Microwave scatterometers are capable of acquiring land surface backscattering coefficients day and night under all-weather conditions, offering advantages for snow cover monitoring. However, the relatively low spatial resolution of traditional scatterometer data limits their application in the fine-scale monitoring of snow cover distribution. To improve the spatial representation of snow cover and mitigate mixed-pixel effects in complex spring snowmelt scenarios, this study proposes an adaptive bilateral filtering scatterometer image reconstruction (SIR-ABF) algorithm based on the rotating fan-beam scanning characteristics of the FengYun-3E Wind Radar (FY-3E WindRAD). The Ku-band data of FY-3E WindRAD were reconstructed from the original 10 km resolution to an enhanced resolution of 3.125 km. Furthermore, by integrating the reconstructed scatterometer backscatter with multi-source auxiliary data, an optimal feature subset was determined through a feature selection strategy that considers both feature-label correlation and inter-feature multicollinearity. Finally, the best feature subset was combined with four machine learning (ML) models for snow cover classification. The results indicate that the Support Vector Machine (SVM) achieved the best performance, yielding an Overall Accuracy (OA), Macro-F1, and Kappa coefficient (Kc) of 91.64%, 86.47%, and 0.730, respectively. Compared with the snow cover classification results derived from the original 10 km Ku-band data, the 3.125 km reconstructed data provided more detailed spatial information and better characterized fragmented snow patches and snow transition boundaries. Further comparison with existing snow cover products demonstrated the spatial consistency and continuity of the proposed classification results, highlighting the potential of high-resolution scatterometer data for fine-scale snow cover monitoring during the spring snowmelt period in Northeast China.
It is shown that high-resolution sea ice topography can be reconstructed directly from optical satellite imagery using a conditional diffusion framework, providing a scalable pathway for high-resolution sea ice mapping and roughness estimation in data-sparse environments.
Quantifying the spatiotemporal distribution of mountain snowpack is critical for hydrology, as it represents a vital freshwater resource for over a billion people. While C-band synthetic aperture radar (SAR) from Sentinel-1 provides high-resolution estimates over mountains, its use for daily snow monitoring is hindered by infrequent revisits and signal attenuation in wet snow. To address these limitations, this work proposes the adaptive snow-phenology shape model fitting (ASP-SMF), a physically constrained fusion algorithm that generates all-weather continuous daily 500-m snow-depth maps for Northern Hemisphere mountains. Designed for data-scarce regions, ASP-SMF integrates Sentinel-1, ERA5-Land, and Interactive Multisensor Snow and Ice Mapping System (IMS) data independently of in situ calibration. The algorithm first adaptively segments the highly dynamic snow time series into three distinct phenological phases using physical rules of snowfall and snowmelt. Subsequently, it utilizes Sentinel-1 retrievals and IMS snow-free constraints to correct the temporal, amplitude, and offset biases of the reanalysis data via a robust reduced major axis (RMA) regression and uncertainty-aware sequential least-squares programming (SLSQP) optimization. Comprehensive validation against 565 sites and 55 airborne LiDAR/photogrammetry surveys (2017–2021) demonstrates that ASP-SMF: 1) reduces errors relative to Sentinel-1 under dry snow conditions, reducing root-mean-square error (RMSE) by 26% (from 0.27 to 0.20 m) and increasing R2 from 0.49 to 0.72; 2) exhibits pronounced improvement under wet snow conditions, reducing RMSE by 23% (from 0.61 to 0.47 m) and boosting R2 by 145% (from 0.22 to 0.54); and 3) shows comparable performance to the SNOw Data Assimilation System (SNODAS) product without in situ data assimilation, with a higher Kling–Gupta efficiency (KGE) (0.57 versus 0.37) against Airborne Snow Observatory (ASO) LiDAR in the Rocky Mountains. Critically, this independence from in situ inputs enables ASP-SMF to deliver spatiotemporally consistent snow monitoring in ungauged regions, providing a useful framework for high-resolution assessment of mountain snow water resources.
A deep learning–based emulator is developed to reproduce SWE simulated by Noah-MP using a Convolutional Long Short-Term Memory (ConvLSTM) network trained on meteorological forcings from the Weather Research and Forecasting model and augmented with static physiographic variables and remotely sensed snow cover and leaf area index data.
Accurately estimating Snow Water Equivalent (SWE) in glacierized regions is critical for glacier modeling and mass balance analysis yet remains challenging due to sparse observations and uncertainties in precipitation products. The objectives are twofold: (a) assess how well three widely used precipitation datasets—Stage IV (a radar–gauge composite product), Integrated Multi‐satellite Retrievals for GPM version 07 (IMERG V07, a satellite‐based global product), and ERA5 (a global atmospheric reanalysis)—represent end‐of‐season SWE over seven Alaskan glaciers across two winter seasons, using airborne radar SWE retrievals (ARBN) from NASA's Operation IceBridge campaign as the reference, and (b) determine whether combining precipitation with auxiliary variables via machine learning (here XGBoost) modeling can reliably predict SWE beyond airborne coverage. Results reveal systematic biases: Stage IV generally overestimates SWE, IMERG V07 underestimates it, and ERA5 aligns most closely with ARBN. Model performance depends strongly on training data representativeness, with significant discrepancies arising when training and testing SWE distributions diverge. The most effective framework integrates IMERG V07, ERA5 and key auxiliary variables–snowfall fraction, total precipitable water and 2‐m air temperature–with IMERG V07 and ERA5 found to be the dominant predictors. Nevertheless, accuracy is constrained by sparse in situ measurements and the paucity of high‐resolution data in complex terrain. These findings underscore the need for high‐quality targeted observations and integrated strategies that leverage diverse precipitation data and representative training data sets to advance SWE estimation and assessment in cryospheric environments.
Lake ice and snow cover on the Tibetan Plateau play a critical role in regulating lake-surface radiative, thermodynamic, and hydrological processes, yet accurate monitoring remains challenging at moderate spatial resolutions. Over frozen lakes, widely used snow products (e.g., MOD10A1) frequently misclassify lake ice as snow and cannot simultaneously retrieve subpixel fractional ice cover (FIC) and fractional snow cover (FSC), leading to systematic biases in snow estimation and surface albedo. To address this limitation, we develop a retrieval framework that explicitly resolves lake ice–snow ambiguity through simultaneous subpixel estimation of lake ice and snow fractions by integrating multisource remote sensing observations. High-resolution Sentinel-2 data are used to construct subpixel reference fractions, which are fused with daily moderate-resolution imaging spectroradiometer (MODIS) observations within a physics-informed extreme gradient boosting (XGBoost) regression framework incorporating spectral, thermal, and spatiotemporal information. Model transferability is rigorously evaluated using spatially independent lakes across the Tibetan Plateau. Results demonstrate robust generalization across independent lakes, achieving a root-mean-square error (RMSE) of 0.122 for both FSC and FIC. Compared with MOD10A1, the proposed approach reduces snow-cover RMSE by approximately 40% and substantially improves consistency with Sentinel-2 reference data. Physically interpretable predictors mitigate ice–snow spectral confusion and reduce albedo overestimation in conventional snow products. By explicitly differentiating lake ice and snow at the subpixel scale, this framework provides a physically consistent approach for characterizing lake–atmosphere interactions, with strong potential for advancing hydrological modeling and generating long-term fractional ice and snow datasets over the Tibetan Plateau.