New Global MuSyQ GPP/NPP Remote Sensing Products from 1981 to 2018

Abstract

Long time series of vegetation productivity products are significant for the research of global carbon cycle and climate change. In this article, the 0.05° global gross primary productivity (GPP) and net primary productivity (NPP) products from 1981 to 2018 were estimated by using the improved multisource data synergized quantitative (MuSyQ) NPP algorithm. The model was based on the fraction of absorbed photosynthetically active radiation (FPAR) and leaf area index (LAI) data from the global land surface satellite (GLASS) dataset, the light use efficiency (LUE) from the parameterization approach with the clearness index (CI), the ERA-Interim meteorological data, and other environmental factors. The results suggested that the accuracy of the MuSyQ GPP product was slightly higher than that of the MOD17 GPP product when compared with the FLUXNET GPP, especially for the evergreen broadleaf forest (EBF), deciduous broadleaf forest (DBF), wetland (WET), cropland (CRO), woody savanna (WSAV), and closed shrubland (CSH) land types. MuSyQ NPP product also has higher accuracy [R2 = 0.81, RMSE = 214.6 gC/(m2year)] than MOD17 NPP [R2 = 0.55, RMSE = 214.7 gC/(m2year)] when compared with the BigFoot NPP, which indicated the reliability of the improved MuSyQ-NPP algorithm in estimating global NPP. Our results showed a significant upward trend in global NPP, which was most affected by FPAR, followed by LUE, temperature, and PAR. The average NPP declined significantly in Asia and Amazon tropical rainforests and increased significantly in Africa tropical rainforest, which were affected by the local deforestation or the forest expansion, and also the climate factors.