Based on long-term series Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products, daily snow cover products without data gaps at 500 m spatial resolution over the Tibetan Plateau from 2002 to 2021 were generated by employing a Hidden Markov Random Field (HMRF) modeling technique. This HMRF framework optimally integrates spectral, spatiotemporal, and environmental information together, which not only fills data gaps caused by frequent clouds, but also improves the accuracy of the original MODIS snow cover products. In particular, this technology incorporates solar radiation as an environmental contextual information to improve the accuracy of snow identification in mountainous areas. Validation with in situ observations and snow cover derived from Landsat-8 OLI images revealed that these new snow cover products achieved an accuracy of 98.31% and 92.44%, respectively. Specifically, the accuracy of the new snow products is higher during the snow transition period and in complex terrains with higher elevations as well as sunny slopes. These gap-free snow cover products effectively improve the spatiotemporal continuity and the low accuracy in complex terrains of the original MODIS snow products, and is thus the basis for the study of climate change and hydrological cycling in the TP.

File naming and required software

File name: snow cover products are stored in GeoTIFF format. The name of the file is "HMRFSTP_yyyyddd. tif", in which HMRFSTP is the abbreviation of "A Hidden Markov Radom Field -based snow cover product for Tibetan Plateau", yyyy stands for year and DDD stands for Julian day, such as 2002135.tif describes the snow cover on Tibet Plateau on the 135th day of 2002.
Methods for using Data: all GeoTIFF files in the dataset can be viewed and processed directly based on ArcGIS, QGIS, ENVI and MATLAB.

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Cite as:

Huang, Y., Xu, J. (2022). Daily cloud-free snow cover products for Tibetan Plateau from 2002 to 2021. A Big Earth Data Platform for Three Poles, DOI: 10.11888/Cryos.tpdc.272204. CSTR: 18406.11.Cryos.tpdc.272204. (Download the reference： RIS | Bibtex )

Related Literatures:

1. Huang, Y., Liu, H., Yu, B., Wu, J., Kang, E. L., Xu, M., Wang, S., Klein, A., & Chen, Y. (2018). Improving MODIS snow products with a HMRF-based spatio-temporal modeling technique in the Upper Rio Grande Basin. Remote Sensing of Environment, 204, 568-582.( View Details | Bibtex)

Using this data, the data citation is required to be referenced and the related literatures are suggested to be cited.

Support Program

National Natural Science Foundation of China (No:42071306)

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