Dataset of ground truth of land surface evapotranspiration at regional scale in the Heihe River Basin (2012-2016) 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


Surface evapotranspiration (ET) is an important variable that connects the land energy balance, water cycle and carbon cycle. The accurate acquisition of ET is helpful to the research of global climate change, crop yield estimation, drought monitoring, and it is of great significance to regional and global water resource planning and management. The methods of obtaining evapotranspiration mainly include ground observation, remote sensing estimation, model simulation and assimilation. The high-precision surface evapotranspiration data can be obtained by ground observation, but the spatial representation of observation stations is very limited; remote sensing estimation, model simulation and assimilation methods can obtain the spatial continuous surface evapotranspiration, but there are problems in the verification of accuracy and the rationality of spatial-temporal distribution pattern. Therefore, this study makes full use of a large number of high-precision station observation data, combined with multi-source remote sensing information, to expand the observation scale of ground stations to the region, to obtain high-precision, spatiotemporal distribution of continuous surface evapotranspiration.

Based on the "Heihe River Integrated Remote Sensing joint experiment" (water), "Heihe River Basin Ecological hydrological process integrated remote sensing observation joint experiment" (hiwater), the accumulated station observation data (automatic meteorological station, eddy correlator, large aperture scintillation instrument, etc.), 36 stations (65 station years, distribution map is shown in Figure 1) are selected in combination with multi-source remote sensing data (land cover) Five machine learning methods (regression tree, random forest, artificial neural network, support vector machine, depth belief network) were used to construct different scale expansion models of surface evapotranspiration, and the results showed that: compared with The other four methods, random forest method, are more suitable for the study of the scale expansion of surface evapotranspiration from station to region in Heihe River Basin. Based on the selected random forest scale expansion model, taking remote sensing and air driven data as input, the surface evapotranspiration time-space distribution map (etmap) of Heihe River Basin during the growth season (May to September) from 2012 to 2016 was produced. The results show that the overall accuracy of etmap is good. The RMSE (MAPE) of upstream (las1), midstream (las2-las5) and downstream (las6-las8) are 0.65 mm / day (18.86%), 0.99 mm / day (19.13%) and 0.91 mm / day (22.82%), respectively. In a word, etmap is a high-precision evapotranspiration product in Heihe River Basin, which is based on the observation data of stations and the scale expansion of random forest algorithm. Please refer to Xu et al. (2018) for all station information and scale expansion methods, and Liu et al. (2018) for observation data processing.


Data Citations Data citation guideline What's data citation?
Cite as:

Liu, S., Xu, T. (2019). Dataset of ground truth of land surface evapotranspiration at regional scale in the Heihe River Basin (2012-2016) ETMap Version 1.0. A Big Earth Data Platform for Three Poles, DOI: 10.11888/Meteoro.tpdc.270141. CSTR: 18406.11.Meteoro.tpdc.270141. (Download the reference: RIS | Bibtex )

Related Literatures:

1. Xu, T.R., Guo, Z.X., Liu, S.M., He, X.L., Meng, Y.F.Y., Xu, Z.W., Xia, Y.L., Xiao, J.F., Zhang, Y., Ma, Y.F, Song, L.S. (2018). Evaluating Different Machine Learning Methods for Upscaling Evapotranspiration from Flux Towers to the Regional Scale. Journal of Geophysical Research: Atmospheres, 123(16), 8674-8690. doi: 10.1029/2018JD028447.( 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.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 )

2.Song, L.S., Kustas WP, Liu, S.M., Colaizzi PD, Nieto H, Xu, Z.W., Ma, Y.F., Li, M.S., Xu, T.R., Agam, N., Tolk, J., & Evett, S. (2016). Applications of a thermal-based two-source energy balance model using Priestley-Taylor approach for surface temperature partitioning under advective conditions. Journal of Hydrology, doi:10.1016/j.jhydrol.2016.06.034. (View Details )

3.Song, L.S., Liu, S.M., Kustas, W.P., Zhou, J., Xu, Z.W., Xia, T., & Li, M.S. (2016). 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, 230-231, 8-19. (View Details | Download )

4.Liu, S.M., Xu, Z.W., Song, L.S., Zhao, Q.Y., Ge, Y., Xu, T.R., Ma, Y.F., Zhu, Z.L., Jia, Z.Z., &Zhang, F. (2016). Upscaling evapotranspiration measurements from multi-site to the satellite pixel scale over heterogeneous land surfaces. Agricultural and Forest Meteorology, 230-231, 97-113. (View Details | Download )

5.Xu, Z.W., Ma, Y.F., Liu, S.M., Shi, S.J., &Wang, J.M. (2017). Assessment of the energy balance closure under advective conditions and its impact using remote sensing data. Journal of Applied Meteorology and Climatology, 56, 127-140. (View Details | Download )

6.Li Xin, Liu Shaomin, Ma Mingguo, Xiao Qing, Liu Qinhuo, Jin Rui, Che Tao. HiWATER: An Integrated Remote Sensing Experiment on Hydrological and Ecological Processes in the Heihe River Basin. Advances in Earth Science, 2012, 27(5): 481-498. (View Details | Download )

7.Xu, T.R., He, X.L., Bateni, S.M., Auligne, T., Liu, S.M., Xu, Z.W., Zhou, J., Mao, K.B. (2019). Mapping Regional Turbulent Heat Fluxes via Variational Assimilation of Land Surface Temperature Data from Polar Orbiting Satellites. Remote Sensing of Environment, 221, 444-461, doi.org/10.1016/j.rse.2018.11.023 (View Details )

8.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 )

9.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 )

10.Song, L.S., Liu, S.M., William P, K., Hector, N., Sun, L., Xu, Z.W., Todd H, S., Yang, Y., Ma, M.G., Xu, T.R., Tang, X.G., Li, Q.P. (2018). Monitoring and validating spatially and temporally continuous daily evaporation and transpiration at river basin scale. Remote Sensing of Environment, 219, 72–88. doi: 10.1016/j.rse.2018.10.002. (View Details )

11.Liu, S.M., Xu, Z.W., Zhu, Z.L., Jia, Z.Z., &Zhu, M.J. (2013). Measurements of evapotranspiration from eddy-covariance systems and large aperture scintillometers in the Hai River Basin, China. Journal of Hydrology, 487, 24-38. (View Details )

12.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 )

13.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 )


Support Program

(No:41531174)

Copyright & License

To respect the intellectual property rights, protect the rights of data authors, expand services of the data center, and evaluate the application potential of data, data users should clearly indicate the source of the data and the author of the data in the research results generated by using the data (including published papers, articles, data products, and unpublished research reports, data products and other results). For re-posting (second or multiple releases) data, the author must also indicate the source of the original data.


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


Related Resources
Comments

Current page automatically show English comments Show comments in all languages

Download Follow
Keywords
Geographic coverage
East: 102.00 West: 97.00
South: 37.80 North: 42.70
Details
  • Temporal resolution: Yearly
  • File size: 1 MB
  • Views: 11764
  • Downloads: 87
  • Access: Requestable
  • Temporal coverage: 2012-01-14 To 2017-01-13
  • Updated time: 2021-04-19
Contacts
: LIU Shaomin   XU Tongren   

Distributor: A Big Earth Data Platform for Three Poles

Email: poles@itpcas.ac.cn

Export metadata