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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. (2022). 祁连山地区日值0.05°×0.05°地表土壤水分数据（2020，SMHiRes, V2）. 时空三极环境大数据平台, DOI: 10.11888/Terre.tpdc.272375. CSTR: 18406.11.Terre.tpdc.272375.
[Chai, L., Zhu, Z., Liu, S. (2022). Daily 0.05°×0.05° land surface soil moisture dataset of Qilian Mountain area (2020,SMHiRes,V2). A Big Earth Data Platform for Three Poles, DOI: 10.11888/Terre.tpdc.272375. CSTR: 18406.11.Terre.tpdc.272375. ] (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.Liu, Q., Wang, L. Z., Qu, Y., Liu, N. F., Liu, S. H., Tang, H. R., and Liang, S. L. (2013) Preliminary Evaluation of the Long-term GLASS Albedo Product, International Journal of Digital Earth, doi: 10.1080/17538947.2013.804601 (查看 )

2.Xiao, Z.Q., Song, J.L., Yang, H., Sun, R., & Li, J. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. International Journal of Remote Sensing, 43(4), 1199-1225. (https://doi.org/10.1080/01431161.2022.2039415) (查看 )

3.Liu, J., Chai, L., Dong, J., Zheng, D., Wigneron, J., Liu, S., & Zhou, J. (2021). Uncertainty analysis of eleven multisource soil moisture products in the third pole environment based on the three-corned hat method. Remote Sensing of Environment, 255, 112225. (https://doi.org/10.1016/j.rse.2020.112225) (查看 )

4.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 )

5.Xiao, Z.Q., Liang, S.L., Wang, J.D., Chen, P., Yin, X.J., Zhang, L.Q., & Song, J.L. (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, vol.52, no.1, pp. 209-223. (https://doi.org/10.1109/TGRS.2013.2237780) (查看 )

6.Xiao, Z.Q., Liang, S.L., Wang, J.D., Xiang, Y., Zhao, X., & Song, J.L. (2016). Long Time-Series Global Land Surface Satellite (GLASS) Leaf Area Index Product Derived from MODIS and AVHRR Data, IEEE Transactions on Geoscience and Remote Sensing, 54(9), 5301-5318. (https://doi.org/10.1109/TGRS.2016.2560522) (查看 )

7.Zhang, X., Zhou, J., Liang, S., Wang, D. (2021). A practical reanalysis data and thermal infrared remote sensing data merging (RTM) method for reconstruction of a 1-km all-weather land surface temperature. Remote Sensing of Environment, 260, 112437. https://doi.org/10.1016/j.rse.2021.112437. (查看 )

8.Liu, J., Chai, L., Lu, Z., Liu, S., Qu, Y., Geng, D., & Wang, J. (2019). Evaluation of SMAP, SMOS-IC, FY3B, JAXA, and LPRM soil moisture products over the Qinghai-Tibet Plateau and its surrounding area. Remote Sensing, 11, 792. (https://doi.org/10.3390/rs11070792) (查看 )

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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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