Conference Agenda

Methane (CH4) is a major greenhouse gas; however, large-scale monitoring remains challenging due to the high costs and spatial limitations of ground-based and airborne observations. In contrast, Sentinel-2 shortwave infrared (SWIR)–based plume detection is hindered by its coarse spectral resolution, surface artifacts, and limited real-world annotations. This study proposes a self-supervised learning (SSL) framework based on the Simple Framework for Contrastive Learning of Visual Representations (SimCLR) to learn transferable CH4 plume representations from unlabeled Sentinel-2 data. A real-world dataset of 456 Sentinel-2 image tiles was manually annotated using the multi-band–multi-pass (MBMP) approach and utilized to evaluate six encoder backbones. Across five labeled-data portions ranging from 20% to 100%, SimCLR pretraining improved plume segmentation compared to ImageNet-only initialization. In the full-data scenario, MobileNet achieved an F1-score of 0.90 with an Intersection over Union (IoU) of 0.80, while Shifted Window Transformer (SwinT) reached an F1-score of 0.85 with an IoU of 0.75. The benefit of self-supervised pretraining was most evident with limited labeled data, where ImageNet-only models degraded substantially, while SimCLR-pretrained encoders achieved higher accuracy. Moreover, the Integrated Mass Enhancement (IME) method was employed for quantifying the emission flux rate. MobileNet provided the strongest agreement with reference emission estimates, achieving an RMSE of 1690 kg/h. Finally, the results demonstrate that SimCLR-based SSL substantially enhances CH4 plume detection from Sentinel-2 imagery and supports more reliable emission quantification for large-scale CH4 monitoring.

Column-averaged methane (XCH4) data spanning 2018-2023 from the European Space Agency (ESA) Tropospheric Monitoring Instrument (TROPOMI) on the Copernicus Sentinel-5 Precursor satellite are assessed for evidence of methane (CH4) emissions from permafrost. We generated bi-monthly anomaly maps of XCH4 from TROPOMI and soil temperature (Tsoil) from reanalysis data for all land north of 50°N. Considering the XCH4 anomalies in the contexts of soil carbon content and wind variability led to a focus on Canada’s Hudson Bay Lowlands (HBL), Earth’s second largest peatland complex (~325,000 km2), which is underlain by continuous to isolated permafrost. This sub-Arctic region is vulnerable to rapid climatic warming and exhibits wind conditions favorable for emission detection from space. HBL XCH4 anomalies strongly correlate with soil temperature anomalies (R = 0.626 to 0.866), consistent with wetlands as the primary CH4 emission source; however, the strong increase in CH4 emissions over 2018-2023 may also suggest a contribution from permafrost thaw and expansion of thermokarst fens.

This study leverages TROPOMI satellite observations and atmospheric inversion modelling to quantify methane emissions from urban wetlands across six major North American cities, including Toronto, Montreal, New York, Los Angeles, Houston, and Mexico City. By coupling high-resolution column-averaged methane measurements with the GEOS-Chem chemical transport model via the Integrated Methane Inversion (IMI) platform, the research distinguishes emissions from both natural wetland and anthropogenic urban sectors. Results indicate that prior inventories substantially underestimate urban wetland methane emissions in most cities. Posterior wetland emissions are resolved alongside dominant anthropogenic sources such as landfills, energy systems, and wastewater, revealing spatially distinct patterns and highlighting seasonal wetland flux variability. The findings demonstrate that urban wetlands, although representing a relatively smaller source compared to anthropogenic emissions, display considerable underrepresented contributions to local methane budgets, underscoring the need for robust, integrated monitoring in urban environments. This methodology provides a scalable framework for routine urban wetland methane flux quantification and supports evidence-based climate mitigation and land management strategies.

Methane plume detection from medium-resolution multispectral satellites such as Sentinel-2 remains challenging due to weak methane signals and strong background variability across land cover, illumination conditions, and atmospheric states. To advance automated detection capabilities, we develop a large-scale benchmark dataset that combines simulated methane plume enhancements with real Sentinel-2 imagery, covering a wide range of emission magnitudes and diverse environmental scenarios. The dataset includes over 64,000 samples and incorporates methane-sensitive inputs derived from the MBMP retrieval workflow, providing a comprehensive foundation for robust model training and evaluation. Building on this dataset, a hybrid transformer–U-Net architecture is proposed, integrating global self-attention with Grouped Attention Gates to enhance feature fusion and improve segmentation of methane structures. The model achieves high accuracy on the benchmark dataset and demonstrates strong generalization to real emission events in complex environments. The combined contributions of the benchmark dataset and hybrid model offer a promising path toward reliable, scalable methane plume monitoring using widely available multispectral satellite observations.

Methane (CH₄) is a potent short-lived climate pollutant, making the detection of major point sources (“super-emitters”) crucial for mitigation. The Sentinel-5 Precursor (S5P) mission, with the TROPOMI instrument, captures global methane concentrations at ~7 × 5.5 km resolution with near-daily coverage. While this resolution is too coarse to identify emissions from individual facilities, its revisit frequency allows effective regional monitoring. Conversely, high-resolution (HR) imaging spectrometers like Carbon Mapper’s Tanager (~30 m) and NASA’s EMIT (~60 m) provide detailed plume mapping but have limited spatial and temporal coverage.

Carbon Mapper releases open-access, high-resolution plume products including georeferenced rasters and metadata. In this study, these HR detections serve as reference events to assess their visibility in coarser Sentinel-5P observations. The workflow includes curating HR events, summarizing their emission context, and inspecting nearby Sentinel-5P data for consistent methane enhancements. The method is exploratory and avoids presupposing Sentinel-5P’s success or failure in detecting plumes at this scale.

This analysis bridges the gap between frequent global monitoring and targeted HR observations. It establishes a path for future cross-sensor integration, combining HR spatial precision with Sentinel-5P’s temporal continuity. With additional labeled data, this approach could inform machine-learning tools for methane anomaly detection and plume segmentation, improving operational methane monitoring across scales.

This study proposes a deep learning–based enhancement of feature tracking to improve Sea Ice Drift (SID) estimation using Sentinel-1 Synthetic Aperture Radar (SAR) imagery. Traditional computer vision methods, such as Oriented FAST and Rotated BRIEF (ORB), are commonly used for generating initial drift vectors within the Nansen Environmental and Remote Sensing Center (NERSC) workflow; however, their performance declines under rotational variations, low-texture surfaces, and the fluid-like, short-term dynamics of sea ice. To address these limitations, this study evaluates two deep learning–based methods—SuperGlue and the Local Feature Transformer (LoFTR)—to enhance the robustness and accuracy of feature matching between consecutive SAR scenes. Furthermore, to effectively utilize multi-polarization information, a multi-polarization strategy was applied across both the feature tracking and pattern matching stages. Performance was evaluated using in-situ drift observations from Ice-Tethered Profiler (ITP) buoys, with feature matching assessed by the number of matched keypoints and estimated SID vectors, and drift accuracy evaluated using RMSE and the coefficient of determination (R²). Experimental results demonstrate that polarization integration significantly improves performance, reducing RMSE and increasing R². Among the methods, LoFTR achieved the best performance, followed by SuperGlue and ORB, with notable reductions in speed and directional errors. Overall, the findings demonstrate that deep learning–based methods substantially improve the stability and accuracy of SAR-derived SID estimation. These methods enable more stable and reliable performance in the Arctic environment, which is characterized by sea ice reduction, strong seasonal variability, and highly dynamic drift patterns.

global sea ice distributions on a daily basis. Ice concentration (IC) is one of the most important sea ice parameters derived from brightness temperatures measured by the microwave radiometers. However, even at microwave frequencies, the brightness

temperature data over open ocean areas are affected by the presence of adverse weather conditions, including elevated atmospheric water vapor, cloud liquid water, and abnormal surface roughness conditions. The net result is the retrieval of moderate sea ice concentration values in the open ocean where sea ice is not expected. The current sea ice algorithms make use of what is called a “weather filter” to correct such false retrieval of sea ice, but significant areas in the ice-free water that have the false ice cover remain in some areas. In this study, an improved weather filter, namely the Advanced Weather Filter (AWF), that minimizes, if not eliminates, this problem, developed by Cho et al. (2023), was implemented to produce JAXA/AMSR2 sea ice concentration products

of the Arctic for verification. The AWF was validated and shown to be very effective in selected study regions in the Arctic during the summer time from 30 June to 3 July 2014 and the winter time from 15 December to 18 December 2014, thereby supporting the

integration of the AWF into the standard AMSR2 sea ice concentration product. The AWF should be broadly applicable and can be implemented in other satellite passive microwave ice concentration datasets.

Seasonal soil freeze-thaw (FT) transitions govern critical hydrological and biogeochemical processes across northern landscapes. The physical state of freezing soil exists on a thermodynamic continuum influenced by the zero-curtain effect, a period where latent heat exchange stabilizes temperatures near 0°C. Despite this, operational passive microwave algorithms, such as FT-SMAP and FT-ESDR, enforce discrete binary classifications that mask this biogeochemically active partially frozen period. To address this limitation, this study establishes a probabilistic, non-binary FT detection framework using a parsimonious L1-regularized logistic regression model driven by multi-frequency passive microwave observations. To isolate dynamic phase changes from static landscape noise, the model integrates two locally standardized indices: the Normalized Polarization Ratio (NPR) from SMAP L-band to track soil liquid water permittivity, and the Normalized Difference V-Pol (NDV) from AMSR2 Ka/Ku-bands to capture volume scattering within canopies and snowpack. The model was trained using topsoil temperatures from North American networks, employing a probabilistic Soil Freezing Characteristic Curve to isolate high-confidence training end-members and a density-based spatial clustering approach to prevent spatial data leakage. The logistic framework demonstrated robust geographic generalizability, achieving an F1-score of 0.957 in Tundra environments. Crucially, it significantly mitigated false alarms in complex forested canopies, suppressing false positive rates in Mixed Forests to 12.6%, compared to 44.3% for FT-ESDR and 33.5% for FT-SMAP. By mathematically isolating the zero-curtain transition, this scalable approach provides the continuous baseline data necessary for advancing seasonal carbon respiration modeling in rapidly warming northern environments.

This work presents instrument development and calibration of a terrestrial L-band radiometer designed to support satellite retrieval validation and radiation transfer model parameter refinement in the Arctic. As satellite-based retrievals of key geophysical variables such as snow density and ground temperature continue to improve, their accuracy remains limited by scarce ground-truth data. Our refined radiometer addresses this gap by providing targeted, high-resolution terrestrial measurements capable of characterizing surface heterogeneity across Arctic land and water environments.

The instrument was redesigned from an existing model, and was improved based on lessons from earlier field campaigns, focusing on robustness, simplified operation, and enhanced radio-frequency isolation. Calibration procedure focused on measuring the night sky over several nights in cold temperatures to accurately characterize the operation in very cold conditions.

Initial calibration experiments show stable performance and improved consistency compared to earlier instrument versions. While some challenges remain, the system is expected to be field ready and able to capture brightness temperatures accurately over long time periods and varying conditions.

Future campaigns will extend these measurements to lake and sea ice, supported by ground-penetrating radar enabled surface roughness characterization. These efforts will ultimately contribute to improved radiative transfer modeling and more accurate satellite retrievals of key Arctic geophysical variables.

Accurate mapping of retrogressive thaw slumps (RTSs) in permafrost regions remains challenging due to their irregular morphology, blurred boundaries, and strong spatial correlation. This paper proposes a lightweight multi-level self-modulation (MLSM) module embedded into the UNet++ backbone to enhance non-local feature modeling for high-resolution image segmentation. The overall framework is built upon a UNet++ backbone with dense skip connections, where the proposed MLSM module adaptively fuses multi-scale contextual information to enhance feature coherence across spatially correlated regions. By incorporating low-rank regularization through a soft nuclear norm, MLSM dynamically modulates feature responses according to structural variations, allowing attention to adapt to spatially complex RTS regions. The integration of depth-wise convolution and channel recalibration further refines feature aggregation efficiency. Experimental evaluations on Maxar dataset demonstrate that the proposed method achieves superior segmentation accuracy and smoother boundary delineation compared with existing models. The proposed framework provides a robust and computationally efficient approach for RTS mapping, contributing to improved understanding of local geomorphic patterns.

This study investigates long-term snow persistence dynamics across the North-Western Himalaya (NWH) spanning 2000–2024 using MODIS Terra and Aqua daily snow products. Snow persistence—defined as the number of days a location remains snow-covered—is a crucial indicator of climatic variability and hydrological behaviour in high-mountain environments. Annual snow persistence was derived from daily CGF_NDSI_Snow_Cover layers after mosaicking, clipping to the study region, reclassifying snow pixels, and summing snow days at 500 m resolution. Pixel-wise trend analysis was conducted using the Mann–Kendall test, supported by Kendall’s Tau, p-values, and variability metrics.

The results show clear spatial contrasts: high-elevation zones (>4000 m) maintain persistent snow cover (>300 days/year), while mid-altitude regions (1500–3000 m) exhibit moderate persistence but significant negative trends. Low-elevation areas display minimal snow longevity and rapid decline over the 25-year period. The region recorded maximum snow-covered area in 2019 and a notably reduced extent in 2016. Approximately 29% of the NWH shows statistically significant trends, predominantly negative, with an overall mean decline of −3.2 snow days per year. Variability is highest in mid-elevation transition zones, which appear particularly sensitive to warming.These findings highlight ongoing reductions in seasonal snow cover in the NWH and their implications for glacier mass balance, water resource availability, and hydrological timing. The study underscores the value of long-term satellite-based monitoring to understand cryospheric response under changing climate conditions.

This study takes into account the heterogeneity of regional development stages and the local development context, systematically explores the relationship between localization and sustainable development, and constructs a quantifiable localized SDGs assessment model for China. An empirical analysis of the multi-level SDGs evaluation system was conducted. To address the challenges posed by heterogeneous multi-source data in the assessment process, a composite Key Performance Indicator (KPI) screening model based on Random Forest and Hyperlink-Induced Topic Search (HITS) was proposed, enhancing the scientific rigor and efficiency of localized SDGs monitoring and evaluation.

This paper is mainly based on the experience of Jiaxing City, which has done a good job in the investigation and monitoring work in China, to inspire the investigation and monitoring work in Beijing, and to provide technical support for supporting the investigation and monitoring work in Beijing to achieve the goal of "one inspection, multi-purpose, integration and sharing". Through research, the four databases of new basic surveying and mapping, land change survey, urban land space monitoring, and land space planning are integrated, and the integration of content indicators, survey methods, collection and storage, management and sharing is realized.

Accurate extraction of water bodies from remote sensing imagery is essential for environmental monitoring, water resource management, and hydrological applications. However, the performance of deep learning models for water segmentation depends on the availability of representative datasets that capture diverse environmental and spectral conditions, particularly in tropical and semi-arid regions that remain underrepresented in existing datasets. This study presents the Pernambuco Waterbody Dataset (PWD), a multi-source dataset comprising aerial and satellite remote sensing imagery for water-body segmentation. The dataset covers the state of Pernambuco, Brazil, including tropical and semi-arid environments associated with the Atlantic Forest and Caatinga biomes. The dataset includes high-resolution aerial imagery (0.5 m) from the Pernambuco Tridimensional Program (PE3D) and Sentinel-2A imagery (10 m), with manually annotated water bodies generated by cartographic specialists. The dataset was constructed through data acquisition, preprocessing, manual annotation, mask generation, patch extraction (512 × 512 pixels), and division into training, validation, and test subsets. The first version includes 51,743 aerial patches and 15,321 Sentinel-2A patches. To validate the dataset, U-Net, U-Net++, and DeepLabV3+ architectures with ResNet and EfficientNet backbones were evaluated using Recall, Precision, F1-score, and IoU metrics. The best performance was achieved by U-Net++ (ResNet34) for aerial imagery (IoU 0.946) and U-Net (ResNet34) for Sentinel-2A imagery (IoU 0.871). Overall, the proposed dataset provides a robust benchmark for advancing deep learning-based water body extraction using multi-source remote sensing data.

Multispectral satellite imagery provides an essential source of information for monitoring environmental transformations, yet robust unsupervised change detection remains challenging due to radiometric variability and seasonal dynamics. At the same time, supervised approaches based on Deep Learning are often constrained by the need for computationally expensive accelerated hardware and the limited availability of high-quality annotated datasets. This work introduces a framework based on the Bitemporal Spatial Autocorrelation (BSAC) matrix, that rather than relying on pixel-wise spectral differencing or data-intensive Deep Learning models, it is designed to quantify structural changes by evaluating the symmetry properties of the spatial autocorrelation across multiple spatial lags. Three complementary metrics are derived from the BSAC representation: a binary change/no-change trigger that identifies structural discontinuities, an asymmetry magnitude that measures the intensity of change, and a normalized Symmetry Index obtained via singular value decomposition to characterize the geometric coherence of the correlation structure.

The methodology is applied to Sentinel-2 imagery of Lake Fanaco (Sicily, Italy), which experienced severe desiccation during the 2024 drought. Experiments conducted using NDWI and NDVI confirm the index-agnostic nature of the framework, capturing both

hydrological contraction and vegetation stress. Comparison with an unsupervised K-means segmentation baseline shows strong spatial agreement in identifying the affected areas. Thanks to its unsupervised formulation and near-linear computational complexity, the BSAC framework represents a scalable and interpretable approach for operational change monitoring in Earth observation.

Accurate and rapid burned-area mapping is essential for assessing the ecological impacts of forest fires and supporting post-fire recovery efforts. Traditional pixel-based methods often suffer from limited accuracy due to spectral confusion, topographic effects, and reliance on empirical thresholds. Although deep learning models such as U-Net, DeepLab, and SegFormer improve spatial precision, their operational scalability is constrained by the need for extensive labeled data and regional retraining.

This study introduces a zero-shot burned area mapping approach using the Segment Anything Model (SAM) with Sentinel-2 imagery. SAM, trained on over a billion masks, enables prompt-based segmentation without task-specific training. Composite inputs derived from NBR, NBR2, and NDVI indices were generated and fed into SAM, followed by testing multiple pre-processing, post-processing, and hyperparameter configurations.

Results show that multi-scale settings (crop_n_layers = 2) significantly enhance boundary continuity and geometric accuracy. The method achieved IoU values of 0.89 (Bursa) and 0.87 (Çanakkale), with corresponding F1 scores of 0.94 and 0.92 performances comparable to, and in some cases exceeding, supervised models. Integrating spectral index composites further reduced boundary fragmentation and improved discrimination between burned and unburned surfaces.

Overall, the proposed framework eliminates dependence on manual labeling, offering a fast, scalable, and cost-effective solution adaptable to diverse ecosystems and sensor conditions. The study demonstrates one of the first systematic applications of SAM for burned-area detection, highlighting its strong potential for zero-shot environmental monitoring and rapid post-fire assessment.

In many regions of the world, especially those affected by armed conflicts, urbanization, or intensive environmental transformations, a high dynamic of land cover and land use changes is observed. Reliable monitoring of these processes requires the application of classification methods that ensure both high thematic accuracy and temporal consistency. This paper presents a multitemporal classification methodology based on Sentinel-2 optical data and machine learning models.

The research was conducted for the city of Sievierodonetsk (Luhansk Oblast, Ukraine) – an area that suffered significant destruction in 2022 as a result of military operations. The aim of the analysis was to identify land use changes in the years 2021-2025 using three classifiers: k-Nearest Neighbors (kNN), Random Forest (RF), and Gradient Boosting Classifier (GBC), combined into an ensemble system based on dynamic confidence weighting.

Quality assessment using the recall metric showed that the fusion method outperformed individual classifiers, achieving average values of 0.87-0.96, while classical models obtained 0.81-0.89. The largest changes (39%) occurred in the years 2022-2023, coinciding with the period of greatest military activity. The proposed method achieved the highest classification quality indices (F1 = 0.93, Acc = 0.98 for 2021), surpassing global products and models based on AlphaEarth. In subsequent years, high stability was maintained (F1 ≥ 0.88), confirming the effectiveness and robustness of the approach under various environmental conditions

This study examines the landscape dynamics in the region surrounding Comandante Ferraz Antarctic Station, Keller Peninsula, King George Island, focusing on the quantification of land cover changes over 23 years. Emphasis is placed on the integration of a multitemporal Landsat time series (2001–2024) within a standardized spatio-temporal data cube framework, coupled with a Random Forest (RF) classification approach. This methodology enables consistent pixel-wise trajectory analysis across seven distinct epochs. The RF models achieved robust performance, with F1-scores for dominant classes like water and soil typically exceeding 0.90, although seasonal snow and ice showed greater spectral ambiguity in transitional months. Quantitative results from the transition matrices reveal a significant landscape reconfiguration: while ice (85.3%) and soil (81.2%) showed high persistence, a prominent trend of deglaciation was identified, characterized by the transition of ice and snow into exposed soil and the emergence of pioneer vegetation communities detected from 2014 onwards. The study demonstrates that the integration of machine learning and data cubes provides a powerful tool for monitoring environmental shifts in high-latitude maritime Antarctica, supporting long-term ecological assessments and climate impact modeling.