Estimation of global GPP from GOME-2 and OCO-2 SIF by considering the dynamic variations of GPP-SIF relationship

Previous studies have indicated that gross primary production (GPP) and solar-induced chlorophyll fluorescence (SIF) have a strong linear relationship, and usually exhibit similar spatial and temporal patterns. However, the responses of GPP and SIF to the environment may be different, which will lead to a variant GPP-SIF relationship. To better investigate the impact of the dynamics in GPP-SIF relationship on GPP estimation, we established two GPP models. An inconstant GPP/SIF ratio model (Dynamic-Ratio model, DR model) was first established using meteorological variables and leaf area index (LAI) based on random forest regression algorithm. The model was then used to estimate GPP (referred to as GPP_DR) with different satellite SIF datasets i.e., downscaled fine resolution SIF from the Orbiting Carbon Observatory-2 (GOSIF) and Global Ozone Monitoring Experiment-2 SIF (downscaled GOME-2 SIF). The second model (SIF-Climate-LAI model, SCL model) was also based on the random forest algorithm but was directly driven by meteorological variables, LAI and SIF data, and no GPP/SIF ratio was used in the model. As a comparison, the linear relationship between GPP and SIF was also established using eddy covariance tower GPP (GPP_EC) and SIF datasets based on linear regression without considering variations of GPP-SIF relationship (Fixed-Ratio model, FR model). Considering the spatio-temporal variations of GPP-SIF relationship can improve the GPP simulation to a certain extent by mitigating the underestimation of peak GPP values. This improvement was found for both DR and SCL models. Owing to the dynamic variations of GPP/ SIF ratio and associated uncertainties, the performance of DR model was not as good as that of SCL model. GPP estimation derived from GOSIF matched better with GPP_EC than that from downscaled GOME-2 SIF for DR, SCL and FR models. Our findings suggested that GPP can be better derived from satellite SIF by considering the variations of GPP-SIF relationship.

Automated summary

Previous studies have shown that GPP and SIF have a strong linear relationship and exhibit similar spatial and temporal patterns. However, their responses to the environment may differ. To investigate the impact of the dynamics in GPP-SIF relationship on GPP estimation, two GPP models were established. Considering the variations of GPP-SIF relationship can improve GPP simulation to a certain extent, but the performance of one model is not as good as the other due to associated uncertainties.