeneity, shading, and complex terrain. While satellite thermal products and stationary air temperature observations provide essential regional and temporal context, their spatial resolution and coverage, as well as satellite revisit frequency, limit the quantification of surface thermal variability within urban blocks and campus-scale environments. Unmanned aerial vehicle (UAV) thermal imagery can bridge this scale gap, but quantitative LST retrieval remains sensitive to radiometric calibration, emissivity assumptions, local viewing geometry, geolocation accuracy, and acquisition-time atmospheric conditions.
This contribution develops and demonstrates a reproducible UAV thermal remote sensing workflow that converts raw thermal imagery into georeferenced LST mosaics over complex urban surfaces. Using a DJI Matrice 4T thermal sensor over a university campus in Sheffield, UK, thermal data were collected through multiple field surveys combining UAV flights with ground measurements collected alongside the flights. UAV flights were conducted in late June 2025, with flight planning targeting approximately 80% forward and side overlap. Raw thermal imagery derived from UAV was batch-converted using documented acquisition parameters informed by on-site conditions. Key factors include target distance, relative humidity, emissivity, and reflected apparent temperature, applied consistently within each survey to support cross-frame comparability. This research: (1) converts raw thermal imagery to georeferenced thermal outputs using ground-informed acquisition parameters (i.e. distance, humidity, emissivity, and reflected apparent temperature) to stabilise cross-frame temperature consistency; (2) reduces spatial distortions through co-registration with high-resolution basemaps, with a digital terrain model (DTM) used as an additional terrain reference; (3) accounts for surface emissivity variability by integrating land use/land cover and material proxies derived from complementary geospatial datasets, with high-resolution RGB orthomosaics used to derive land cover or material proxies (e.g., vegetation and pavements) that inform thermal processing parameters and support consistent interpretation of microscale thermal patterns.
The workflow delivers thermal remote sensing products at centimetre-level ground sampling distances and is designed to be transferable to other urban sites using standard UAV surveys and widely available geospatial datasets. By foregrounding calibration, emissivity handling, and quality control, this study strengthens the methodological basis for integrating UAV thermal observations into environmental remote sensing in urban settings, enabling more robust cross-scale interpretation of urban thermal patterns and supporting evidence-based decision making.
How to cite: Wang, J.: UAV thermal remote sensing for land surface temperature mapping in complex urban environments, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21586, https://doi.org/10.5194/egusphere-egu26-21586, 2026.
