Search Results (589)

Land surface temperature (LST) is a key variable in the physics of land surface processes on both regional and global scales. This paper addresses the all-sky (clear-sky and cloudy-sky) LSTs retrieval from the data acquired by the Medium-Resolution Spectral Imager II on Fengyun 3D (FY-3D) satellite. First, an improved split-window algorithm to retrieve clear-sky LSTs is developed using numerical radiative transfer modeling experiments. Then, clear-sky LSTs are retrieved from MERSI-II/FY-3D data in January and July 2022 over an Asian area (70°E~130°E, 10°N~50°N), and cross-validated against MODIS/Aqua LST/emissivity (LST/E) Daily version 6 (MYD11C1 V6) product. Next, a hybrid method combining the eXtreme Gradient Boosting (XGBoost) model and the surface energy balance theory is developed to estimate cloudy-sky LSTs. After that, cloudy-sky LSTs are estimated from the MERSI-II data and validated with the China Meteorological Administration Land Data Assimilation System Version 2 (CLDAS V2) dataset. Against the MYD11C1 LSTs, the root mean square error (RMSE), bias and coefficient of determination (R²) of the retrieved clear-sky LSTs are 1.15 K, 0.01 ± 1.14 K, and 0.99, respectively. Against the CLDAS LSTs, the RMSE, bias and R² of the estimated hypothetical clear-sky LSTs are 4.05 K, 0.75 ± 3.98 K and 0.91, respectively, while they are 3.69 K, 0.36 ± 3.67 K, and 0.92 for the retrieved cloudy-sky LSTs, respectively, which indicates that the retrieval accuracy of cloudy-sky LSTs is improved after the cloud radiation effect correction. The all-sky LSTs retrieved in this study are accurate and consistent with the results in previous studies. Full article

Remote-sensing land surface deformation (LSD) is a powerful and effective approach for investigating regional alpine permafrost variations. However, alpine permafrost is often distributed in areas characterized by earthquakes, and the LSD of alpine permafrost is potentially contaminated or diminished by earthquake-related LSD. Therefore, this study aimed to derive the effective LSD in the alpine permafrost of the Source Area Yellow River (SAYR) by removing LSD originating from the M_w 7.4 Maduo earthquake in 2021-05-22 and analyzing the spatiotemporal variations in LSD during 2017–2024. Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) was used to obtain the initial LSD time series from Sentinel-1 images acquired during 2017–2024. The LSD of the M_w 7.4 Maduo earthquake, its aftershocks and the post-seismic relaxation in SAYR was simulated separately by considering its temporal process and removed from the LSD time series in SAYR. The final LSD was validated against in situ Global Navigation Satellite System (GNSS) measurements, and the spatiotemporal variations in LSD in SAYAR were subsequently analyzed. The study found the following: (1) the removal of the earthquake-related LSD was successful both spatially and temporally and the final LSD has mean absolute error (MAE) of 3.22 mm and root mean squared error (RMSE) of 3.92 mm; (2) during 2017–2024, the vertical LSD in SAYR was mostly −8–8 mm/y; (3) soil moisture determined the spatial distribution of the LSD direction in SAYR as a result of local drainage conditions, air temperature, precipitation and snow melt. This study demonstrated the necessity of removing the earthquake-related LSD when investigating the alpine permafrost LSD in tectonically active areas. The strategy adopted in this study serves as a technical reference for future investigations of this kind. The findings in this study provide insight for a thorough understanding of permafrost evolution on the Tibetan Plateau in the context of climate change. Full article

Microwave-based remote sensing possesses the capability to penetrate through atmospheric obstructions such as cloud layers and fog, making it extensively utilized for estimating parameters including soil water content, atmospheric moisture levels, and terrestrial surface temperatures. Extended temporal datasets serve as fundamental requirements for climatological investigations; however, individual satellite operational lifespans remain constrained and prove inadequate for establishing multi-decade temporal sequences. Consequently, conducting comparative analyses and implementing cross-calibration procedures across measurements obtained from distinct sensors exhibiting comparable operational features becomes imperative. The FengYun (FY)-3G spacecraft, deployed into orbit during April 2023, hosts China’s most recent orbiting microwave radiometric instrument, designated as the Microwave Radiation Imager–Rainfall Mission (MWRI-RM). The FY-3G satellite’s unique drifting equator crossing time orbit plays a critical role in the calibration behavior of the MWRI-RM instrument, representing a key novelty of this study. The reliability of its brightness temperature (TB) observations has attracted considerable attention. Within this investigation, we conduct comparative assessments of orbital TB observations acquired from FY-3G/MWRI-RM against corresponding measurements obtained from the Advanced Microwave Scanning Radiometer 2 (AMSR2) installed on the Global Change Observation Mission–Water 1 (GCOM-W1) platform, and establish a straightforward linear inter-calibration methodology. Both sensing systems show strong consistency, with correlation coefficients exceeding 0.9 for all corresponding channels and systematic biases ranging from −1.40 K to −0.14 K. FY-3G/MWRI-RM generally reports lower TB values than GCOM-W1/AMSR2. The inter-sensor differences vary with frequency, land cover type, and TB range. Larger negative biases are mainly observed at 23.8 GHz and over water bodies, whereas the biases at 89 GHz are generally close to zero for most surface types. Latitude-dependent TB biases are most evident at 10.65 and 18.7 GHz, especially for vertical polarization at high latitudes, while orbit-dependent differences are more pronounced for vertically polarized low- and mid-frequency channels. After applying an inter-calibration procedure using AMSR2 as the reference, the agreement between FY-3G/MWRI-RM and GCOM-W1/AMSR2 is improved substantially, with mean biases below 0.25 K and RMSE values below 2 K for all channels. Validation using independent datasets further supports the stability of the calibration. The calibrated FY-3G/MWRI-RM TB data provide a basis for constructing long-term passive microwave brightness temperature records and for retrieving land and atmospheric parameters. Full article

Accurate estimation of the weighted mean temperature (Tm) is essential for retrieving precipitable water vapor (PWV) from ground-based Global Navigation Satellite System (GNSS) observations. Machine learning (ML) techniques excel in modeling nonlinear relationships among Tm time series, station geographic coordinates, and surface meteorological parameters, and recent studies have demonstrated that ML and neural network models outperform conventional linear Tm models. However, the full potential of surface meteorological measurements at GNSS stations for high-precision Tm retrieval remains to be fully explored. This study develops regional Tm empirical models using two ML methods—random forest (RF) and Temporal Mixture of Experts with Sequential Attention (TMESA)—to generate reliable real-time Tm estimates and enhance the accuracy of operational GNSS-PWV retrievals over China. A traditional linear model is adopted as the baseline to evaluate the performance improvements of the proposed models. The models are trained and tested using 10-year (2014–2023) hourly ERA5-derived Tm products and in-situ surface pressure, temperature, and relative humidity from 2377 meteorological stations, with Tm diurnal variations, station coordinates, and day of year integrated as auxiliary predictive features. Validation is conducted using 2024 ERA5 reanalysis data and radiosonde profiles from 120 stations across China. Results show that the RF model yields a bias (RMSE) of −0.11 K (2.67 K) against ERA5 and −0.21 K (2.67 K) against radiosonde data, while the TMESA model achieves superior performance with bias (RMSE) of −0.02 K (2.34 K) and 0.09 K (2.46 K), respectively, whose performance levels comparable to state-of-the-art studies. Compared with the traditional linear model, the RF model reduces Tm RMSE by 32% against ERA5 and 25% against radiosonde data, while the TMESA model achieves reductions of 40% and 33%, respectively. These findings confirm that the proposed ML models can provide high-accuracy Tm estimates for reliable GNSS-PWV retrieval. Future work will focus on the operational application of these models for near-real-time GNSS-PWV estimation. Full article

The Northern Tibetan Plateau is among the most climate-sensitive alpine regions globally. To address the limited applicability of the traditional Remote Sensing Ecological Index (RSEI) in sparsely vegetated areas, this study developed a Soil-Adjusted Remote Sensing Ecological Index (SRSEI) tailored to cold and arid environments. The ecological quality of the Northern Tibetan Plateau from 2000 to 2025 was systematically evaluated and analyzed. The results indicate that: (1) The improved SRSEI achieved a first principal component (PC1) contribution of 72.76%, a significant enhancement over traditional models that effectively mitigates noise from soil backgrounds and anthropogenic features. (2) Between 2000 and 2025, ecological quality was predominantly moderate, following a characterized east-to-west declining spatial gradient. Overall mean SRSEI values fluctuated between 0.420 and 0.476, exhibiting a marginal downward trend. (3) Ecological degradation affected 50.17% of the region, with 26.14% facing risks of sustained decline. Conversely, 40.11% of the area displayed potential recovery trends, suggesting potential spatial divergence in future ecological trajectories. (4) Regional ecological dynamics are governed by a topographic-thermal compound driving mechanism. Elevation (DEM), temperature (TEMP), and surface shortwave radiation (SRAD) emerged as the dominant explanatory variables. Furthermore, dual-factor interactions exhibited significant enhancement effects, while the influence of anthropogenic factors was comparatively weak at the regional scale. These findings provide a scientific basis for the long-term monitoring of fragile alpine ecosystems and the strategic development of the Qiangtang National Park. Full article

Existing Google Earth Engine-based retrieval workflows often use fixed normalized difference vegetation index thresholds and coarse atmospheric water vapor inputs, which may limit their adaptability to regional surface and atmospheric conditions. This study evaluates how these two intermediate parameters influence Landsat 8 land surface temperature retrieval over northeastern Egypt using the generalized single-channel algorithm. Atmospheric water vapor was derived from MERRA-2 and NCEP reanalysis products, while land surface emissivity was estimated using ASTER Global Emissivity Dataset data and an NDVI-threshold framework. Reanalysis-derived total precipitable water was first compared with MODIS MOD05_L2. MERRA-2 showed a stronger correlation with MOD05_L2, whereas NCEP produced lower bias and root mean square error. The retrieved land surface temperature was then compared with the Landsat 8 Collection 2 Level 2 LST product as an internal consistency check. Using MERRA-2 reduced the overall root mean square error from 1.3977 K to 1.2615 K relative to NCEP, although it also increased the magnitude of the negative bias. A grid search of 24 normalized difference vegetation index threshold combinations showed that retrieval consistency was sensitive to threshold selection in cropland areas, while desert and built-up areas were largely insensitive. The best overall consistency with the Landsat product was obtained using a soil threshold of 0.20 and a vegetation threshold of 0.75, with a root mean square error of 1.2507 K and a bias of −0.7236 K. External validation at the Baseline Surface Radiation Network Gobabeb station showed a slight improvement when using MERRA-2 instead of NCEP, with root mean square error decreasing from 4.726 K to 4.441 K. Overall, the results show that intermediate parameter choices can affect Landsat land surface temperature retrieval, but the optimized settings should be interpreted as region-specific and relative to the Landsat product because independent validation remains limited. Full article

Land surface temperature (LST) is a core physical parameter that characterizes land surface processes and surface-atmosphere energy exchange. As the demand for high-accuracy LST products intensifies across diverse research domains—including climate science, hydrology, and ecosystem modeling—the systematic quantification of pixel-level retrieval uncertainties has become essential for generating long-term, consistent Climate Data Records (CDRs). However, existing studies predominantly emphasize algorithmic development or localized validation, with limited attention to systematic cross-site and long-term uncertainty assessments. This gap impedes a comprehensive understanding of the compositional structure and spatiotemporal variability of LST retrieval uncertainties under heterogeneous surface and atmospheric conditions. In this study, based on the improved generalized split-window (GSW) algorithm and error propagation theory, the total uncertainty (U_total) and its four primary components—algorithm uncertainty (U_a), land surface emissivity uncertainty (U_e), noise equivalent delta temperature uncertainty (U_n), and atmospheric water vapor uncertainty (U_w)—at the pixel level over long time series and across multiple sites are quantified. Our analysis spans a 21-year period (2003–2023) and encompasses multiple geographically distributed sites, utilizing high-quality Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared data—specifically MYD11_L2 and MOD11_L2 products—collocated at the locations of 15 globally distributed ground-based reference sites. These sites are used to represent diverse climatic regimes and land-cover conditions, rather than to provide point-scale “true” LST values for residual-based validation. Results show that the interquartile range (IQR) of U_total is consistently concentrated between 1.0 and 1.2 K, demonstrating long-term stability. Systematic differences in U_total are identified across sensor platforms and diurnal cycles: U_total for Aqua/MYD data (1.13–1.25 K) is marginally higher than that for Terra/MOD data (1.05–1.17 K); similarly, daytime U_total (1.08–1.23 K) is generally slightly elevated relative to nighttime U_total (1.05–1.18 K). The contributions of individual uncertainty components to U_total exhibit substantial variation, with mean relative contributions of 81.97%, 11.32%, 4.46%, and 2.25% for U_e, U_a, U_n, and U_w, respectively. The dominant drivers of U_total differ markedly across climatic regions: in arid regions, U_total is predominantly governed by U_e, termed “emissivity-dominated,” accounting for over 85% of the total; conversely, humid tropical regions exhibit a “surface-atmosphere co-influenced” regime, characterized by a reduced contribution from U_e and correspondingly enhanced contributions from U_a and U_w. Furthermore, U_total decreases with increasing total column water vapor (TCWV) (Pearson correlation coefficient r = −0.498; linear slope k = −0.0425 K/(g/cm²)), and increases with increasing viewing zenith angle (VZA) (r = 0.208; k = 0.0022 K/degree). While U_a, U_n, and U_w all increase with TCWV, U_e decreases. Full article

Plastic injection molding in cleanrooms involves high thermal loads and strict particle limits. The hot surfaces of the injection molding machine and peripherals increase the cooling demand of the heating, ventilation, and air conditioning system to an undefined amount. Moreover, the generation of buoyancy-driven plumes has the potential to disturb the cleanroom airflow around the injection mold, thereby risking cross contamination of the manufactured components. The present study quantifies the global heat load of injection molding machines in an ISO Class 7 cleanroom with a laminar flow microenvironment around the mold. Therefore, a measurement-based method to determine the heat load of a complete injection molding production cell is applied to a hydraulic and an electric machine. This method revealed that the heat load of the isolated machines is process-independent, whereas the total heat load of the complete production cell scales linearly with mold temperature. Moreover, the emitted heat to the cleanroom is considerable lower than the injection molding machine’s installed power. Secondly, the airflow regime and particle transport in the mold area are analyzed. This is achieved by means of schlieren visualization and aerosol measurements. The introduction of a modified Archimedes number, incorporating mold size and convective heat flux, has led to the observation of a correlation between flow regimes and the resulting particle load. This enables the selection of case-dependent FFU velocities that deviate from the conventional recommendation of an air speed of 0.45 m/s ± 20%. Despite the presence of a filter-fan unit, the particle load near the injection mold cavity increases for flow conditions that exceed a critical Archimedes number. Full article

Understanding the climate-driven range dynamics of the oriental hornet (Vespa orientalis) is essential for ecological risk assessment and biodiversity management. This study utilized Maximum Entropy (MaxEnt) modeling to estimate current and future (2050) habitat suitability across the Western Palearctic. The model demonstrated strong predictive performance, yielding a mean cross-validation AUC of 0.95 ± 0.01 and a TSS of 0.78 ± 0.02, indicating high stability and discriminatory capacity. Jackknife analysis and response curves identified temperature annual range (bio7) and annual precipitation (bio12) as the primary environmental drivers. The species exhibits a distinct preference for moderate thermal variability and balanced moisture regimes, while extreme summer heat (bio5) and warm winter conditions (bio11) impose significant constraints. Current projections identify a high-suitability core concentrated within the Mediterranean basin. By mid-century, projections indicate a spatial reorganization marked by localized gains mainly in the eastern part of the study region alongside suitability losses across North Africa and parts of southern Europe. Multivariate Environmental Similarity Surface (MESS) analysis confirmed high model transferability across most expansion zones, despite increased uncertainty in hyper-arid and high-altitude regions. These findings underscore the dynamic nature of the V. orientalis climatic niche and provide a critical baseline for proactive biosecurity and monitoring in emerging high-risk regions. Given the global decline in Hymenoptera diversity, this study provides timely insights into species-specific responses to climate change, supporting broader efforts in biodiversity conservation and ecological risk assessment. Full article

The Greater Caucasus is one of the most extensively glacierized mountain systems in mid-latitude Eurasia and has experienced substantial glacier retreat in recent decades. Continuous monitoring using high-resolution satellite observations is therefore essential for accurately quantifying ongoing and future changes. In this study, we present a new glacier inventory for 2025 derived from high-resolution (3 m) PlanetScope satellite imagery combined with topographic information from the 30 m Advanced Land Observing Satellite (ALOS) Global Digital Surface Model (2006–2011). A total of 101 cloud-free PlanetScope scenes, acquired primarily during August–September 2025, were manually delineated to ensure precise glacier boundary detection. Regional climatic data, including summer temperature and winter precipitation from the ERA5 reanalysis, were compiled to support interpretation of glacier changes since the 1960s. The new inventory identifies 2341 glaciers covering 964.0 ± 22.8 km² across the Greater Caucasus. Glacier distribution is highly uneven: most of the glacier-covered area is found in the Central Caucasus (730.2 ± 15.5 km²), whereas considerably smaller glacierized areas occur in the Western and Eastern sectors. Most glaciers are located on northern slopes (687.7 ± 16.0 km²), reflecting strong topographic and climatic asymmetry. Mean glacier elevations range from ~3300 to 3600 m a.s.l., increasing eastward in response to decreasing precipitation. Size-class analysis shows that small glaciers (<0.5 km²) dominate numerically, whereas a limited number of large valley glaciers (>5.0 km²) contribute disproportionately to total glacier area. Comparison with previous inventories indicates continued and accelerated glacier retreat, particularly since 2014, with a mean area loss rate of −1.8% yr⁻¹. These comparisons further show that a total of 965 glaciers (~122.9 km²) have become extinct across the Greater Caucasus since the 1960s. This trend is primarily driven by increasing summer temperatures and declining winter precipitation. This high-resolution inventory provides the most detailed glacier dataset currently available for the Greater Caucasus and establishes an updated benchmark for future glacier monitoring, climate change studies, and hydrological assessments. Full article

The adhesion between asphalt and aggregate significantly influences the durability and lifespan of road structures. This study employs molecular dynamics simulations to investigate the interface behavior between asphalt and aggregates with varying defect sizes under chloride salt solution immersion and ion infiltration (physical erosion without chemical reactions). The interfacial adhesion energy (E_int), relative ion concentration (R_C), mean square displacement (MSD), and hydrogen bond count were analyzed to assess the adhesion performance of asphalt at the defective aggregate interface. The effects of chloride concentration and temperature on adhesion were also examined. Results indicate that aggregate surface defects enhance local asphalt adhesion within the defect region, although larger defects reduce the global interfacial adhesion energy normalized by total area: the adhesion energy decreases from −417 kcal/mol (defect-free) to −315 kcal/mol (20 Å) and −277 kcal/mol (30 Å), with a reduction of 24–34%. Additionally, defects accelerate ion diffusion significantly, with diffusion coefficients of water and ions increasing by up to 69%, promoting chloride ion accumulation, which exacerbates erosion physical interface deterioration. Both elevated temperature and chloride concentration further accelerate this degradation physical interface weakening, with high temperatures causing severe interface damage: adhesion energy decreases by about 28% as temperature rises from 290 K to 340 K, and by 15% as NaCl concentration increases from 0% to 20%. These findings offer a theoretical foundation for understanding the adhesion mechanisms of the asphalt–aggregate interface under chloride erosion chloride ion infiltration and physical erosion and provide insights into enhancing chloride resistance to chloride ion infiltration of road materials. Full article

Using monthly surface air temperature observations from the CN05.1 dataset and simulations from 47 CMIP6 climate models, this study evaluates historical and future temperature changes over the Tibetan Plateau (TP). Observations reveal rapid warming during the historical period, with clear spatial heterogeneity characterized by relatively weaker warming in the southeastern Plateau and stronger warming elsewhere. CMIP6 models generally reproduce the historical warming trend but underestimate the observed warming magnitude in most seasons, and inter-model uncertainty is largest over the western Plateau. Future projections show a strong and robust positive relationship between TP warming and global mean temperature increase that is insensitive to the projection period, with a best-fit regression slope of approximately 1.36, indicating amplified warming over the Plateau relative to the global mean. The spatial patterns of future warming closely resemble those observed historically, suggesting that future changes largely represent an intensification of existing warming structures rather than a reorganization of spatial variability. In response to an additional 0.5 °C of global warming, the strongest temperature increases occur in autumn and winter, exceeding 0.8 °C across most regions, and the Plateau response strengthens with increasing global warming in winter, highlighting the elevated sensitivity and risk under incremental global temperature increases. Full article

Urban heat risk is intensifying globally, yet the relative contributions of climate warming and demographic restructuring to spatiotemporal risk change remain poorly understood, particularly in post-socialist cities experiencing simultaneous thermal intensification and population aging. This study develops a Heat Risk Population Index (HRPI) integrating satellite-derived land surface temperature, CERRA reanalysis air temperature, and census-based demographic sensitivity for 218 Zagreb neighborhood councils (2001–2024). A multi-scale analytical framework combining additive decomposition, enhanced partial correlations, and geographically weighted regression (GWR) was applied to disentangle the drivers of heat risk change. HRPI increased significantly across all neighborhood councils (mean ΔHRPI = 0.197, p < 0.001), with strong positive spatial autocorrelation (Moran’s I = 0.416). While air temperature change dominated the city-wide mean increase (72.1%), demographic sensitivity change explained the largest share of spatial variance across neighborhood councils (partial r = 0.677 vs. 0.524 for air temperature), driven by spatially heterogeneous demographic transitions—youth out-migration, aging-in-place in southeastern post-socialist estates, and gentrification in central districts. GWR substantially outperformed global OLS (ΔAICc = 60.1; Adj. R²: 0.649 → 0.816), with local demographic effect sizes varying fivefold across the city. These results demonstrate that heat risk drivers operate at distinct spatial scales: climate dominates city-wide magnitude while demographics determine spatial differentiation. Effective adaptation requires universal thermal interventions combined with spatially targeted demographic strategies in identified hotspot neighborhoods. The multi-scale framework is applicable to other post-socialist cities undergoing concurrent climate and demographic change. Full article

This study presents an application-oriented CFD–FEM integrated workflow for analyzing chamber-side field non-uniformity and kelp-side thermal response during infrared drying. A three-dimensional steady-state CFD model was first established to reconstruct the chamber temperature, airflow, and incident radiation fields under certain operating conditions. Numerical consistency was checked through residual convergence; monitored variables; and global mass balance, for which the net mass imbalance was 0.004077 kg s⁻¹. The reconstructed mid-plane fields were then processed in MATLAB to extract the mean values, extrema, and coefficients of variation, and a composite objective function was used to screen the tested operating conditions in terms of field uniformity, temperature band compliance, and overheating risk. The thermal loads obtained via CFD were subsequently mapped onto a kelp finite element model to simulate the transient surface temperature evolution. Among the tested cases, case01 yielded the lowest composite objective value (J = 0.4535); its mapped kelp response showed a mean surface temperature of 62.23 °C and a maximum temperature of 63.57 °C at the exported time step. The proposed framework is therefore suitable for thermal response assessment and operating condition screening, although determining the full drying behavior still requires coupling of moisture transfer and improved experimental validation. Full article

Sea ice classification is of fundamental importance for polar monitoring and global climate research. Global Navigation Satellite System Reflectometry (GNSS-R) has emerged as a frontier technology in polar remote sensing due to its high spatiotemporal resolution and cost-effectiveness. Based on BeiDou System Reflectometry (BDS-R) data acquired from the Fengyun-3E (FY-3E) satellite, this study introduces a classification approach that integrates multi-dimensional sea ice information. A comprehensive feature set was constructed by integrating the Spectral Entropy (SE) of the Normalized Integrated Delay Waveform (NIDW) First-order Differential Curve to characterize the oscillatory complexity of the trailing edge power decay process as a scattering dynamic property, the Root Mean Square height (RMS) to characterize the attenuation magnitude of scattering intensity arising from surface roughness and related factors as a scattering intensity attenuation property, and salinity (S) and L-band brightness temperature (TB) data from SMOS to describe dielectric and radiative properties. These novel features are combined with traditional GNSS-R features. After selecting the optimal feature set via an ablation study, the features were used to train a Random Forest (RF) classifier for sea ice classification. Validated against Ocean and Sea Ice Satellite Application Facility (OSI SAF) sea ice type products, the proposed method yielded an overall accuracy of 93.86% and a Kappa coefficient of 0.8061. The integration of multi-dimensional features notably improved the identification of Multi-Year Ice (MYI), achieving a Recall of 85.11% and an F1-score of 84.43%. These results indicate that the proposed multi-dimensional feature set provides an effective solution for GNSS-R-based sea ice classification. Full article