Estimating fractional snow cover from passive microwave brightness temperature data using MODIS snow cover product over North America

• Published in: The cryosphere 2021-02, Vol.15 (2), p.835-861
• Main Authors: Xiao, Xiongxin, Liang, Shunlin, He, Tao, Wu, Daiqiang, Pei, Congyuan, Gong, Jianya
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Artificial neural networks; Artificial satellites in remote sensing; Brightness; Brightness temperature; Business competition; Climate change; Climate system; Clouds; Datasets; Detection; Dynamic characteristics; Ecological function; Error reduction; Evaluation; Extreme values; Extreme weather; Hydrology; Land cover; Mars; MODIS; Neural networks; Regional variations; Regression; Regression analysis; Remote sensing; Resolution; Retrieval; Satellite observation; Satellites; Sensors; Snow; Snow cover; Snow cover data; Snow cover variations; Spatial discrimination; Spatial resolution; Spectroradiometers; Spline functions; Splines; Surface radiation temperature; Temperature; Temperature data; Weather stations
• ISSN: 1994-0424; 1994-0416; 1994-0424; 1994-0416
• DOI: 10.5194/tc-15-835-2021

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