DOI: 10.5194/tc-15-835-2021
论文题名: Estimating fractional snow cover from passive microwave brightness temperature data using MODIS snow cover product over North America
作者: Xiao X. ; Liang S. ; He T. ; Wu D. ; Pei C. ; Gong J.
刊名: Cryosphere
ISSN: 19940416
出版年: 2021
卷: 15, 期: 2 起始页码: 835
结束页码: 861
语种: 英语
英文关键词: brightness temperature
; estimation method
; microwave radiometer
; MODIS
; satellite data
; snow cover
; solar radiation
; spatial resolution
; spatiotemporal analysis
; North America
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 Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/164831
Appears in Collections: 气候变化与战略
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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
Recommended Citation:
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]. Cryosphere,2021-01-01,15(2)