Fusing Multisensor Data to Generate a Highly Integrated Global Solar-Induced Chlorophyll Fluorescence (FSIF) Time Series

Satellite solar-induced chlorophyll fluorescence (SIF) provides a new opportunity to quantify vegetation photosynthesis at large scales, but individual satellite observations are usually constrained by their respective limitations regarding spatial continuity or temporal coverage. Although various reconstructed SIF products have already been generated, their spatiotemporal variability remains highly dependent on selected explanatory variables, and complementary information from multiple sensors has not been fully exploited. Here, we developed a multi-source SIF fusion framework to integrate the respective strengths of the Global Ozone Monitoring Experiment-2 (GOME-2), Orbiting Carbon Observatory-2 (OCO-2), and TROPOspheric Monitoring Instrument (TROPOMI) products, to generate a Fusion-based global continuous SIF time series with high quality (FSIF). Specifically, a statistical spatiotemporal fusion (STF) approach is first employed to preliminarily merge the spatial resolution and timespan advantages across GOME-2 and TROPOMI SIF observations, and followed by a Light Gradient Boosting Machine (LightGBM) model to extract the information from auxiliary data and OCO-2 SIF. FSIF covers the full period observed by the three sensors from 2008 to 2022, with a 0.05-degree and 8-day resolution. The spatial distribution of FSIF is consistent with TROPOMI SIF (R2=0.91, p2difference of 0.09, which benefits from the superiority of OCO-2 SIF in GPP representation. Extensive factorial experiments also declare the importance of fusing each sensor and the significance of coupling STF- machine learning (ML) models.