<p>This data includes NPP grid data from five Central Asian countries and Xinjiang in China for 12 months in 2016. Combined with data such as land use, temperature, vegetation index, rainfall, solar radiation and evapotranspiration, the NPP is calculated and inverted with the help of CASA model. The spatial resolution is 10km×10km and the temporal resolution is month. Each file has 12 bands, which correspond to the NPP results for each month of the year, and the projection is a sinusoidal projection. In the context of climate change, it can be used to analyze the correlation between meteorological elements and vegetation characteristics, and can also be combined with other vegetation data and ecological data to analyze land degradation. </p>
Remote sensing data such as land use, temperature, vegetation index, rainfall, solar radiation and evapotranspiration.
Combined with data such as land use, temperature, vegetation index, rainfall, solar radiation and evapotranspiration, the NPP is obtained by calculation and inversion using CASA model. The Carnegie Ames-Stanford Approach Model (CASA) is a model for estimating ecosystem net primary productivity (NPP) based on remote sensing data and meteorological data. It was jointly developed by the Carnegie Institution, the Ames Research Center and Stanford University and is mainly used to assess the amount of carbon fixed by vegetation through photosynthesis.
The spatial resolution is 10km×10km, the temporal resolution is months, and each file has 1 band, corresponding to the NPP results for each month.
| # | number | name | type |
| 1 | XDA20000000 | Other |
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CSTR:33110.11.ariddc.00127
10.12389/ariddc.00127
