China’s deserts greening and response to climate variability and human activities

annual NDVI data for each pixel to reduce the atmospheric effects of clouds and aerosols [47– 49]. The mean values for spring (March-May), summer (June-August), autumn (September- November), and winter (January, February, and December) were calculated separately to truly reflect seasonal differences in the NDVI. Otherwise, the maximum values of the NDVI for spring, summer, and autumn would be very similar, but a large gap would appear for winter. Climate data. Data on the monthly mean temperature and monthly accumulated precipi- tation from 2000 to 2017 at a 1 km spatial resolution were obtained from the National Tibetan Plateau Data Center (http://data.tpdc.ac.cn) [50–52]. The original climate data were merged, projection transformed, masked, and registered to be consistent with the NDVI data. The sea- sonal and annual mean temperature and accumulated precipitation were calculated from monthly values to aid the analysis. Multi-year averages of temperature (Fig 1(b)) and precipita- tion (Fig 1(c)) from 2000 to 2017 were also calculated for understanding the climatic condi- tions of the study area. Fig 1. Spatial distribution of deserts in China(a), and mean annual temperature(b) and precipitation(c) of China’s deserts from 2000 to 2017. https://doi.org/10.1371/journal.pone.0256462.g001 PLOS ONE China’s deserts greening and response to climate variability and human activities PLOS ONE | https://doi.org/10.1371/journal.pone.0256462 August 30, 2021 4 / 20