the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Estimating spatial distribution of daily snow depth with kriging methods: combination of MODIS snow cover area data and ground-based observations
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.
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- SC C1745: 'Comment to: Estimating Spatial Distribution of Daily Snow Depth with Kriging Methods: Combination of MODIS Snow Cover Area Data and Ground-based Observations', Daniele Bocchiola, 25 Sep 2015
- RC C2164: 'Review', Anonymous Referee #1, 06 Nov 2015
- RC C2385: 'Estimating Spatial Distribution of Daily Snow Depth with Kriging Methods: Combination of MODIS Snow Cover Area Data and Ground-based Observations', Anonymous Referee #2, 26 Nov 2015
- SC C1745: 'Comment to: Estimating Spatial Distribution of Daily Snow Depth with Kriging Methods: Combination of MODIS Snow Cover Area Data and Ground-based Observations', Daniele Bocchiola, 25 Sep 2015
- RC C2164: 'Review', Anonymous Referee #1, 06 Nov 2015
- RC C2385: 'Estimating Spatial Distribution of Daily Snow Depth with Kriging Methods: Combination of MODIS Snow Cover Area Data and Ground-based Observations', Anonymous Referee #2, 26 Nov 2015
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Cited
5 citations as recorded by crossref.
- Estimating Snow Depth Using Multi-Source Data Fusion Based on the D-InSAR Method and 3DVAR Fusion Algorithm Y. Liu et al. 10.3390/rs9111195
- Using Universal Kriging for Spatiotemporal Data of Soil Pollution with Metals in Al Karama Industrial Area in Mosul City M. Ali & G. Dhahir 10.32441/kjps.07.02.p9
- Spatial distribution of snow depth based on geographically weighted regression kriging in the Bayanbulak Basin of the Tianshan Mountains, China Y. Liu et al. 10.1007/s11629-017-4564-z
- Developing Daily Cloud‐Free Snow Composite Products From MODIS and IMS for the Tienshan Mountains Y. Li et al. 10.1029/2018EA000460
- Spatial Topographic Interpolation for Meandering Channels V. Thanh et al. 10.1061/(ASCE)WW.1943-5460.0000582