Daily 0.01°×0.01° Land Surface Soil Moisture Dataset of the Qinghai-Tibet Plateau (2005、2010、2015、2017and 2018) (SMHiRes, V1)

Daily 0.01°×0.01° Land Surface Soil Moisture Dataset of the Qinghai-Tibet Plateau (2005、2010、2015、2017and 2018) (SMHiRes, V1)

This dataset contains daily 0.01°×0.01° land surface soil moisture products in the Qinghai-Tibet Plateau in 2005, 2010, 2015, 2017, and 2018. The dataset was produced by utilizing the multivariate statistical regression model to downscale the “SMAP Time-Expanded 0.25°×0.25° Land Surface Soil Moisture Dataset in the Qinghai-Tibet Plateau (SMsmapTE, V1)”. The auxiliary datasets participating in the multivariate statistical regression include GLASS Albedo/LAI/FVC, 1km all-weather surface temperature data in western China by Ji Zhou, and Lat/Lon information.

File naming and required software

File Naming Convention: YYYYMMDD.tiff (YYYY: year, MM: month, DD: day)
Data Version:V1
Projection:+proj=longlat +datum=WGS84 +no_defs
Data Format: GeoTIFF, 2000 rows ×3000 columes
Soil Moisture Unit: cm3/cm3
Soil Moisture Valid Range:0.02-0.5
Filled Value:Nodata

Data Citations Data citation guideline What's data citation?
Cite as:

Chai, L., Zhu, Z., Liu, S. (2020). Daily 0.01°×0.01° Land Surface Soil Moisture Dataset of the Qinghai-Tibet Plateau (2005、2010、2015、2017and 2018) (SMHiRes, V1). National Tibetan Plateau Data Center, DOI: 10.11888/Soil.tpdc.270947. CSTR: 18406.11.Soil.tpdc.270947. (Download the reference: RIS | Bibtex )

Related Literatures:

1. Hu, Z., Chai, L., Crow, W.T., Liu, S., Zhu, Z., Zhou, J., Qu, Y., Liu, J., Yang, S., Lu, Z., 2022. Applying a Wavelet Transform Technique to Optimize General Fitting Models for SM Analysis: A Case Study in Downscaling over the Qinghai–Tibet Plateau. Remote Sensing 14, 3063. https://doi.org/10.3390/rs14133063( View Details | Bibtex)

2. Qu, Y., Zhu, Z., Montzka, C., Chai, L., Liu, S., Ge, Y., Liu, J., Lu, Z., He, X., & Zheng, J. (2021). Inter-comparison of several soil moisture downscaling methods over the Qinghai-Tibet Plateau, China. Journal of Hydrology, 592, 125616. (https://doi.org/10.1016/j.jhydrol.2020.125616)( View Details | Bibtex)

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

References literature

1.Jia, K., Liang, S., Liu, S., Li, Y., et al. (2015). Global land surface fractional vegetation cover estimation using general regression neural networks from MODIS surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 53(9), 4787 - 4796. (View Details )

2.Merlin, O., Escorihuela, M.J., Mayoral, M.A., et al. (2013). Self-calibrated evaporation-based disaggregation of SMOS soil moisture: An evaluation study at 3 km and 100 m resolution in Catalunya, Spain. Remote Sensing of Environment, 130(4), 25-38. (View Details )

3.Piles, M., Petropoulos, G.P., Sánchez, N., et al. (2016). Towards improved spatio-temporal resolution soil moisture retrievals from the synergy of SMOS and MSG SEVIRI spaceborne observations. Remote Sensing of Environment, 180, 403-417. (View Details )

4.Srivastava, P.K., Han, D., Ramirez, M.R., et al. (2013). Machine Learning Techniques for Downscaling SMOS Satellite Soil Moisture Using MODIS Land Surface Temperature for Hydrological Application. Water Resources Management, 27(8), 3127-3144. (View Details )

5.Im, J., Park, S., Rhee, J., et al. (2016). Downscaling of AMSR-E soil moisture with MODIS products using machine learning approaches. Environmental Earth Sciences, 75(15), 1120. (View Details )

6.Xiao, Z., Liang, S., Wang, J., et al. (2014). Use of general regression neural networks for generating the GLASS leaf area index product from time-series MODIS surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 52(2), 209 - 223. (View Details )

7.Zhao, W., Sánchez, N., Lu, H., Li, A. (2018). A spatial downscaling approach for the SMAP passive surface soil moisture product using random forest regression. Journal of Hydrology, 563, 1009-1024. (View Details )

8.Qu, Y., Liu, Q., Liang, S., Wang, L., Liu, N., Liu, S. (2014). Direct-estimation algorithm for mapping daily land-surface broadband albedo from MODIS data. IEEE Transactions on Geoscience and Remote Sensing, 52(1), 907 - 919. (View Details )

Support Program

Pan-Third Pole Environment Study for a Green Silk Road-A CAS Strategic Priority A Program (No:XDA20000000)

the Strategic Priority Research Program of the Chinese Academy of Sciences (No:XDA20100101) [XDA20100101]

Copyright & License

To respect the intellectual property rights, protect the rights of data authors,expand servglacials of the data center, and evaluate the application potential of data, data users should clearly indicate the source of the data and the author of the data in the research results generated by using the data (including published papers, articles, data products, and unpublished research reports, data products and other results). For re-posting (second or multiple releases) data, the author must also indicate the source of the original data.

Example of acknowledgement statement is included below: The data set is provided by National Tibetan Plateau Data Center (http://data.tpdc.ac.cn).

License: This work is licensed under an Attribution 4.0 International (CC BY 4.0)

Related Resources

Current page automatically show English comments Show comments in all languages

Download Follow
Geographic coverage
East: 110.00 West: 70.00
South: 25.00 North: 45.00
  • Temporal resolution: Daily
  • Spatial resolution: <= 0.01º
  • File size: 85,197 MB
  • Views: 4,877
  • Downloads: 215
  • Access: Open Access
  • Temporal coverage: 2015-01-24 To 2015-09-24
  • Updated time: 2022-07-05
: CHAI Linna   ZHU Zhongli   LIU Shaomin  

Distributor: National Tibetan Plateau Data Center

Email: data@itpcas.ac.cn

Export metadata