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黑河流域区域尺度地表蒸散发相对真值数据集(2012~2016年) ETMap Version 1.0
Dataset of ground truth of land surface evapotranspiration at regional scale in the Heihe River Basin(2012-2016)ETMap Version 1.0


地表蒸散发(Evapotranspiration,ET)是连接着陆地能量平衡、水循环以及碳循环等的重要变量,地表蒸散发的准确获取有助于全球气候变化、作物估产、干旱监测等研究,并且对区域与全球的水资源规划管理具有重要的意义。地表蒸散发的获取方法主要包括地面观测、遥感估算、模式模拟与同化等。地面观测可以获得高精度的地表蒸散发数据,但观测站点的空间代表性十分有限;遥感估算、模式模拟与同化方法可以获得空间连续的地表蒸散发,但存在精度与时空分布格局合理性的验证问题。因此,本研究充分利用众多的高精度站点观测数据,结合多源遥感信息,将地面站点观测尺度扩展至区域上,获得高精度、时空分布连续的地表蒸散发量。

基于近年来开展的“黑河综合遥感联合试验”(WATER)、“黑河流域生态-水文过程综合遥感观测联合试验”(HiWATER)、所积累的站点观测数据(自动气象站、涡动相关仪、大孔径闪烁仪等),共选用36个站点(65个站年,分布图见图1),结合多源遥感数据(土地覆盖与植被类型图,叶面积指数、地表温度等)和大气驱动数据等,运用五种机器学习方法(回归树、随机森林、人工神经网络、支持向量机、深度信念网络)分别构建了不同的地表蒸散发尺度扩展模型,对各尺度扩展模型进行了全面的对比分析,结果表明:相比于其他四种方法,随机森林方法更适合于黑河流域由站点到区域的地表蒸散发尺度扩展研究。基于优选出的随机森林尺度扩展模型,以遥感及大气驱动数据作为输入,生产了2012~2016年生长季(5~9月)黑河流域地表蒸散发时空分布图(ETMap)。以LAS观测值为真值进行验证,结果表明:ETMap整体精度良好,上游 (LAS1)、中游 (LAS2-LAS5)和下游 (LAS6 - LAS8)的RMSE (MAPE)分别为0.65 mm/day(18.86%)、0.99 mm/day (19.13%)和0.91 mm/day (22.82%)。总之,ETMap是基于站点观测数据运用随机森林算法进行尺度扩展得到的精度较高的黑河流域地表蒸散发产品。所有站点信息和尺度扩展方法请参考Xu et al. (2018),观测数据处理请参考Liu et al. (2018)。


本数据要求的多篇文献引用

  1. Liu SM, Li X, Xu ZW, Che T, Xiao Q, Ma MG, Liu QH, Jin R, Guo JW, Wang LX, Wang WZ, Qi Y, Li HY, Xu TR, Ran YH, Hu XL, Shi SJ, Zhu ZL, Tan JL, Zhang Y, Ren ZG. The Heihe Integrated Observatory Network: A basin-scale land surface processes observatory in China. Vadose Zone Journal, 2018, 17:180072. doi:10.2136/vzj2018.04.0072查看 下载
  2. Xu,T., Guo,Z., Liu,S., He,X., Meng,Y., Xu,Z.,et al. Evaluating different machine learning methods for upscaling evapotranspiration from towers to the regional scale. Journal of Geophysical Research: Atmospheres, 2018, 123. https://doi.org/10.1029/2018JD028447.查看 下载

相关文献(作者推荐)

  1. Liu SM, Xu ZW, Song LS, Zhao QY, Ge Y, Xu TR, Ma YF, Zhu ZL, Jia ZZ, Zhang F. Upscaling evapotranspiration measurements from multi-site to the satellite pixel scale over heterogeneous land surfaces. Agricultural and Forest Meteorology, 2016, 230-231, 97-113. doi:10.1016/j.agrformet.2016.04.008.查看下载
  2. Xu TR, He XL, Bateni SM, Auligne T, Liu SM, Xu ZW, Zhou J, Mao KB. Mapping Regional Turbulent Heat Fluxes via Variational Assimilation of Land Surface Temperature Data from Polar Orbiting Satellites. Remote Sensing of Environment, 2019, 221: 444-461, doi.org/10.1016/j.rse.2018.11.023查看
  3. Li X, Cheng GD, Liu SM, Xiao Q, Ma MG, Jin R, Che T, Liu QH, Wang WZ, Qi Y, Wen JG, Li HY, Zhu GF, Guo JW, Ran YH, Wang SG, Zhu ZL, Zhou J, Hu XL, Xu ZW. Heihe Watershed Allied Telemetry Experimental Research (HiWATER): Scientific objectives and experimental design. Bulletin of the American Meteorological Society, 2013, 94(8): 1145-1160, 10.1175/BAMS-D-12-00154.1.查看下载
  4. Li X, Liu SM, Xiao Q, Ma MG, Jin R, Che T, Wang WZ, Hu XL, Xu ZW, Wen JG, Wang LX. A multiscale dataset for understanding complex eco-hydrological processes in a heterogeneous oasis system. Scientific Data, 2017, 4: 170083. doi:10.1038/sdata.2017.83.查看下载
  5. Liu SM, Xu ZW, Zhu ZL, Jia ZZ, Zhu MJ. Measurements of evapotranspiration from eddy-covariance systems and large aperture scintillometers in the Hai River Basin, China. Journal of Hydrology, 2013, 487, 24-38.查看下载
  6. Li X, Liu SM, Li HX, Ma YF, Wang JH, Zhang Y, Xu ZW, Xu TR, Song LS, Yang XF, Lu Z, Wang ZY, Guo ZX. Intercomparison of six upscaling evapotranspiration methods: From site to the satellite pixel. Journal of Geophysical Research: Atmospheres, 2018, 123(13): 6777-6803.查看下载
  7. Ma YF, Liu SM, Song LS, Xu ZW, Liu YL, Xu TR, Zhu ZL. Estimation of daily evapotranspiration and irrigation water efficiency at a Landsat-like scale for an arid irrigation area using multi-source remote sensing data. Remote Sensing of Environment, 2018, 216: 715-734. doi:10.1016/j.rse.2018.07.019.查看
  8. Song LS, Liu SM, William P. Kustas, Hector Nieto, Sun L, Xu ZW, Todd H. Skaggs, Yang Y, Ma MG, Xu TR, Tang XG, Li QP. Monitoring and validating spatially and temporally continuous daily evaporation and transpiration at river basin scale. Remote Sensing of Environment, 2018, 219: 72–88.doi: 10.1016/j.rse.2018.10.002.查看
  9. Xu TR, Bateni S.M., Neale C.M.U., Auligne T., Liu SM. Estimation of turbulent heat fluxes by assimilation of land surface temperature observations from GOES satellites into an ensemble Kalman smoother framework. Journal of Geophysical Research: Atmospheres, 2018, 123(5): 2409-2423. doi: 10.1002/2017JD027732.查看
  10. Jung M, Reichstein M, Margolis H. A., Cescatti A, Richardson AD, Arain MA, Williams, C.. Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations. Journal of Geophysical Research: Biogeosciences, 2011, 16, G00J07. doi: 10.1029/2010JG001566.查看
  11. Xu ZW, Ma YF, Liu SM, Shi SJ, Wang JM. Assessment of the energy balance closure under advective conditions and its impact using remote sensing data. Journal of Applied Meteorology and Climatology, 2017, 56: 127-140, doi: 10.1175/JAMC-D-16-0096.1.查看下载
  12. Song LS, Liu SM, Kustas W P, Zhou J, Xu ZW, Xia T, Li MS. Application of remote sensing-based two-source energy balance model for mapping field surface fluxes with composite and component surface temperatures. Agricultural and Forest Meteorology, 2016, doi:10.1016/j.agrformet.2016.01.005.查看下载
  13. Song LS , Kustas WP, Liu SM, Colaizzi PD, Nieto H, Xu ZW, Ma YF, Li MS, Xu TR, Agam N, Tolk JA, Evett SR. Applications of a thermal-based two-source energy balance model using Priestley-Taylor approach for surface temperature partitioning under advective conditions. Journal of Hydrology, 2016, doi:10.1016/j.jhydrol.2016.06.034.查看下载
  14. Hu MG, Wang JH, Ge Y, Liu MX, Liu SM, Xu ZW, Xu TR. Scaling Flux Tower Observations of Sensible Heat Flux Using Weighted Area-to-Area Regression Kriging. Atmosphere 2015, 6, 1032-1044.查看下载

专题文献

  1. Li X, Cheng GD, Liu SM, Xiao Q, Ma MG, Jin R, Che T, Liu QH, Wang WZ, Qi Y, Wen JG, Li HY, Zhu GF, Guo JW, Ran YH, Wang SG, Zhu ZL, Zhou J, Hu XL, Xu ZW. Heihe Watershed Allied Telemetry Experimental Research (HiWATER): Scientific objectives and experimental design. Bulletin of the American Meteorological Society, 2013, 94(8): 1145-1160, 10.1175/BAMS-D-12-00154.1.查看 | 下载
  2. 李新, 刘绍民, 马明国, 肖青, 柳钦火, 晋锐, 车涛, 王维真, 祁元, 李弘毅, 朱高峰, 郭建文, 冉有华, 闻建光, 王树果. 黑河流域生态-水文过程综合遥感观测联合试验总体设计. 地球科学进展, 2012, 27(5): 481-498.查看 | 下载

数据使用声明

本数据由“黑河生态水文遥感试验(HiWATER)”产生,用户在使用数据时请在正文中明确声明数据的来源,并在参考文献部分引用本元数据提供的引用方式。

资助项目

1.国家自然科学基金重点项目:陆表遥感产品真实性检验中的关键理论与方法研究(项目编号: 41531174)

2.国家重大科学研究计划(973)项目:高分辨率陆表能量水分交换过程的机理与尺度转换研究(项目编号: 2015CB953702)


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    最近10条服务记录如下:

    1. 2019-01-08 中国水利水电科学研究院 易珍言 用途:蒸散发遥感反演及其尺度相关研究
    2. 2018-12-25 中国科学院遥感与数字地球研究所 胡光成 用途:验证遥感估算黑河流域蒸散发

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    • 数据时间范围:2012-01-01 至 2016-12-31
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    • 元数据更新时间:2019-01-09
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