Abstract

Solar-induced fluorescence (SIF) is an important indicator of terrestrial photosynthesis and an increasingly targeted observable in spaceborne remote sensing. Here, we present results from efforts to harmonize data across multiple sensors in order to create long-term records that are suitable for multi-decadal analyses. Nevertheless, discontinuities in harmonized records and non-linearities in the relationship between SIF and gross primary production (GPP) demand the use of model enhanced approaches to bridge the gap between observed SIF and inferred GPP. Bayesian model-data fusion (MDF) provides an increasingly established and effective tool to reconcile different satellite datasets and systematically retrieve otherwise unobserved quantities (i.e., not directly measured by spaceborne sensors) such as biomass, leaf area, and GPP, and more accurately estimate interactions between carbon pools and changing climate. Here, we apply the CARbon DAta–MOdel fraMework (CARDAMOM) MDF system to (i) optimize the parameters and initial states of a terrestrial ecosystem model against global harmonized SIF datasets and ancillary vegetation products to constrain terrestrial photosynthesis and generate reanalyses of GPP, (ii) propagate uncertainty from SIF observations into the GPP reanalyses, and (iii) diagnose these reanalyses to examine the current and evolving state of photosynthesis with climate. Using two SIF products derived from OCO-2 and GOME-2/SCIAMACHY, respectively, we have produced two twenty-year reanalyses of global GPP at a monthly, 0.5-degree resolution. The variability of GPP in these products is well constrained by their respective SIF observations, and reproduces a significant fraction of the observed spatial and interannual variability from globally distributed flux towers. Our results provide a basis for further investigation of photosynthetic carbon uptake as a data product available for the research community.

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