文件命名约定:YYYYMMDD.tiff (YYYY:年,MM:月,DD:日)
数据版本号:V1
投影:+proj=longlat +datum=WGS84 +no_defs
数据格式:GeoTIFF, 2000行×3000列
土壤水分单位:cm3/cm3
土壤水分有效值范围:0.02~0.5
填充值:Nodata
1. Liu, J., Chai, L.N., Lu, Z., Liu, S.M., Qu, Y.Q., Geng, D.Y., Song, Y.Z., Guan, Y.B., Guo, Z.X., Wang, J., Zhu, Z.L. (2019). Evaluation of SMAP, SMOS-IC, FY3B, JAXA, and LPRM Soil Moisture Products over the Qinghai-Tibet Plateau and Its Surrounding Areas. Remote Sensing, 11(7), 792. (https://doi.org/10.3390/rs11070792)(查看)
2. Qu, Y.Q., Zhu, Z.L., Chai, L.N., Liu, S.M., Montzka, C., Liu, J., Yang, X.F., Lu, Z., Jin, R., Li, X., Guo, Z.X., Zheng, J. (2019). Rebuilding a Microwave Soil Moisture Product Using Random Forest Adopting AMSR-E/AMSR2 Brightness Temperature and SMAP over the Qinghai–Tibet Plateau, China. Remote Sensing, 11(6), 683. (https://doi.org/10.3390/rs11060683)(查看)
数据的引用
柴琳娜, 朱忠礼, 刘绍民. (2020). 青藏高原地区5年(2005、2010、2015、2017和2018)逐日0.01°×0.01°地表土壤水分数据(SMHiRes, V1). 国家青藏高原科学数据中心, 2020. doi:
10.11888/Soil.tpdc.270947.
[Chai, L., Zhu, Z., Liu, S. (2020). Daily 0.01°×0.01° Land Surface Soil Moisture Dataset of the Qinghai-Tibet Plateau (SMHiRes, V1). National Tibetan Plateau Data Center, 2020]
使用本数据时必须引用“文章的引用”中列出的文献,并进行数据的引用
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.( 查看 )
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.( 查看 )
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.( 查看 )
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.( 查看 )
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.( 查看 )
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.( 查看 )
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.( 查看 )
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.( 查看 )
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License:
This work is licensed under an
Attribution 4.0 International (CC BY 4.0)
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