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数据文件命名方式和使用方法

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

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本数据要求的引用方式 查看数据引用帮助 数据引用必读

数据的引用

柴琳娜, 朱忠礼, 刘绍民. (2020). 青藏高原地区（2005、2010、2015、2017和2018）逐日0.01°×0.01°地表土壤水分数据（SMHiRes, V1）. 时空三极环境大数据平台, DOI: 10.11888/Soil.tpdc.270947. CSTR: 18406.11.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 (2005、2010、2015、2017and 2018) (SMHiRes, V1). A Big Earth Data Platform for Three Poles, DOI: 10.11888/Soil.tpdc.270947. CSTR: 18406.11.Soil.tpdc.270947. ] (下载引用： RIS格式 | RIS英文格式 | Bibtex格式 | Bibtex英文格式 )

文章的引用

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( 查看 | Bibtex格式)

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)( 查看 | Bibtex格式)

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

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参考文献

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.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 )

4.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 )

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.Qu, Y., Q. Liu, S. L. Liang, L. Z. Wang, N. F. Liu & S. H. Liu (2013). Direct-estimation algorithm for mapping daily land-surface broadband albedo from MODIS data. IEEE. Trans. on Geos. and Remote Sens., doi：10.1109/TGRS.2013.2245670. (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.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 )

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资助项目

泛第三极环境变化与绿色丝绸之路建设专项(XDA20000000) (项目编号:XDA20000000) Pan-Third Pole Environment Study for a Green Silk Road-A CAS Strategic Priority A Program

数据使用声明

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

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