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Journal of Applied Remote Sensing • Open Access

Synthesis method for simulating snow distribution utilizing remotely sensed data for the Tibetan Plateau
Author(s): Hongyi Li; Zhiguang Tang; Jian Wang; Tao Che; Xiaoduo Pan; Chunlin Huang; Xufeng Wang; Xiaohua Hao; Shaobo Sun

Paper Abstract

The complex terrain, shallow snowpack, and cloudy conditions of the Tibetan Plateau (TP) can greatly affect the reliability of different remote sensing (RS) data, and available station data are scarce for simulating and validating the snow distribution. Aiming at these problems, we design a synthesis method for simulating the snow distribution in the TP where the snow is patchy and shallow in most regions. Different RS data are assimilated into the SnowModel, using the ensemble Kalman filter method. The station observations are used for the validation of assimilated snow depth. To avoid the scale effect during validation, we design a random sampling comparison method by constructing a subjunctive region near each station. For years 2000 to 2008, the root-mean-square error of the assimilated results are in the range [0.002 m, 0.008 m], and the range of Pearson product-moment correlation coefficients between the in situ observations and the assimilated results are in the range [0.61, 0.87]. The result suggests that the snow depletion curve is the most important parameter for the simulation of the snow distribution in ungauged regions, especially in the TP where the snow is patchy and shallow.

Paper Details

Date Published: 2 April 2014
PDF: 17 pages
J. Appl. Remote Sens. 8(1) 084696 doi: 10.1117/1.JRS.8.084696
Published in: Journal of Applied Remote Sensing Volume 8, Issue 1
Show Author Affiliations
Hongyi Li, Cold and Arid Regions Environmental and Engineering Research Institute (China)
Zhiguang Tang, Cold and Arid Regions Environmental and Engineering Research Institute (China)
Jian Wang, Cold and Arid Regions Environmental and Engineering Research Institute (China)
Tao Che, Cold and Arid Regions Environmental and Engineering Research Institute (China)
Xiaoduo Pan, Cold and Arid Regions Environmental and Engineering Research Institute (China)
Chunlin Huang, Cold and Arid Regions Environmental and Engineering Research Institute (China)
Xufeng Wang, Cold and Arid Regions Environmental and Engineering Research Institute (China)
Xiaohua Hao, Cold and Arid Regions Environmental and Engineering Research Institute (China)
Shaobo Sun, Cold and Arid Regions Environmental and Engineering Research Institute (China)


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