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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：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

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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. (2021). 祁连山地区日值0.05°×0.05°地表土壤水分数据（2019，SMHiRes, V2）. 时空三极环境大数据平台,
[Chai, L., Zhu, Z., Liu, S. (2021). Daily 0.05°×0.05° land surface soil moisture dataset of Qilian Mountain area (2019,SMHiRes,V2). A Big Earth Data Platform for Three Poles, ] (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.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 )

2.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 )

3.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. https://doi.org/10.1109/TGRS.2019.2892417 (查看 | 下载 )

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.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 )

6.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 )

7.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 )

8.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 )

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.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

为尊重知识产权、保障数据作者的权益、扩展数据中心的服务、评估数据的应用潜力，请数据使用者在使用数据所产生的研究成果中（包括公开发表的论文、论著、数据产品和未公开发表的研究报告、数据产品等成果），明确注明数据来源和数据作者。对于转载（二次或多次发布）的数据，作者还须注明原始数据来源。

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

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