Daily 0.01°×0.01° Land Surface Soil Moisture Dataset of the Qinghai-Tibet Plateau (2005、2010、2015、2017and 2018) (SMHiRes, V1)
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
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.
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 )
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]
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East: 110.00 | West: 70.00 |
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South: 25.00 | North: 45.00 |
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