气候变化领域集成服务门户(CCPortal): Estimating fractional snow cover from passive microwave brightness temperature data using MODIS snow cover product over North America

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DOI	10.5194/tc-15-835-2021
	Estimating fractional snow cover from passive microwave brightness temperature data using MODIS snow cover product over North America
	Xiao X.; Liang S.; He T.; Wu D.; Pei C.; Gong J.
发表日期	2021
ISSN	19940416
起始页码	835
结束页码	861
卷号	15 期号:2
英文摘要	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. © Author(s) 2021. This work is distributed under the Creative Commons Attribution 4.0 License
英文关键词	brightness temperature; estimation method; microwave radiometer; MODIS; satellite data; snow cover; solar radiation; spatial resolution; spatiotemporal analysis; North America
语种	英语
来源期刊	Cryosphere
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/202453
作者单位	School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, China; Department of Geographical Sciences, University of Maryland, College Park, MD 20742, United States
推荐引用方式 GB/T 7714	Xiao X.,Liang S.,He T.,et al. Estimating fractional snow cover from passive microwave brightness temperature data using MODIS snow cover product over North America[J],2021,15(2).
APA	Xiao X.,Liang S.,He T.,Wu D.,Pei C.,&Gong J..(2021).Estimating fractional snow cover from passive microwave brightness temperature data using MODIS snow cover product over North America.Cryosphere,15(2).
MLA	Xiao X.,et al."Estimating fractional snow cover from passive microwave brightness temperature data using MODIS snow cover product over North America".Cryosphere 15.2(2021).