文件命名约定：YYYYMMDD.tiff （YYYY：年，MM：月，DD：日）
数据版本号：V2
投影：+proj=longlat +datum=WGS84 +no_defs
数据格式：GeoTIFF, 220行×360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. 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)(查看)
数据的引用
柴琳娜, 朱忠礼, 刘绍民. (2021). 祁连山地区日值0.05°×0.05°地表土壤水分数据（2019，SMHiRes, V2）. 国家青藏高原科学数据中心, 2021.
[Chai, L., Zhu, Z., Liu, S. (2021). Daily 0.05°×0.05° land surface soil moisture dataset of Qilian Mountain area (2019,SMHiRes,V2). National Tibetan Plateau Data Center, 2021]
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.Qu, Y., Zhu, Z., Chai, L., Liu, S., Montzka, C., Liu, J., Yang, X., Lu, Z., Jin, R., & Li, X. (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, 683.( 查看 )
3.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 )
4.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 )
5.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 )
6.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 )
7.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 )
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 )
9.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 )
10.Zhang, X., Zhou, J., Göttsche, F., Zhan, W., Liu, S., & Cao, R. (2019). A method based on temporal component decomposition for estimating 1-km all-weather land surface temperature by merging satellite thermal infrared and passive microwave observations. IEEE Transactions on Geoscience and Remote Sensing, 57, 4670-4691.( 查看 )
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License：
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泛第三极环境变化与绿色丝绸之路建设专项
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3. 祁连山综合观测网：黑河流域地表过程综合观测网（大满超级站气象要素梯度观测系统-2019）
5. 青藏高原8千米分辨率植被净初级生产力数据集（1990-2015）
6. 祁连山综合观测网：黑河流域地表过程综合观测网（混合林站涡动相关仪-2020）
7. 黑河综合遥感联合试验：冰沟流域加密观测区K&Ka波段机载微波辐射计地面同步观测数据集（2008年3月29日）
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| East: 107.00 | West: 89.00 |
|---|---|
| South: 34.00 | North: 45.00 |
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