Recently, Prof. ZHANG Yongqiang of the Institute of Geographic Sciences and Natural Resources Research of the Chinese Academy of Sciences published the PML_V2 global evapotranspiration and gross primary production (2002.07-2019.08) dataset in the National Tibetan Plateau Data Center. This dataset includes gross primary product (GPP), vegetation transpiration (Ec), soil evaporation (Es), vaporization of intercepted rainfall (Ei), and water body, ice and snow evaporation (ET_water) – a total of five elements. The data format is tiff; the space-time resolution is 8 days, 0.05°; and the time span is from 2002.07-2019.08.
Based on the Penman-Monteith-Leuning (PML) model, PML_V2 is coupled with the GPP process based on stomatal conductance theory. ET simulation by PML_V2 is greatly improved compared with the previous model because GPP and ET mutually restrict each other. The parameters of PML_V2 are divided into different vegetation types and are determined by 95 vorticity-related flux stations around the world. The parameters were then migrated globally according to the MODIS MCD12Q2.006 IGBP classification. PML_V2 uses GLDAS 2.1 meteorological drive and MODIS leaf area index (LAI), reflectivity (Albedo) and emissivity (Emissivity) as inputs, then finally obtains PML_V2 terrestrial evapotranspiration and gross primary production as outputs.
This dataset can provide an important basis for the study of changes in water heat flux and vegetation gross primary production in the context of climate change. In addition, the PML_V2 model is of great significance for geoscientists when building big data ecological and hydrological models on the GEE platform. Scientists are welcome to download and use the dataset to improve the model structure.
The research work was carried out by a team led by Prof. ZHANG Yongqiang. The results were published in Remote Sensing of Environment, in an article entitled “Coupled estimation of 500 m and 8-day resolution global evapotranspiration and gross primary production in 2002–2017.” The research was supported by the Hundred Talents Program of the Chinese Academy of Sciences.
Link to data: https://data.tpdc.ac.cn/en/data/48c16a8d-d307-4973-abab-972e9449627c/
Link to article: https://www.sciencedirect.com/science/article/pii/S003442571830590X
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