| Technology and Methodology |
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Mapping of monthly mean snow depth in Northern Xinjiang using a multivariate nonlinear regression Kriging model based on MODIS snow cover data |
| XU Jianhui1, SHU Hong1, LI Yang2 |
1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China;
2. Institute of Desert Meteorology, CMA, Urumqi 830002, China |
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Abstract To accurately map the spatial-temporal variability of snow depth in Northern Xinjiang, the authors analyzed the spatial autocorrelation of monthly mean snow depths of 48 meteorological stations from December 2006 to January 2007, and investigated the relationship between snow depth, longitude, latitude and elevation. A multivariate nonlinear regression Kriging (MNRK) model based on the MODIS snow cover data is proposed to predict the spatial patterns of monthly mean snow depth. Relative to the ordinary Kriging (OK) and CoKriging with elevation (CoK) as covariate, the relative root mean square error(RRMSE) of predicted snow depth decreased by 15.14% and 9.54% in December, and decreased by 4.8% and 6.7% in January. The comparative results show that the MNRK method outperforms the other two methods. Integrating more information related to snow depth, the MNRK method is more efficient in capturing more spatial details of snow depth which varies with longitude, latitude and elevation. The CoK method without significantly correlated covariate produces worse results than the OK method.
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| Keywords
high-resolution remote sensing image(HRI)
image segmentation
spectral mergence
edge mergence
road extraction
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Issue Date: 23 July 2015
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2015,
Vol. 27
