Assessing the Role of Global Satellite-Derived SIF and Vegetation Traits as Proxy Predictors of Gross Primary Productivity

Solar-induced chlorophyll fluorescence (SIF) provides a direct optical proxy of photosynthetic activity and has shown strong empirical relationships with gross primary productivity (GPP). However, the spatiotemporal extent to which satellite-derived SIF and vegetation traits encode meteorological constraints for prediction of GPP remains insufficiently understood. In this study, we analyze the relationships among SIF, vegetation structural variables, meteorological drivers, and GPP using global satellite and reanalysis datasets. Two global Gaussian process regression (GPR)-based GPP products were intercompared: (i) a hybrid model driven by TROPOMI-derived SIF (TROPOSIF), Sentinel-3-derived vegetation traits, and three ERA5-Land key meteorological variables; and (ii) a tower-driven empirical model based on additional meteorological predictors and MODIS-derived leaf area index (LAI). The results showed strong agreement between the two products (median R=0.76), suggesting that satellite-derived SIF and vegetation traits capture variability associated with meteorological forcing that is relevant for GPP prediction. Next, Pearson correlation worldwide and multivariate Granger causality over Europe were analysed in 2019 to investigate dependencies among meteorological variables, vegetation traits, and TROPOSIF. The results indicate that incoming shortwave radiation, temperature, soil moisture, latent heat flux, and leaf area index, in combination, significantly influence SIF dynamics over large regions. Together, the findings highlight the complementary role of satellite SIF and vegetation traits as proxy predictors of ecosystem productivity monitoring, and provide insights relevant for the recently launched FLEX mission.