Abstract. The dynamic characteristics of seasonal snow cover are critical for hydrology management, climate system, and ecosystem function. Although optical satellite remote sensing has proved to be an effective tool for monitoring global and regional variations of snow cover, it is still problematic to accurately capture the snow dynamics characteristics at a finer spatiotemporal resolution, because the observations from optical satellite sensors are seriously affected by clouds and solar illumination. Besides, traditional methods of mapping snow cover from passive microwave data only provide binary information with a 25-km spatial resolution. In this study, we first present an approach to predict fractional snow cover over North America under all-weather conditions, derived from the enhanced resolution passive microwave brightness temperature data (6.25 km). This estimation algorithm used Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products between 2010 and 2017 to create the reference fractional snow cover data as the "true" observations. Further, the influence of many factors, including land cover, topography, and location, were incorporated into the retrieval models. The results show that the proposed retrieval models based on random forest regression technique perform much better using independent test data for all land cover classes, with higher accuracy and no out-of-range estimated values, when compared to the other three approaches (linear regression, artificial neural networks (ANN), and multivariate adaptive regression splines (MARS)). The results of the output evaluated by using independent data indicate that the root-mean-square error (RMSE) of the estimated fractional snow cover ranges from 16.7 % to 19.8 %. In addition, the estimated fractional snow cover is verified in the snow mapping aspect by using snow cover observation data from meteorological stations (more than 0.31 million records). The result shows that the binary snow cover obtained by the proposed retrieval algorithm is in a good agreement with the ground measurements (kappa: 0.67). The accuracy of our algorithm estimation in the snow cover identification shows significant improvement when benchmarked against the Grody’s snow cover mapping algorithm: overall accuracy is increased by 18 % (from 0.71 to 0.84), and omission error is reduced by 71 % (from 0.48 to 0.14). Daily time-series and full space-covered sub-pixel snow cover area data are urgently needed for climate and reanalysis studies. According to our experiment results, we can conclude that it is feasible for estimating fractional snow cover from passive microwave brightness temperature data, and this strategy also has a great advantage in detecting snow cover area.
Analyzing Machine Learning Predictions of Passive Microwave Brightness Temperature Spectral Difference Over Snow-Covered Terrain in High Mountain Asia
