Improved Global Gross Primary Productivity Estimation by Considering Canopy Nitrogen Concentrations and Multiple Environmental Factors

The terrestrial gross primary productivity (GPP) plays a crucial role in regional or global ecological environment monitoring and carbon cycle research. Many previous studies have produced multiple products using different models, but there are still significant differences between these products. This study generated a global GPP dataset (NI-LUE GPP) with 0.05(?) spatial resolution and at 8 day-intervals from 2001 to 2018 based on an improved light use efficiency (LUE) model that simultaneously considered temperature, water, atmospheric CO2 concentrations, radiation components, and nitrogen (N) index. To simulate the global GPP, we mapped the global optimal ecosystem temperatures (T- opt(eco)) using satellite-retrieved solar-induced chlorophyll fluorescence (SIF) and applied it to calculate temperature stress. In addition, green chlorophyll index (CIgreen), which had a strong correlation with the measured canopy N concentrations (r = 0.82), was selected as the vegetation index to characterize the canopy N concentrations to calculate the spatiotemporal dynamic maximum light use efficiency (epsilon max). Multiple existing global GPP datasets were used for comparison. Verified by FLUXNET GPP, our product performed well on daily and yearly scales. NI-LUE GPP indicated that the mean global annual GPP is 129.69 +/- 3.11 Pg C with an increasing trend of 0.53 Pg C/yr from 2001 to 2018. By calculating the SPAtial Efficiency (SPAEF) with other products, we found that NI-LUE GPP has good spatial consistency, which indicated that our product has a reasonable spatial pattern. This product provides a reliable and alternative dataset for large-scale carbon cycle research and monitoring long-term GPP variations.

Automated summary

This study generated a global GPP dataset based on an improved LUE model, considering temperature, water, atmospheric CO2 concentrations, radiation components, and nitrogen index. The dataset showed good spatial consistency and provides a reliable alternative for large-scale carbon cycle research and long-term GPP monitoring.