Accurate evapotranspiration (ET) estimation is important for understanding hydrological cycle and water resources management in the cropland. Based on eight flux sites within the North China Plain (NCP) and the surrounding area, which were integrated together for the first time, we applied support vector regression method to develop ET dataset for the cropland in NCP from 2001 to 2015 with 1km spatial resolution and eight-day temporal interval.

File naming and required software

The data format of the original SVR-ET datasets are .mat (float), which can be read by Matlab. A simple Matlab program named ‘mat2asc.m’ is provided to transform the .mat files into .asc files for your consideration. Moreover, two corresponding .asc files are provided as examples, which also show the spatial extents of the datasets. The projection files (.prj) are also provided.
The data unit of the SVR-ET dataset is Wm-2, and there are 46 digits for each pixel in time series within each year. The values of the first 45 digits are the averages of the corresponding eight days for each pixel (i.e. the first value is the average of the first day of the year to the eighth day of the year). The last digit is the average of the last five (nonleap year) or six days (leap year) for each year. The ‘NaN’ values within the datasets refer to the invalid data (e.g., non-cropland, outside the boundary).
The shapefile of NCP is named ‘NCP_bountary’ for your consideration.

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Cite as:

Lei, H. (2021). An evapotranspiration dataset based on upscaling eddy covariance observations over the dryland of the North China Plain (2001-2015). A Big Earth Data Platform for Three Poles, DOI: 10.11888/Hydro.tpdc.271661. CSTR: 18406.11.Hydro.tpdc.271661. (Download the reference： RIS | Bibtex )

Related Literatures:

1. Fang, B. J., Lei, H. M., Zhang, Y. C., Quan, Q., & Yang, D. W. (2020). Spatio-temporal patterns of evapotranspiration based on upscaling eddy covariance measurements in the dryland of the North China Plain. Agricultural and Forest Meteorology, 281. doi:ARTN 10784410.1016/j.agrformet.2019.107844( View Details | Bibtex)

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

Support Program

The National Natural Science Foundation (No:51922063)

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