This dataset contains daily land surface evapotranspiration products of 2018 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 and China Meteorological Forcing Dataset (He Jie, Yang Kun. China Meteorological Forcing Dataset. Cold and Arid Regions Science Data Center at Lanzhou, 2011. doi:10.3972/westdc.002.2014.db).

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。

Data Citations Data citation guideline What's data citation?

Cite as:

Yao, Y., Liu, S., Shang, K. (2019). Daily MODIS-based land surface evapotranspiration dataset in Qilian Mountain Area (ETHi-merge V1) (2018). National Tibetan Plateau Data Center, DOI: 10.11888/Geogra.tpdc.270192. CSTR: 18406.11.Geogra.tpdc.270192. (Download the reference： RIS | Bibtex )

Related Literatures:

1. Yao, Y. J., Liang, S. L., Li, X. L., Chen, J. Q., Liu, S. M., Jia, K., Zhang, X. T., Xiao, Z. Q., Fisher, J. B., Mu, Q. Z., Pan, M., Liu, M., Cheng, J., Jiang, B., Xie, X. H., Grünwald, T., Bernhofer, C., & Roupsard, O. (2017). Improving global terrestrial evapotranspiration estimation using support vector machine by integrating three process-based algorithms. Agricultural and Forest Meteorology, 242, 55-74.( 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.Mu Q. Z., Zhao M. S., & Running S. W. (2011). Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sensing of Environment, 115(8), 1781-1800. (View Details )

2.Shuttleworth, W. J., & Wallace, J. S. (1985). Evaporation from sparse crops-An energy combination theory. Quarterly Journal of the Royal Meteorological Society, 111, 839–855. (View Details )

3.Fisher J. B., Tu K. P., & Baldocchi D. 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. (View Details )

4.Yao, Y. J., Liang, S. L., Cheng, J., Liu, S. M., Fisher, J. B., Zhang, X. D., Jia, K., Zhao, X., Qin, Q. M., Zhao, B., Han, S. J., Zhou, G. S., Zhou, G. Y., Li, Y. L., & Zhao, S. H. (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. (View Details )

5.Wang K. C., Dickinson R. E., Wild, M., & Liang, S. L. (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. (View Details )

6.Wang K. C., & Liang S. L. (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. (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]

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