The snow cover distribution is crucial to Earth's climate and hydrological systems. Snow cover maps from Moderate Resolution Imaging Spectroradiometer (MODIS) are widely utilized in climate research and water-source management. Three MODIS-derived fractional snow cover (FSC) products, namely, MOD10A1 version 6, MODSCAG, and MODAGE, are assessed to evaluate the accuracy of FSC data over the Tibetan Plateau from May 2013 to April 2015. A Landsat-8/ Operational Land Imager (OLI) snow map that is based on linear spectral mixture analysis is regarded as the “reference dataset.” A total of 149 OLI images that span the Tibetan Plateau are used. The performance of these three datasets is compared to the “reference dataset” by using binary and fractional metrics over forest, grass, and bare soil, and in the Himalaya region. The binary classification assessment reveals that larger local illumination angles and vegetation coverage at the snow boundaries resulted in omission errors in MOD10A1, and snow patchiness caused this model to miss snow. MODSCAG correctly identified snow pixels, but overestimated snow at the snow boundaries and at local illumination angles that exceeded 30°. MODAGE precisely recognized snow-free areas, but underestimated snow coverage because of snow patchiness. MODSCAG and MODAGE produced smaller root mean square errors (RMSE) than MOD10A1, likely because spectral mixture analysis is superior to the normalized difference snow index-based empirical method. The behaviors of these products differed at various spatial scales. The RMSE significantly declined with decreasing spatial resolution from 500 m to 2 km. However, when aggregated to 5 km, the RMSE increased because fewer pixels were involved in the calculation.

Launched on 15 November 2017, China’s FengYun-3D (FY-3D) has taken over prime operational weather service from the aging FengYun-3B (FY-3B). Rather than directly implementing an FY-3B operational snow depth retrieval algorithm on FY-3D, we investigated this and four other well-known snow depth algorithms with respect to regional uncertainties in China. Applicable to various passive microwave sensors, these four snow depth algorithms are the Environmental and Ecological Science Data Centre of Western China (WESTDC) algorithm, the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) algorithm, the Chang algorithm, and the Foster algorithm. Among these algorithms, validation results indicate that FY-3B and WESTDC perform better than the others. However, these two algorithms often result in considerable underestimation for deep snowpack (greater than 20 cm), while the other three persistently overestimate snow depth, probably because of their poor representation of snowpack characteristics in China. To overcome the retrieval errors that occur under deep snowpack conditions without sacrificing performance under relatively thin snowpack conditions, we developed an empirical snow depth retrieval algorithm suite for the FY-3D satellite. Independent evaluation using weather station observations in 2014 and 2015 demonstrates that the FY-3D snow depth algorithm’s root mean square error (RMSE) and bias are 6.6 cm and 0.2 cm, respectively, and it has advantages over other similar algorithms.

Daily fractional snow cover (FSC) products derived from optical sensors onboard low Earth orbit (LEO) satellites are often discontinuous, primarily due to prevalent cloud cover. To map the daily cloud-reduced FSC over China, we utilized clear-sky multichannel observations from the first-generation Chinese geostationary orbit (GEO) satellites (namely, the FY-2 series) by taking advantage of their high temporal resolution. The method proposed in this study combines a newly developed binary snow cover detection algorithm designed for the Visible and Infrared Spin Scan Radiometer (VISSR) onboard FY-2F with a simple linear spectral mixture technique applied to the visible (VIS) band. This method relies upon full snow cover and snow-free end-members to estimate the daily FSC. The FY-2E/F VISSR FSC maps of China were compared with the Moderate Resolution Imaging Spectroradiometer (MODIS) FSC data based on the multiple end-member spectral mixture analysis (MESMA), and with Landsat-8 Operational Land Imager (OLI) FSC maps based on the SNOWMAP approach. The FY-2E/F VISSR FSC maps, which demonstrate a lower cloud coverage, exhibit the root mean squared errors (RMSEs) of 0.20/0.19 compared with the MODIS FSC data. When validated against the Landsat-8 OLI FSC data, the FY-2E/F VISSR FSC maps, which display overall accuracies that can reach 0.92, have an RMSE of 0.18~0.29 with R² values ranging from 0.46 to 0.80.

Ongoing information on snow and its extent is critical for understanding global water and energy cycles. Passive microwave data have been widely used in snow cover mapping given their long-time observation capabilities under all-weather conditions. However, assessments of different passive microwave (PMW) snow cover area (SCA) mapping algorithms have rarely been reported, especially in China. In this study, the performances of seven PMW SCA mapping algorithms were tested using in situ snow depth measurements and a one-kilometer Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover product over China. The selected algorithms are the FY3 algorithm, Grody’s algorithm, the South China algorithm, Kelly’s algorithm, Singh’s algorithm, Hall’s algorithm and Neal’s algorithm. During the test period, most algorithms performed reasonably well. The overall accuracy of all algorithms is higher than 0.895 against in situ observations and higher than 0.713 against the IMS product. In general, Singh’s algorithm, Hall’s algorithm and Neal’s algorithm had poor performance during the test. Their misclassification errors were larger than those of the remaining algorithms. Grody’s algorithm, the South China algorithm and Kelly’s algorithm had higher positive predictive values and lower omission errors than those of the others. The errors of these three algorithms were mainly caused by variations in commission errors. Comparing to Grody’s algorithm, the South China algorithm and Kelly’s algorithm, the FY3 algorithm presented a conservative snow cover estimation to balance the problem between snow identification and overestimation. As a result, the overall accuracy of the FY3 algorithm was the highest of all the tested algorithms. The accuracy of all algorithms tended to decline with a decreased snow cover fraction as well as SD < 5 cm. All tested algorithms have severe omission errors over barren land and grasslands. The results shown in this study contribute to ongoing efforts to improve the performance and applicability of PMW SCA algorithms.

Daily snow-covered area retrieval using the imagery in solar reflective bands often encounters extensive data gaps caused by cloud obscuration. With the inception of geostationary satellites carrying advanced multispectral imagers of high temporal resolution, such as Japan’s geostationary weather satellite Himawari–8, considerable progress can now be made towards spatially-complete estimation of daily snow-covered area. We developed a dynamic snow index (normalized difference snow index for vegetation-free background and normalized difference forest–snow index for vegetation background) fractional snow cover estimation method using Himawari–8 Advanced Himawari Imager (AHI) observations of the Tibetan Plateau. This method estimates fractional snow cover with the pixel-by-pixel linear relationship of snow index observations acquired under snow-free and snow-covered conditions. To achieve reliable snow-covered area mapping with minimal cloud contamination, the daily fractional snow cover can be represented as the composite of the high temporal resolution fractional snow cover estimates during daytime. The comparison against reference fractional snow cover data from Landsat–8 Operational Land Imager (OLI) showed that the root–mean–square error (RMSE) of the Himawari–8 AHI fractional snow cover ranged from 0.07 to 0.16, and that the coefficient of determination (R²) reached 0.81–0.96. Results from the 2015/2016 and 2016/2017 winters indicated that the daily composite of Himawari–8 observations obtained a 14% cloud percentage over the Tibetan Plateau, which was less than the cloud percentage (27%) from the combination of Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua.

Satellite microwave radiometer measurements have been used to extract snow depth information for approximately three to four decades. However, the coarse spatial resolution of satellite microwave radiobrightness observations affects the snow depth retrieval accuracy and hinders the application of snow depth data in studies on water, energy, and carbon cycles. Therefore, snow depth measurements require better spatial resolution to improve the retrieval accuracy and enhance the resolution of the data. In this paper, an improved linear unmixing method is proposed to improve the accuracy of existing methods and downscale brightness temperatures from satellite observations to improve snow depth estimates. Tests conducted on a dataset consisting of both simulated and satellite data show the effectiveness of the proposed method. The unmixing method is then applied to FengYun-3B satellite/microwave radiation imager measurements for snow depth retrievals, and the resulting snow depths are compared with weather station observations from January to February 2011. The results show that the snow depth estimated from downscaled brightness temperatures performs better than the original data and presents higher correlations and lower root mean square errors.