Conference Agenda

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.