Dataset of ground truth land surface evapotranspiration at the satellite pixel scale in the Heihe River Basin (from the single station observation to satellite pixel scale) Version 1.0

Dataset of ground truth land surface evapotranspiration at the satellite pixel scale in the Heihe River Basin (from the single station observation to satellite pixel scale) Version 1.0


The evapotranspiration (ET) is an important variable connecting land energy balance, water cycle and carbon cycle. Accurate monitoring and estimations of ET are essential not only for water resources management but also for simulating regional, global climate, and hydrological cycles. Remote sensing technology is an effective method to monitor ET. At present, a variety of ET remote sensing products have been produced and released. However, in the process of validation, there is a problem of spatial scale mismatch between ET remote sensing estimation value and station observation value, especially on heterogeneous surface. Therefore, it is very important to obtain the ground truth ET values at the satellite pixel scale by upscaling method on heterogeneous surface. In this study, using the station observation data and multi-source remote sensing information, the ET observed at a single ground station is upscaled to the satellite pixel scale, and the ground truth ET values at the satellite pixel scale in Heihe River Basin is obtained.

Based on the ET data observed by the eddy covariance (EC) at 15 stations (3 superstations and 12 ordinary stations) in the Heihe integrated observatory network, combined with the fused high-resolution remote sensing data (surface temperature, vegetation index, net radiation, etc.) and atmospheric reanalysis data, the upscaling is carried out to obtain the ground truth ET at the satellite pixel scale. The distribution diagram is shown in Figure 1. Specifically, firstly, the spatial heterogeneity of the spatial heterogeneity of the land surface hydrothermal conditions was evaluated; Secondly, nine upscaling methods (the integrated Priestley-Taylor equation method, the Penman-Monteith equation combined with EnKF method, the Penman-Monteith equation combined with SCE_UA method, EC observation value, artificial neural network, Bayesian linear regression, deep belief network, Gaussian process regression, and random fores and directly taking the EC observation value as the ground truth ET) were compared and analyzed through direct validation and cross-validation; Finally, a comprehensive method (directly using the EC observation value on the homogeneous underlying surface; using the Gaussian process regression method for upscaling on the moderately heterogeneous underlying surface and highly heterogeneous underlying surface) was optimized to obtain the groud truth ET at the satellite pixel scale at 15 typical underlying surfaces in Heihe River Basin (2010-2016, spatial resolution of 1km). The results showed that the ground truth ET at the satellite pixel scale is relatively reliable. Compared with the pixel scale reference value (LAS observation value), the MAPE of the ground turth ET at the satellite pixel scale at the three superstations are 1.57%, 3.23% and 4.59% respectively, which can meet the needs of the validation of ET remote sensing products. For all site information and data processing, please refer to Liu et al. (2018), and for upscaling methods, please refer to Li et al. (2021).


File naming and required software

Named by site name, grid TIF format
Can be opened with ArcGIS software


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Cite as:

Liu, S., Li, X., Xu, Z. (2022). Dataset of ground truth land surface evapotranspiration at the satellite pixel scale in the Heihe River Basin (from the single station observation to satellite pixel scale) Version 1.0. National Tibetan Plateau Data Center, DOI: 10.11888/Atmos.tpdc.272509. CSTR: 18406.11.Atmos.tpdc.272509. (Download the reference: RIS | Bibtex )

Related Literatures:

1. Li, X., Liu, S.M., Yang, X.F., Ma, Y.F., He X.L., Xu, Z.W., Xu, T.R., Song, L.S., Zhang, Y., Hu, X., Ju, Q., &Zhang X.D. (2021). Upscaling evapotranspiration from a single-site to satellite pixel scale. Remote Sensing, 13(20), 4072. doi.org/10.3390/rs13204072.( View Details | Bibtex)

2. Liu, S., Li, X., Xu, Z., Che, T., Xiao, Q., Ma, M., Liu, Q., Jin, R., Guo, J., Wang, L., Wang, W., Qi, Y., Li, H., Xu, T., Ran, Y., Hu, X., Shi, S., Zhu, Z., Tan, J., Zhang, Y., Ren, Z. (2018). The Heihe Integrated Observatory Network: A basin‐scale land surface processes observatory in China. Vadose Zone Journal, 17,180072. https://doi.org/10.2136/vzj2018.04.0072.( View Details | Bibtex)

Using this data, the data citation is required to be referenced and the related literatures are suggested to be cited.


References literature

1.Xu, Z.W., Liu, S.M., Zhu, Z.L., Zhou, J., Shi, W.J., Xu, T.R., Yang, X.F., Zhang, Y., & He X.L. (2020). Exploring evapotranspiration changes in a typical endorheic basin through the integrated observatory network. Agricultural and Forest Meteorology, 290, 108010. (View Details )

2.Li, X., Liu, S.M., Li, H.X., Ma, Y.F., Wang, J.H., Zhang, Y., Xu, Z.W., Xu, T.R., Song, L.S., Yang, X.F., Lu, Z., Wang, Z.Y., Guo, Z.X. (2018). Intercomparison of six upscaling evapotranspiration methods: From site to the satellite pixel. Journal of Geophysical Research: Atmospheres, 123(13), 6777-6803. https://doi.org/10.1029/2018JD028422. (View Details )

3.Ma, Y.F., Liu, S.M., Song, L.S., Xu, Z.W., Liu, Y.L., Xu, T.R., Zhu, Z.L. (2018). 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, 216, 715-734. doi:10.1016/j.rse.2018.07.019. (View Details )

4.Li, X., Liu, S.M., Xiao, Q., Ma, M.G., Jin, R., Che, T., Wang, W.Z., Hu, X.L., Xu, Z.W., Wen, J.G., Wang, L.X. (2017). A multiscale dataset for understanding complex eco-hydrological processes in a heterogeneous oasis system. Scientific Data, 4, 170083. doi:10.1038/sdata.2017.83. (View Details | Download )

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9.Wang, J.M., Zhuang, J.X., Wang, W.Z., Liu, S.M., &Xu, Z.W. (2015). Assessment of uncertainties in eddy covariance flux measurement based on intensive flux matrix of HiWATER-MUSOEXE. IEEE Geoscience and Remote Sensing Letters, 12(2), 259-263. (View Details )

10.Su, P.X., Yan, Q.D., Xie, T.T., Zhou,Z.J., & Gao, S. (2012). Associated growth of C3 and C4 desert plants helps the C3 species at the cost of the C4 species. Acta Physiologiae Plantarum, 34(6), 2057-2068. (View Details )

11.Ge, Y., Liang, Y.Z., Wang, J.H., Zhao, Q.Y., &Liu, S.M. (2015). Upscaling sensible heat fluxes with area-to-area regression kriging. IEEE Geoscience and Remote Sensing Letters, 12(3), 656-660. (View Details )

12.Bai, J., Jia, L., Liu, S., Xu, Z., Hu, G., Zhu, M., &Song, L. (2015). Characterizing the Footprint of Eddy Covariance System and Large Aperture Scintillometer Measurements to Validate Satellite-Based Surface Fluxes. IEEE Geoscience and Remote Sensing Letters, 12(5), 943-947. (View Details | Download )

13.Hu, M.G., Wang, J.H., Ge, Y., Liu, M.X., Liu, S.M., Xu, Z.W., &Xu, T.R. (2015). Scaling Flux Tower Observations of Sensible Heat Flux Using Weighted Area-to-Area Regression Kriging. Atmosphere, 6(8), 1032-1044. (View Details | Download )

14.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. (View Details )

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Keywords
Geographic coverage
East: 102.00 West: 97.00
South: 37.80 North: 42.70
Details
  • Temporal resolution: Daily
  • Spatial resolution: 100m - 1km
  • File size: 7 MB
  • Views: 1,964
  • Downloads: 26
  • Access: Requestable
  • Temporal coverage: 2010-01-01 To 2016-12-31
  • Updated time: 2022-06-10
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