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Preprints
https://doi.org/10.5194/tcd-9-4997-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/tcd-9-4997-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

  22 Sep 2015

22 Sep 2015

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This preprint was under review for the journal TC. A revision for further review has not been submitted.

Estimating spatial distribution of daily snow depth with kriging methods: combination of MODIS snow cover area data and ground-based observations

C. L. Huang1,2, H. W. Wang1,2, and J. L. Hou1,2 C. L. Huang et al.
  • 1Key Laboratory of Remote Sensing of Gansu Province, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, China
  • 2Heihe Remote Sensing Experimental Research Station, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, Gansu, 730000, China

Abstract. Accurately measuring the spatial distribution of the snow depth is difficult because stations are sparse, particularly in western China. In this study, we develop a novel scheme that produces a reasonable spatial distribution of the daily snow depth using kriging interpolation methods. These methods combine the effects of elevation with information from Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover area (SCA) products. The scheme uses snow-free pixels in MODIS SCA images with clouds removed to identify virtual stations, or areas with zero snow depth, to compensate for the scarcity and uneven distribution of stations. Four types of kriging methods are tested: ordinary kriging (OK), universal kriging (UK), ordinary co-kriging (OCK), and universal co-kriging (UCK). These methods are applied to daily snow depth observations at 50 meteorological stations in northern Xinjiang Province, China. The results show that the spatial distribution of snow depth can be accurately reconstructed using these kriging methods. The added virtual stations improve the distribution of the snow depth and reduce the smoothing effects of the kriging process. The best performance is achieved by the OK method in cases with shallow snow cover and by the UCK method when snow cover is widespread.

C. L. Huang et al.

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C. L. Huang et al.

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Short summary
An integrated framework was developed for estimating spatial distribution of snow depth. Four types of kriging methods were compared and evaluated: OK, UK, OCK, UCK. MODIS SCA data were used to compensate for the scarcity and uneven distribution of stations Results from the paper showed snow depth could be improved by the proposed method.
An integrated framework was developed for estimating spatial distribution of snow depth. Four...
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