Abstract
<p>The net primary productivity (NPP) reflects the growth or production of terrestrial vegetation and plays an important role in the carbon cycle on the earth. It quantifies the difference between the organic matter produced by photosynthesis and the loss of maintenance and growth respiration. The investigation of the spatio-temporal variation in NPP is significant for monitoring plant photosynthesis and carbon uptake in terrestrial ecosystems. In this study, the variability and trend of NPP in China during 2001-2017 are analysed using level 4 MODIS product (MOD17A2H). Additionally, to explore whether the NPP change in recent decades are related with the photosynthetically active radiation (PAR) variation caused by increasing aerosol loading, the correlation between NPP, PAR and aerosol optical depth (AOD) are analysed at national, regional, and pixel scales. The results show that the annual mean NPP shows higher values in the southeast than in the northwest. The highest NPP level above 2.5 gCm<sup>-2</sup>day<sup>-1</sup> is mainly distributed in tropical humid regions, including Zhejiang, Fujian, Guangdong and western Yunnan. The NPP increases with an amplitude of 0.131 gCm<sup>-2</sup>day<sup>-1</sup> during the study period. The forests have higher mean levels of NPP (1.808 gCm<sup>-2</sup>day<sup>-1</sup>) and larger increasing magnitudes (0.35 gCm<sup>-2</sup>day<sup>-1</sup>) than those of croplands and grasslands. The NPP and AOD show a negative correlation (-0.6<R<-0.2) at a significance level of 0.05 over the middle area of China. The PAR direct and diffuse components generally have positive (0<R<sub>PARdir_NPP</sub><0.6) and negative correlations (-0.6<R<sub>PARdif_NPP</sub><0) with NPP, respectively, in most of China except the northeast and Tibetan Plateau. The NPP have stronger correlations (0.215 and -0.218) with the direct and diffuse PAR in forests than in croplands and grasslands, implying that NPP is more sensitive to the change in PAR in forests than in other vegetation cover types.</p>
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