A comprehensive and hemispheric-scale snow cover and snow depth analysis is a prerequisite for all related processes and interactions investigation on regional and global surface energy and water balance, weather and climate, hydrological processes, and water resources. However, such studies were limited by the lack of data products and/or valid snow retrieval algorithms. The overall objective of this study is to investigate the variation characteristics of snow depth across the Northern Hemisphere from 1992 to 2016. We developed long-term Northern Hemisphere daily snow depth (NHSnow) datasets from passive microwave remote sensing data using the support vector regression (SVR) snow depth retrieval algorithm. NHSnow is evaluated, along with GlobSnow and ERA-Interim/Land, for its accuracy across the Northern Hemisphere against meteorological station snow depth measurements. The results show that NHSnow performs comparably well with a relatively high accuracy for snow depth with a bias of −0.6 cm, mean absolute error of 16 cm, and root mean square error of 20 cm when benchmarked against the station snow depth measurements. The analysis results show that annual average snow depth decreased by 0.06 cm per year from 1992 to 2016. In the three seasons (autumn, winter, and spring), the areas with a significant decreasing trend of seasonal maximum snow depth are larger than those with a significant increasing trend. Additionally, snow cover days decreased at the rate of 0.99 day per year during 1992–2016. This study presents that the variation trends of snow cover days are, in part, not consistent with the variation trends of the annual average snow depth, of which approximately 20% of the snow cover areas show the completely opposite variation trends for these two indexes over the study period. This study provides a new perspective in snow depth variation analysis, and shows that rapid changes in snow depth have been occurring since the beginning of the 21st century, accompanied by dramatic climate warming.

The dynamic characteristics of seasonal snow cover are critical for hydrology management, the climate system, and the ecosystem functions. Optical satellite remote sensing has proven to be an effective tool for monitoring global and regional variations in snow cover. However, accurately capturing the characteristics of snow dynamics at a finer spatiotemporal resolution continues to be problematic as observations from optical satellite sensors are greatly impacted by clouds and solar illumination. Traditional methods of mapping snow cover from passive microwave data only provide binary information at a spatial resolution of 25 km. This innovative study applies the random forest regression technique to enhanced-resolution passive microwave brightness temperature data (6.25 km) to estimate fractional snow cover over North America in winter months (January and February). Many influential factors, including land cover, topography, and location information, were incorporated into the retrieval models. Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products between 2008 and 2017 were used to create the reference fractional snow cover data as the “true” observations in this study. Although overestimating and underestimating around two extreme values of fractional snow cover, the proposed retrieval algorithm outperformed the other three approaches (linear regression, artificial neural networks, and multivariate adaptive regression splines) using independent test data for all land cover classes with higher accuracy and no out-of-range estimated values. The method enabled the evaluation of the estimated fractional snow cover using independent datasets, in which the root mean square error of evaluation results ranged from 0.189 to 0.221. The snow cover detection capability of the proposed algorithm was validated using meteorological station observations with more than 310 000 records. We found that binary snow cover obtained from the estimated fractional snow cover was in good agreement with ground measurements (kappa: 0.67). There was significant improvement in the accuracy of snow cover identification using our algorithm; the overall accuracy increased by 18 % (from 0.71 to 0.84), and the omission error was reduced by 71 % (from 0.48 to 0.14) when the threshold of fractional snow cover was 0.3. The experimental results show that passive microwave brightness temperature data may potentially be used to estimate fractional snow cover directly in that this retrieval strategy offers a competitive advantage in snow cover detection.