In this study, we classified the variability in snow cover persistence across China by using a novel method; continuous snow cover days and variability of snow cover were used as the evaluation indicators based on a long-term Advanced Very High Resolution Radiometer (AVHRR) snow cover extent (SCE) product. The product has been generated by the snow research team in the Northwest Institute of Eco-Environment and Resources (NIEER), Chinese Academy of Sciences. There were obvious differences in snow cover classification in three snow cover areas (northern Xinjiang, northeast China, and the Tibetan Plateau): northern Xinjiang was dominated by persistent snow cover, most regions of northeast China were covered by persistent and periodic variable snow cover. There was the most abundant snow cover classification in the Tibetan Plateau. The extents of persistent and periodic variable snow cover were gradually shrinking due to rising temperatures and decreasing snowfall during 1981–2019. In contrast, non-periodic variable snow cover areas increased significantly. This method takes into account the stability, continuity, and variability of snow cover, and better captures the characteristics and changes of snow cover across China. Based on our research, we found that snow disasters in ephemeral-type (belong to non-periodic variable snow cover) regions cannot be well prevented because of the unfixed snow cover timing. Therefore, we recommend that monitoring and forecasting of snow cover in these snow cover regions should be strengthened.

Lake ice phenology serves as a sensitive indicator of climate change in the lake-rich Northeast China. In this study, the freeze-up date (FUD), break-up date (BUD), and ice cover duration (ICD) of 31 lakes were extracted from a time series of the land water surface temperature (LWST) derived from the combined MOD11A1 and MYD11A1 products for the hydrological years 2001 to 2021. Our analysis showed a high correlation between the ice phenology measures derived by our study and those provided by hydrological records (R² of 0.89) and public datasets (R² > 0.7). There was a notable coherence in lake ice phenology in Northeast China, with a trend in later freeze-up (0.21 days/year) and earlier break-up (0.19 days/year) dates, resulting in shorter ice cover duration (0.50 days/year). The lake ice phenology of freshwater lakes exhibited a faster rate of change compared to saltwater lakes during the period from HY2001 to HY2020. We used redundancy analysis and correlation analysis to study the relationships between the LWST and lake ice phenology with various influencing factors, including lake properties, local climate factors, and atmospheric circulation. Solar radiation, latitude, and air temperature were found to be the primary factors. The FUD was more closely related to lake characteristics, while the BUD was linked to local climate factors. The large-scale oscillations were found to influence the changes in lake ice phenology via the coupled influence of air temperature and precipitation. The Antarctic Oscillation and North Atlantic Oscillation correlate more with LWST in winter, and the Arctic Oscillation correlates more with the ICD.

The launch of Sentinel-1 satellite introduces a novel approach for synthetic aperture radar (SAR) interferometry (InSAR). However, its capabilities for topographic mapping are reportedly limited. It is very challenging to create high-quality digital elevation models (DEMs) from InSAR data in mountain area. The main goal of the study was to generate a new high-quality DEM by combining Sentinel-1A and Sentinel-1B data with a short temporal baseline and multiple perpendicular baselines from 43 to 143 m. Five DEMs were produced from five interferometric pairs. The five DEMs were fused to generate a more reliable fused DEM. The performance of the new DEMs was evaluated against ICESat-2/ATL06 product and global DEM products (NASADEM, advanced spaceborne thermal emission and reflection radiometer (ASTER) global digital elevation model (GDEM), and advanced land observing satellite ALOS World 3D-30 m). The results showed that all Sentinel-1 DEMs performed better than ASTER GDEM, four of DEMs had higher accuracies than NASADEM. The fused DEM with the vertical error of 9.08 m for steep terrain (slope>20°) revealed higher accuracy than three global DEMs. The accuracy of DEM was related to terrain slope and land cover type. The accuracies of DEMs decreased as slope increased. The DEMs in glacier area revealed higher errors than those in bare rock. Besides, there was no clear relationship between the perpendicular baseline and vertical accuracy of DEM. The interferometric pair with the shortest baseline (43 m) produced the worst quality DEM, while the interferometric pair with slightly shorter baseline (69 m) produced the highest quality DEM. The study revealed the outstanding accuracy of the new DEM product, which is very valuable data for local glacier research.

Dust deposited on the surface of snow and glaciers can significantly reduce the snow and ice albedo and accelerate melting. Manual observations of the dust mass concentration (DMC) on snow and glacier surfaces are routinely performed at many locations worldwide. However, snow and ice surface DMC monitoring methods based on remote sensing data still face challenges. This study presents a new retrieval scheme for estimating dust load on snow-covered surfaces from MODIS and VIIRS in Northeast and Northwest China that utilizes a classical snow radiative transfer model. Our results indicate that the coefficient of variation (CV) of DMC retrieved from in situ measurements of snow spectral reflectance is 4%, which is within a +4% difference compared to DMC observed in snow and ice samples. Estimating atmospheric dust pollutants content deposited on snow surfaces based on satellite remote sensing observations is feasible. In Northwest China, the root mean square error (RMSE) of the DMC values retrieved from VNP09GA data is 9.78 ppm, while that of the DMC values retrieved from MOD09GA data is 13.74 ppm. In Northeast China, the RMSE of the DMC values retrieved from VNP09GA data is 73.98 ppm, while that of the DMC values retrieved from MOD09GA data is 184.32 ppm. The research results can realize continuous monitoring of the atmospheric dust pollutants deposited on snow surfaces, which is of great practical significance to understanding and studying the pollution process of atmospheric dust on snow.

The coarse sediment source area in the middle reaches of Yellow River is most harmful to the main channel of the lower reaches of the Yellow River. Defining the specific scope and strengthening the regional soil erosion control are the key to mitigating sedimentation of the lower reaches of the river. This dataset is produced by using visual interpretation from remote sensing images based on the research results of boundary delimitation of the high and coarse sediment yield area and centralized source area of coarse sediment in the middle reaches of the Yellow River. This dataset basically reflects the specific locations of the two areas, and can be used as the basic data for the water and soil loss control in the middle reaches of Yellow River.