The MOD10A1 daily snow cover product offers a fine temporal resolution, which is particularly important for time-critical applications. Although the reliability of this product was mostly verified by ground measurements before use, the inadequate representativeness of point-scale observations may lead to errors in the assessment of MOD10A1. In this study, based on the Google Earth Engine, MOD10A1 was thoroughly evaluated by a total of 20,131 binary snow maps generated from 30 m Landsat imagery for the 2000-2016 snow seasons in three typical snow regions across China. The three typical snow regions included Northern Xinjiang, Northeast China and the Tibetan Plateau. In general, the accuracy of MOD10A1 under clear sky conditions is 86.5% based on ground observations from meteorological stations and 90.3% based on Landsat imagery. Compared with ground measurements, snow cover derived from Landsat images is in better agreement with MOD10A1 due to a better spatial match. The error analyses also indicate that 1) compared with land cover, topography has larger effects on the accuracy of MOD10A1 over the three snow regions with statistically significant negative relationships, particularly in Northern Xinjiang (R = -0.604, P < 0.05); and 2) MOD10A1 shows a relatively lower accuracy of 81.8% from 2006 to 2008 due to the decrease in snow depth during this period (R = 0.58, P < 0.05) based on the Mann-Kendall test.

Snowmelt is a natural water resource, and its distribution is essential for understanding regional climate change and hydrological cycle. In this study, using dual-polarized C-band Sentinel-1 SAR data, we propose a new wet snow detection algorithm that considers the effects of radar incidence angle, land cover types and topography for snow regions where prairie snow is the main snow cover type. We quantitatively evaluate the performance of the proposed method by optical-derived snow maps. The results show that (1) the overall accuracy of the proposed method is 80.8% (dry snow reference) and 79.0% (snow-free reference); while using a conventional threshold of −2 dB, the accuracy is 32.1% and 43.5%, respectively; (2) −2 dB can only be applied on the ratio image calculated by the snow-free reference to extract wet snow in cropland; (3) adding topographic information greatly improves the identification accuracy, especially for grassland and barren with higher topographic complexity.

Snow density is an important variable in snowpack research. The comprehensive applicability evaluation of the snow density datasets is a prerequisite of these datasets for their applications in hydrology processes and climate change, as well as in snow equivalent water retrieval algorithms. In this letter, the applicability of three snow density datasets, including European ReAnalysis (ERA)-Interim, ERA5, and the newly released ERA5-Land datasets, was first assessed using two ground evaluation datasets with different land covers from seven snow survey courses and four densely sampled networks in China. The results show that the ERA-Interim dataset significantly overestimates snow density during the entire snow season, with an overall root mean square error (RMSE) larger than 112 kg/m3, and lacks temporal dynamics. The ERA5 and ERA5-Land datasets are generally in good agreement with the ground measurements in China. The averaged RMSEs of the ERA5 dataset are 56.2 kg/m3 against snow course sites and 28.3 kg/m3 versus the densely sampled measurements, and those of the ERA5-Land dataset are 56.6 and 28.4 kg/m3, respectively. However, the ERA5 and ERA5-Land datasets still underestimate snow density over time, especially for the middle and late snow seasons. These new findings are expected to provide valuable feedback to model developers to further enhance the accuracy of snow density datasets.

Snow cover is an important component of the cryosphere. Clouds have a large influence on optical remote sensing satellites when recognizing snow cover. Geostationary satellites, due to their high-frequency observations over coverage areas, can effectively compensate for the drawback of snow cover recognition from polar orbit optical satellites under cloud-covered conditions. However, past geostationary satellites have relatively few band settings to produce sensitive factors for snow cover recognition. The FY-4A Advanced Geostationary Radiation Imager (AGRI) satellite has the advantage of high temporal resolution with a wealth of bands, which highlights its potential in reducing the impact of clouds and accurately obtaining snow cover information. Based on the advantages of FY-4A AGRI data and the flow characteristics of clouds, we developed an improved maximum brightness temperature image synthesis algorithm, which can greatly reduce the probability of cloud and snow cover misclassification. Combining the features of FY-4A AGRI data, we reorganized the snow cover recognition factor and developed a new snow cover recognition method. The results show that the proposed method can reduce cloud cover by 57.172% compared with MOD10A1 data. After evaluating the proposed method using meteorological ground observation datasets and MOD10A1 data, we found that the overall accuracy of the proposed method can reach 94.11% and 98.55%, respectively, and the F-score (FS) can reach 73.05% and 85.40%, respectively.

Distribution of snow and its melting is a critical factor affecting local weather, avalanche and flood forecasting, livelihood of people residing, and hydropower production. Most of the existing dry and wet snow identification methods were based on expensive quad-pol SAR with finite generalizability, while dual-pol SAR with larger coverage, longer time series and open availability has more advantages. In this study, an unsupervised algorithm for dry and wet snow discrimination, NSAE-WFCM, is proposed based on a variety of polarimetric features derived from H-α decomposition in dual-pol mode using C-band Sentinel-1 SAR data. NSAE-WFCM constructs a deep training network using the pixel neighborhood-based sparse autoencoder (NSAE) to optimize polarimetric parameters, and inputs reconstructed features with different weights into feature-weighted fuzzy C-means clustering (WFCM) to distinguish dry and wet snow for each underlying surface. Ground observation was carried out during the snow melting period of March 2021 in Altay, China, to validate dual-pol NSAE-WFCM method with an overall accuracy and kappa coefficient of 88.8% and 0.68, respectively. The results show that NSAE-WFCM’s accuracy is similar to that of the quad-pol SAR-based dry and wet snow result (90.0%), and significantly better than that of previously published approaches extended to dual-pol SAR, such as SVM (76.7%), H-α-Wishart (65.5%), SPAN-based threshold method (51.7%), and wet snow-based method (43.1%). Therefore, the NSAE-WFCM algorithm improves the ability to classify wet and dry snow based on dual-pol polarimetric features, overcomes the high dependence of existing methods on quad-pol SAR data, and reduces manual interpretation by using unsupervised clustering.

Melting snow is a sensitive indicator of regional climate change and snowmelt runoff. Optical remote sensing offers an effective tool to delineate the extent of snow cover, but its application is limited by sky conditions and illumination. In contrast, synthetic aperture radar (SAR) has the advantages of high penetration and can obtain the bulk properties of snowpack, such as snow wetness, in addition to the snow-covered area (SCA). The widely used algorithm based on the multitemporal backscattering coefficient can delineate wet snow and further estimate dry snow cover by empirical rules. However, employing a fixed threshold on wet snow extraction and only considering altitude in dry snow estimation lead to the misclassification of snow cover. In this study, an algorithm was developed for dry and wet snow discrimination based on C-band dual-polarization SAR data. First, the adaptive threshold obtained by the Otsu thresholding method (OTSU) was implemented on the ratio image to improve wet snow extraction, and then topographic information, including elevation, slope and aspect, was added to further infer dry snow cover. Snow field observations were carried out in March 2021 and February 2017 to evaluate the effectiveness of the presented method in typical snow regions with different snowpack characteristics. Compared with using a single equation with a fixed threshold (57.9% and 56.5%, respectively), the overall accuracy of the novel approach in prairie and alpine snow areas was 84.2% and 73.9%, respectively. The results show that considering the adaptive threshold and topography offers superior performance in identifying dry and wet snow.