Net primary productivity of Chinaʹs terrestrial ecosystems from a process model driven by remote sensing

The terrestrial carbon cycle is one of the foci in global climate change research. Simulating net primary productivity (NPP) of terrestrial ecosystems is important for carbon cycle research. In this study, Chinaʹs terrestrial NPP was simulated using the Boreal Ecosystem Productivity Simulator (BEPS), a carbon-water coupled process model based on remote sensing inputs. For these purposes, a national-wide database (including leaf area index, land cover, meteorology, vegetation and soil) at a 1 km resolution and a validation database were established. Using these databases and BEPS, daily maps of NPP for the entire Chinaʹs landmass in 2001 were produced, and gross primary productivity (GPP) and autotrophic respiration (RA) were estimated. Using the simulated results, we explore temporal–spatial patterns of Chinaʹs terrestrial NPP and the mechanisms of its responses to various environmental factors. The total NPP and mean NPP of Chinaʹs landmass were 2.235 GtC and 235.2 gC m−2 yr−1, respectively; the total GPP and mean GPP were 4.418 GtC and 465 gC m−2 yr−1; and the total RA and mean RA were 2.227 GtC and 234 gC m−2 yr−1, respectively. On average, NPP was 50.6% of GPP. In addition, statistical analysis of NPP of different land cover types was conducted, and spatiotemporal patterns of NPP were investigated. The response of NPP to changes in some key factors such as LAI, precipitation, temperature, solar radiation, VPD and AWC are evaluated and discussed.