Dataset List

Vol.|Area

No.11 Vol.11,2024
No.10 Vol.11,2024
No.09 Vol.11,2024
No.08 Vol.11,2024
No.07 Vol.11,2024
No.06 Vol.11,2024
No.05 Vol.11,2024
No.04 Vol.11,2024
No.03 Vol.11,2024
No.02 Vol.11,2024
No.01 Vol.11,2024
No.12 Vol.10,2023
No.11 Vol.10,2023
No.10 Vol.10,2023
No.09 Vol.10,2023
No.08 Vol.10,2023
No.07 Vol.10,2023
No.06 Vol.10,2023
No.05 Vol.10,2023
No.04 Vol.10,2023
No.03 Vol.10,2023
No.02 Vol.10,2023
No.01 Vol.10,2023
No.12 Vol.9,2022
No.11 Vol.9,2022
No.10 Vol.9,2022
No.09 Vol.9,2022
No.08 Vol.9,2022
No.07 Vol.9,2022
No.06 Vol.9,2022
No.05 Vol.9,2022
No.04 Vol.9,2022
No.03 Vol.9,2022
No.02 Vol.9,2022
No.01 Vol.9,2022
No.12 Vol.8,2021
No.11 Vol.8,2021
No.10 Vol.8,2021
No.09 Vol.8,2021
No.08 Vol.8,2021
No.07 Vol.8,2021
No.06 Vol.8,2021
No.05 Vol.8,2021
No.04 Vol.8,2021
No.03 Vol.8,2021
No.03 Vol.8,2021
No.02 Vol.8,2021
No.01 Vol.8,2021
No.09 Vol.7,2020
No.08 Vol.7,2020
No.07 Vol.7,2020
No.06 Vol.7,2020
No.05 Vol.7,2020
No.04 Vol.7,2020
No.03 Vol.7,2020
No.02 Vol.7,2020
No.01 Vol.7,2020
No.06 Vol.6,2019
No.05 Vol.6,2019
No.04 Vol.6,2019
No.03 Vol.6,2019
No.02 Vol.6,2019
No.01 Vol.6,2019
No.08 Vol.5,2018
No.07 Vol.5,2018
No.06 Vol.5,2018
No.05 Vol.5,2018
No.04 Vol.5,2018
No.03 Vol.5,2018
No.02 Vol.5,2018
No.01 Vol.5,2018
No.04 Vol.4,2017
No.03 Vol.4,2017
No.02 Vol.4,2017
No.01 Vol.4,2017
No.09 Vol.3,2016
No.08 Vol.3,2016
No.07 Vol.3,2016
No.06 Vol.3,2016
No.05 Vol.3,2016
No.04 Vol.3,2016
No.03 Vol.3,2016
No.02 Vol.3,2016
No.01 Vol.3,2016
No.02 Vol.2,2015
No.01 Vol.2,2015
No.02 Vol.1,2014
No.01 Vol.1,2014
China
Asia
Oceanic region
African
European
North American
South American
Arctic
Global Scale
Data Details

Snow Cover Dataset by Multi-source Data Fusion Algorithm ─ A Case Study in the Northwestern United States


GAO Yang1DONG Huaiwei1,2
1 Key Laboratory of Tibetan Environment Changes and Land Surface Processes，Institute of Tibetan Plateau Research，Chinese Academy of Sciences，Beijing 100101，China2 College of Geodesy and Geomatics，Shandong University of Science and Technology，Qingdao 266590，China

DOI:10.3974/geodb.2022.02.08.V1

Published:Feb. 2022

Visitors：7016       Data Files Downloaded：21      
Data Downloaded：8850.60 MB      Citations：

Key Words：

snow cover,multisource data,daily data,2000-2020,Northwestern United States,

Abstract：

A comprehensive understanding of the snow cover is of great significance to the measurement of coping with the snow change, the management of regional water resources under continuous warming, and the deeply understanding of global climate change. Based on the latest MODIS NDSI data, IMS snow/ice data and the snow measurements at 192 SNOTEL stations, the authors firstly defined the suitable NDSI threshold for snow recognition according to the snow characteristics in the Northwestern United States, and then formulated various fusion rules based on data performances in different periods. Finally the snow cover dataset by multi-source data fusion algorithm -- a case study in the Northwestern United States was developed. The validation proved that the fusion could improve the accuracy and snow recognition performances compared with the source data. The dataset includes: (1) boundary data of the study area; (2) daily snow cover data in the study area during 2000-2020. In addition, the validation data was attached. The spatial resolution is 500 m. The dataset is archived in .tiff, .shp, .xlsx and .txt data formats, and consists of 7,688 data files with data size of 170 GB (Compressed into one single file with 421 MB).Browse

Foundation Item：

Ministry of Science and Technology of P. R. China (2017YFA0603303); National Natural Science Foundation of China (42171136)

Data Citation：

GAO Yang, DONG Huaiwei. Snow Cover Dataset by Multi-source Data Fusion Algorithm ─ A Case Study in the Northwestern United States[J/DB/OL]. Digital Journal of Global Change Data Repository, 2022. https://doi.org/10.3974/geodb.2022.02.08.V1.

GAO Yang, DONG Huaiwei. Snow cover dataset by multi-source data fusion algorithm—a case study in the Northwestern United States [J]. Journal of Global Change Data & Discovery. 2022, 6(2): 280-289. https://doi.org/10.3974/geodp.2022.02.15.

References：

[1] Brown, R., Derksen, C., Wang, L. Assessment of spring snow cover duration variability over northern Canada from satellite datasets [J]. Remote Sensing of Environment, 2007, 111: 367-381.
     [2] Armstrong, R. L., Brodzik, M. J. Recent northern hemisphere snow extent: A comparison of data derived from visible and microwave satellite sensors [J]. Geophysical Research Letters, 2001, 28(19): 3673-3676.
     [3] Organization, W. M. IGOS cryosphere theme: a cryosphere theme report for the IGOS partnership [M]. World Meteorological Organization, 2007.
     [4] Abbas, F., Simon, G., Ghaleb, F., et al. Snow hydrology in Mediterranean mountain regions: A review [J]. Journal of Hydrology, 2017, 551: 374-396.
     [5] Franz, K. J., Hogue, T. S., Sorooshian, S. Operational snow modeling: Addressing the challenges of an energy balance model for National Weather Service forecasts [J]. Journal of Hydrology, 2008, 360(1-4): 48-66.
     [6] Zhong, G. X., Song, K. S., Wang, Z. M., et al. Verification and Comparison of the MODIS and AMSR-E Snow Cover Products in Northeast China [J]. Journal of Glaciology and Cryopedology, 2010, 32(6): 1262-1269.
     [7] Huang, X. D., Zhang, X. T., Li, Z., et al. Accuracy Analysis for MODIS Snow Products of MOD10A1 and MOD10A2 in Northern Xinjiang Area [J]. Journal of Glaciology and Cryopedology, 2007(5): 722-729.
     [8] Chen, R. S., Kang, E. Q., Wu, L. Z., et al. Cold Regions in China [J]. Journal of Glaciology and Cryopedology, 2005(4): 469-475.
     [9] Rasouli, K., Pomeroy, J. W., Whitfield, P. H. Hydrological Responses of Headwater Basins to Monthly Perturbed Climate in the North American Cordillera [J]. Journal of Hydrometeorology, 2019, 20(5): 863-882.
     [10] Zhang, Y. J., Zhong, X. Y. Classification and regionalization of the seasonal snow cover across the Eurasian Continent [J]. Journal of Glaciology and Cryopedology, 2014, 36(3): 481-490.
     [11] Siirila-Woodburn, E. R., Rhoades, A. M., et al. A low-to-no snow future and its impacts on water resources in the western United States [J]. Nature Reviews Earth & Environment, 2021, 2: 800-819.
     [12] Huang, X. D., Li, X. B., Liu, C. Y., et al. Remote sensing inversion of snow cover extent and snow depth/snow water equivalent on the Qinghai-Tibet Plateau: advance and challenge [J]. Glaciology and Geocryology, 2019, 41(5): 1138-1149．
     [13] Gao, Y., Lu, N., Yao, T. D. Evaluation of a cloud-gap-filled MODIS daily snow cover product over the Pacific Northwest USA [J]. Journal of Hydrology, 2011, 404: 157-165.
     [14] Gao, Y., Xie, H. J., Lu, N., et al. Toward advanced daily cloud-free snow cover and snow water equivalent [J]. Journal of Hydrology, 2010, 385: 23-25.
     [15] Li, X. H., Jing, Y. H., Shen, H. F., et al. The recent development in cloud removal approaches of MODIS snow cover product [J]. Hydrology and Earth system Sciences, 2019, 23(5): 2401-2416.
     [16] Gao, Y., Xie, H. J., Yao, T. D., et al. Integrated assessment on multi-temporal and multi-sensor combinations for reducing cloud obscuration of MODIS snow cover products of the Pacific Northwest USA [J]. Remote Sensing of Environment, 2010, 114: 1662-1675.
     [17] Chu, D., Zhaxi, D. Z., Cidan, Y. Z., Analysis on applicability of NOAA IMS snow and ice products in snow cover monitoring over the Tibetan Plateau [J]. Journal of Glaciology and Geocryology, 2021, 43(6): 1659-1672.
     [18] Mazari, N., Ahmet, E., Xie, H. J., et al. Assessment of ice mapping system and moderate resolution imaging spectroradiometer snow cover maps over Colorado Plateau [J]. Journal of Applied Remote Sensing, 2013, 7(1): 1-16.
     [19] Hall, D. K., Riggs, G. A. MODIS/Terra Snow Cover Daily L3 Global 500m SIN Grid, Version 61 [DB]. NASA National Snow and Ice Data Center Distributed Active Archive Center, 2021, https://doi.org/10.5067/MODIS/MOD10A1.061.
     [20] Hall, D. K., Riggs, G. A. MODIS/Aqua Snow Cover Daily L3 Global 500m SIN Grid, Version 61 [DB]. NASA National Snow and Ice Data Center Distributed Active Archive Center, 2021, https://doi.org/10.5067/MODIS/MYD10A1.061.
     [21] Hall, D. K., Riggs, G. A., Salomonson, V. V., et al. MODIS snow-cover products [J]. Remote Sensing of Environment, 2002, 83: 181-194.
     [22] Riggs, G. A., Hall, D. K., Román, M. MODIS snow products user guide for Collection 6(C6) [R/OL]. [2019-07-02]. https://modis-snow-ice.gsfc.nasa.gov/uploads/C6_MODIS_Snow_User_Guide.pdf.
     [23] Zeng, T. Y. Snow phenology dynamics and its response to Climate over Tibetan Plateau [D]. Lanzhou: Lanzhou University, 2019.
     [24] Liu, X., Jin, X., Ke, C. Q. Accuracy evaluation of the IMS snow and ice products in stable snow covers region in China [J]. Glaciology and Geocryology, 2014, 36(3): 500-507.
     [25] U.S. National Ice Center. IMS Daily Northern Hemisphere Snow and Ice Analysis at 1 km, 4 km, and 24 km Resolutions, Version 1 [DB]. National Snow and Ice Data Center, 2008, https://doi.org/10.7265/N52R3PMC.
     [26] Zhang, H. B., Zhang, F., Zhang, G. Q., et al. Ground-based evaluation of MODIS snow cover product V6 across China: Implications for the selection [J]. Science of the Total Environment, 2019, 651: 2712-2726.
     [27] USDA Natural Resources Conservation Service. Snowpack: Snow Water Equivalent (SWE) and Snow Depth [DB/OL]. https://www.nrcs.usda.gov/wps/portal/wcc/.
     

Data Product:

ID Data Name Data Size Operation
0Datapaper_SnowCoverTest_2000-2020.pdf1804.00kbDownLoad
1 SnowCoverTest_2000-2020.rar 431572.12KB
Co-Sponsors
Superintend