RoughNet: Mapping Arctic Sea Ice Roughness Using Diffusion-Based Super-Resolution of Satellite Imagery

A Synergistic Approach for High-Precision Lake Turbidity Retrieval Using ICESat-2 and Sentinel-2 Data

Mapping Submarine Sand Wave Bathymetry from Sentinel-2 Texture Using a Spatial-Sequential Deep Learning Model

Submarine sand waves are widespread on shallow continental shelves. Their complex morphology and potential mobility create challenges for engineering surveys, navigation safety, and seabed stability assessment. Multibeam surveys provide accurate bathymetry but are costly and spatially limited, whereas satellite-based methods offer broader coverage but remain challenging in complex sand wave fields. Here, we propose a spatial-sequential 2DCNN–LSTM model for retrieving submarine sand wave bathymetry from Sentinel-2 surface reflectance imagery. The model represents each target point as a sequence of local multispectral image patches, allowing convolutional layers to extract two-dimensional textural features and LSTM layers to learn profile-scale rhythmic continuity associated with sand wave morphology. The model was trained using multibeam bathymetry and applied to a large extrapolation area of approximately 4000 km2 on the Taiwan Banks. Evaluation on the large extrapolated area against in situ bathymetric data achieved a root mean square error (RMSE) of 3.78 m, a mean absolute error (MAE) of 2.99 m, and a mean relative error (MRE) of 9.1%. The results demonstrate that sand wave-induced optical textures can provide useful information for broad-scale bathymetric reconstruction, although model performance remains dependent on image texture visibility controlled by hydrodynamic, illumination, and atmospheric conditions. This framework offers a cost-effective approach for satellite-based monitoring of large submarine sand wave fields, providing a new perspective for engineering applications.

Synergistic Use of Time-Series Multispectral and Synthetic Aperture Radar Data for Adaptive Glacier Surface-State Mapping

Monitoring glacier surface wetness and near-surface facies evolution with high spatiotemporal resolution is important for characterizing seasonal melt conditions and supporting downstream glaciological modeling. However, current remote sensing methods are hindered by cloud contamination in optical data and ambiguities in SAR backscatter interpretation. In this study, a novel framework for automated glacier surface-state mapping is proposed by integrating Sentinel-1 SAR and Sentinel-2 optical imagery. Pixel-wise wet snow probability maps are generated using a convolutional neural network trained on multitemporal optical data, which then guides an adaptive thresholding scheme for SAR-based wet snow detection. Finally, the wet snow maps are refined through a postprocessing scheme that leverages temporal consistency and spatial segmentation, and classification stability is significantly enhanced. The proposed algorithm is evaluated over three glaciers on the Tibetan Plateau using carefully constructed remote-sensing reference labels. The results show agreement with the reference labels, with mean F1-scores of 0.849 for Shenshe Glacier, 0.811 for Laohugou Glacier No.12, and 0.858 for Bayi Glacier, with peak values exceeding 0.93 during mid-season observations. The results also indicate relatively stable agreement under varying signal conditions. This study provides a flexible and transferable strategy for mapping wet-snow extent, wet-snow timing, and glacier surface facies evolution, which can provide useful constraints for subsequent mass-balance and runoff modelling in complex mountainous terrain.

Comparing 3 m resolution snow cover downscaled from MODIS, VIIRS, and HLS using commercial satellite imagery and terrain information

Integrating meteorological forcings and satellite-derived observations to emulate snow water equivalent in the Colorado Rocky Mountains using a spatiotemporal deep learning model

Accurate estimates of snow water equivalent (SWE) are critical for hydrologic forecasting and water resource management in snow-dominated regions. While in situ observations, such as those from the United States Department of Agriculture snow telemetry (SNOTEL) network, provide direct measurements, their sparse spatial coverage limits their broader applicability. Numerical models such as Noah Multi-Parameterization (Noah-MP) provide spatially continuous SWE estimates but are computationally demanding. This study develops a deep learning–based emulator 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. Incorporating spatial coupling substantially improves performance relative to a pixel-wise Long Short-Term Memory baseline, increasing the fraction of grid cells with non-negative Nash–Sutcliffe Efficiency (NSE) by 15–27% during the test period. The emulator reproduces large-scale SWE patterns consistent with both Noah-MP and Snow Data Assimilation System (SNODAS) and maintains competitive NSE and root mean square error performance relative to SNOTEL observations. Seasonal sensitivity experiments yield high R 2 values up to 0.96 during accumulation and 0.97 during melt. Once trained, the emulator generates SWE predictions faster than the parent physics-based simulation, enabling rapid, high-resolution estimates that support real-time monitoring and ensemble forecasting.

Satellite Topographic Mapping in Complex Intertidal Wetlands: A Canopy-Height-Constrained Fusion of ICESat-2 Photon-Derived Structural Samples and Single-Phase Submeter Optical Imagery

High-resolution topographic mapping of intertidal wetlands is essential for geomorphic analysis, yet existing remote sensing methods often struggle with vegetation interference, dependence on dense time-series data, and limited representation of fine geomorphic features. We propose a canopy-height-constrained stratified cooperative inversion framework for the entire intertidal wetland, integrating single-phase submeter optical imagery (Jilin-1), spaceborne photon-counting light detection and ranging (LiDAR) Ice, Cloud, and Land Elevation Satellite 2 (ICESat-2), and machine learning. To accurately construct digital elevation model (DEM) and canopy height model (CHM) training samples in salt-marsh environments, we developed an ATL03 photon-classification workflow combining histogram-based control-point extraction and morphological refinement to generate these samples directly from ICESat-2 ATL03 photons. The retrieved CHM was then introduced as a structural constraint in the DEM retrieval model to support canopy-terrain signal decoupling in vegetated salt-marsh areas. A case study on Chongming Island, Shanghai, China, demonstrated that the DEM retrieval achieved high accuracy on the test set (R ${}^{2} =0.94$ , root-mean-squared error (RMSE) = 0.28 m) and maintained consistent performance against independent UAV-LiDAR validation data (R ${}^{2} = 0.53-0.77$ and RMSE = 0.34–0.53 m). The retrieved 0.5-m DEM reproduced regional elevation gradients, tidal-creek networks, and microtopographic variations across bare flats and vegetated marshes. Shapley additive explanation (SHAP) analysis showed that elevation retrieval over bare mudflats relied mainly on spectral predictors, whereas vegetated areas exhibited a complementary spectral-texture-CHM structure, with CHM consistently ranking as a mid-to-high predictor (fourth–seventh). This further supports the role of CHM as an effective structural constraint. By using only single-phase imagery and ATL03-derived DEM/CHM samples, the framework enables intertidal topographic retrieval that includes vegetated areas. It therefore provides an efficient and low-cost pathway for high-accuracy intertidal topographic monitoring under complex environmental conditions and limited image availability.