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Discussion papers
https://doi.org/10.5194/tc-2019-280
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-2019-280
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 07 Jan 2020

Submitted as: research article | 07 Jan 2020

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This discussion paper is a preprint. It is a manuscript under review for the journal The Cryosphere (TC).

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

Xiongxin Xiao1, Shunlin Liang2, Tao He1, Daiqiang Wu1, Congyuan Pei1, and Jianya Gong1 Xiongxin Xiao et al.
  • 1School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
  • 2Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA

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.

Xiongxin Xiao et al.
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Short summary
Daily time-series and full space-covered sub-pixel snow cover area data are urgently needed for climate and reanalysis studies. Due to the observations from optical satellite sensors are seriously affected by clouds, this study attempts to capture dynamic characteristics of snow cover at a fine spatiotemporal resolution (daily; 6.25-km) accurately by using passive microwave data. We demonstrate the potential to use the passive microwave and the MODIS data to map the fractional snow cover area.
Daily time-series and full space-covered sub-pixel snow cover area data are urgently needed for...
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