Monitoring terrestrial net primary productivity of China using BIOME-BGC model based on remote sensing

Geographically referenced gross primary productivity (GPP) and net primary productivity (NPP), along with their corresponding fluctuations, a re vital to enhance our understanding of functioning of living ecosystems. In this study, we used a process-based model, BIOME-BGC, which is based on the FOREST-BGC model, to simulate spatial patterns of GPP and NPP over the entire terrestrial land of China. The model was run at ten-day time steps by using 1 km Advanced Very High Resolution Radiometer (AVHRR) data of 10-day composite from NOAA satellite series which had been processed on accurate geometric correction, radiance calibration, atmospheric correction and cloud masking, as well as the daily meteorological data from more than 300 weather stations. Differences of NPP between different land covers were examined and compared. The results indicate that in 1999, the total NPP of terrestrial land of China is 1.65x109 t and the average is 174.45 gC/m(2). The highest NPP was in deciduous conifer forest, deciduous broadleaf forest, sparse woods and farmland, with the average of 346.66 gC/m(2), 318.77 gC/m(2), 309.20 gC/m(2) and 300.47 gC/m(2) respectively and the lowest was in glacier, desert and gravel, with the average of 12.65 gC/m(2), 14.51 gC/m(2) and 16.73 gC/m(2) respectively.