This dataset contains daily land surface evapotranspiration products of 2020 in Qilian Mountain area. It has 0.01 degree spatial resolution. The dataset was produced based on Gaussian Process Regression (GPR) method by fusing six satellite-derived evapotranspiration products including RS-PM (Mu et al., 2011), SW (Shuttleworth and Wallace., 1985), PT-JPL (Fisher et al., 2008), MS-PT (Yao et al., 2013), SEMI-PM (Wang et al., 2010a) and SIM (Wang et al.2008). The input variables for the evapotranspiration products include MODIS products, MERRA meteorological data, and China Meteorological Forcing Dataset.

File naming and required software

File Naming Convention: YYYYMMDD.tiff (YYYY: year, MM: month, DD: day)
Data Version：V1.0
Projection：+proj=longlat +datum=WGS84 +no_defs
Data Format: GeoTIFF, 1079 rows ×1791 columes
Unit: W/m2
Valid Range：0~500
Filled Value：-300

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Cite as:

Yao, Y., Liu, S., Shang, K. (2021). Daily MODIS-based Land Surface Evapotranspiration Dataset of 2020 in Qilian Mountain Area (ETHi-merge V1.0). National Tibetan Plateau Data Center, DOI: 10.11888/Geogra.tpdc.271410. CSTR: 18406.11.Geogra.tpdc.271410. (Download the reference： RIS | Bibtex )

Related Literatures:

1. Yao, Y., Liang S., Li X., Chen J., & Liu S., et al. (2017). Improving global terrestrial evapotranspiration estimation using support vector machine by integrating three process-based algorithms. Agricultural and Forest Meteorology, 242, 55-74. DOI: 10.1016/j.agrformet.2017.04.011.( 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.Fisher, J., Tu, K., & Baldocchi, D. (2008). Global estimates of the land–atmosphere water flux based on monthly AVHRR and ISLSCP-II data, validated at 16 FLUXNET sites. Remote Sensing of Environment, 112(3), 901-919. DOI: 10.1016/j.rse.2007.06.025. (View Details )

2.Mu, Q., Zhao, M., & Running S. (2011). Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sensing of Environment, 115(8), 1781-1800. DOI: 10.1016/j.rse.2011.02.019. (View Details )

3.Wang, K., Liang, S. (2008). An improved method for estimating global evapotranspiration based on satellite determination of surface net radiation, vegetation index, temperature, and soil moisture. J. Hydrometeorol, 9, 712–727. DOI: 10.1109/IGARSS.2008.4779489. (View Details )

4.Yao, Y., Liang, S., Cheng, J., Liu, S., & Fisher, J., et al. (2013). MODIS-driven estimation of terrestrial latent heat flux in China based on a modified Priestly-Taylor algorithm. Agricultural and Forest Meteorology, 171–172, 187–202. DOI: 10.1016/j.agrformet.2012.11.016. (View Details )

5.Shuttleworth, W., Wallace, J. (1985). Evaporation from sparse crops-An energy combination theory, 111, 839–855. DOI: 10.1002/qj.49711146910. (View Details )

6.Wang, K., Dickinson, R., & Wild, M., et al. (2010). Evidence for decadal variation in global terrestrial evapotranspiration between 1982 and 2002: 1. Model development. Journal of Geophysical Research Atmospheres, 115(D20), 898-907. DOI: 10.1029/2009JD013671. (View Details )

Support Program

Pan-Third Pole Environment Study for a Green Silk Road-A CAS Strategic Priority A Program (No:XDA20000000)

the Strategic Priority Research Program of the Chinese Academy of Sciences (No:XDA20100101) [XDA20100101]

Copyright & License

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