Articles | Volume 15, issue 2
https://doi.org/10.5194/tc-15-835-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-15-835-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Estimating fractional snow cover from passive microwave brightness temperature data using MODIS snow cover product over North America
Xiongxin Xiao
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan 430079, China
Shunlin Liang
Department of Geographical Sciences, University of Maryland, College
Park, MD 20742, USA
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan 430079, China
Daiqiang Wu
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan 430079, China
Congyuan Pei
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan 430079, China
Jianya Gong
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan 430079, China
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Preprint withdrawn
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Jin Ma, Ji Zhou, Frank-Michael Göttsche, Shunlin Liang, Shaofei Wang, and Mingsong Li
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Yi Zheng, Ruoque Shen, Yawen Wang, Xiangqian Li, Shuguang Liu, Shunlin Liang, Jing M. Chen, Weimin Ju, Li Zhang, and Wenping Yuan
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Accurately reproducing the interannual variations in vegetation gross primary production (GPP) is a major challenge. A global GPP dataset was generated by integrating the regulations of several major environmental variables with long-term changes. The dataset can effectively reproduce the spatial, seasonal, and particularly interannual variations in global GPP. Our study will contribute to accurate carbon flux estimates at long timescales.
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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 fact that observations from optical satellite sensors are 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...