High-resolution Synthetic Aperture Radar (SAR), as an efficient Earth observation technology, can be used as a complementary means of observation for snow depth (SD) and can address the spatial heterogeneity of mountain snow. However, there is still uncertainty in the SD retrieval algorithm based on SAR data, due to soil surface scattering. The aim of this study is to quantify the impact of soil signals on the SD retrieval method based on the cross-ratio (CR) of high-spatial resolution SAR images. Utilizing ascending Sentinel-1 observation data during the period from November 2016 to March 2020 and a CR method based on VH- and VV-polarization, we quantitatively analyzed the CR variability characteristics of rock and soil areas within typical thick snow study areas in the Northern Hemisphere from temporal and spatial perspectives. The correlation analysis demonstrated that the CR signal in rock areas at a daily timescale shows a strong correlation (mean value > 0.60) with snow depth. Furthermore, the soil areas are more influenced by freeze-thaw cycles, such that the monthly CR changes showed no or negative trend during the snow accumulation period. This study highlights the complexity of the physical mechanisms of snow scattering during winter processes and the influencing factors that cause uncertainty in the SD retrieval, which help to promote the development of high-spatial resolution C-band data for snow characterization applications.

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.

Total suspended matter (TSM) is one of the most widely used water quality parameters, which can influence the light transmission process, planktonic algae, and ecological health. A comprehensive field expedition aiming at water quality assessment was conducted for Lake Qinghai in September 2019. The in-situ measurements were used to support the calibration and validation of TSM concentration using Landsat images. A regional empirical model was established using the top-of-atmosphere (TOA) radiance of Landsat image data at the red band with a wavelength range of 640–670 nm. The coefficient of determination (R²), mean relative error (MRE), and root mean square error (RMSE) of the TSM estimation model were 0.81, 17.91%, and 0.61 mg/L, respectively. The model was further applied to 87 images during the periods from 1986 to 2020. A significant correlation was found between TSM concentration and daily wind speed (r = 0.74, p < 0.01, n = 87), which revealed the dominance of wind speed on TSM concentration. In addition, hydrological changes also had a significant influence on TSM variations of lake estuaries.

The accurate identification of precipitation types is very important for understanding the hydrological processes in cold regions. Existing identification methods have been established based on daily precipitation and meteorological data, which cannot match the high temporal resolution (such as hourly) simulations of hydrological processes. Based on the minutely surface meteorological data in the QTP from 2012 to 2021, we established three sub-models of the dynamic threshold method with wet-bulb temperature (T_w) and three sub-models of the frequency threshold method with air temperature (T_a) for distinguishing among precipitation types. The results revealed that the mean accuracy (ACC) of the three precipitation types was 0.86, and that these models provided a refined and accurate precipitation identification performance for the Qinghai–Tibet Plateau (QTP). However, these models performed well in the identification of rain and snowfall but performed poorly in the identification of sleet. In addition, the smaller the time scale and regional scales, the better the identification rate. In particular, snowfall is overestimated when daily precipitation-type separation thresholds are input into hourly or minute hydrological models. Therefore, to improve simulation performance, it is important to develop multi-temporal scale precipitation-type partitioning models, take regional variations into account when setting temperature thresholds, and conduct analyses at the finest possible time resolutions to minimize scale-related uncertainties.

High-resolution optical remote sensing imagery plays a crucial role in monitoring the Earth’s surface. However, cloud obstruction and spectral confusion between clouds and snow significantly compromise data quality and application reliability, leading to persistent cloud overestimation in optical remote sensing products. To address this challenge, this study developed an enhanced multi-threshold cloud detection algorithm based on AVHRR surface reflectance data, which incorporates dynamic threshold optimization within a multi-level decision tree framework. Utilizing Landsat 5 SR as reference data, the algorithm demonstrated superior cloud-snow discrimination capability, achieving an overall accuracy (OA) of 82.08%, with the user’s accuracy (UA) and F-score reaching 79.41% and 82.55%. Comparative evaluation demonstrates that the proposed algorithm outperforms two existing algorithms, with OA improvements of 17.42% and 7.93%, respectively. A particularly notable enhancement is the significant reduction in cloud misidentification, as reflected by UA increases of 21.02% and 13.21%. These improvements are most pronounced in high-altitude mountainous regions with snow cover. The algorithm maintains computational efficiency while providing reliable cloud masking, thereby offering enhanced support for snow cover monitoring and broader environmental applications.