Satellite‐Derived Leaf Photosynthetic Capacity Data Set Improves Atmospheric Inversion of Terrestrial Carbon Fluxes

Abstract Accurately estimating the terrestrial carbon sink is crucial for understanding the global carbon cycle. Here, we examine how different parameterizations of the key leaf photosynthetic capacity parameter, namely the maximum Rubisco carboxylation rate normalized to 25°C ( V cmax25 ), influence terrestrial carbon flux estimates within an atmospheric inversion system. We demonstrate that using a spatially heterogeneous and seasonally varying V cmax25 data set derived from satellite solar‐induced fluorescence (SIF) and leaf chlorophyll content (LCC) yields more realistic spatial patterns of net ecosystem exchange (NEE) compared to the conventional plant functional type (PFT)‐fixed approach. This improvement subsequently enhances both prior and posterior CO 2 simulations constrained by GOSAT or OCO‐2 vertically averaged CO 2 (XCO 2 ) retrievals, as validated against independent Observation Package (ObsPack) surface flask and aircraft measurements, as well as Total Carbon Column Observing Network (TCCON) retrievals. Our results highlight that accurate photosynthesis parameterization is fundamental to advancing top‐down estimates of the terrestrial carbon sink. Plain Language Summary The terrestrial biosphere's ability to absorb CO 2 is a critical yet highly uncertain component of the global carbon budget. Atmospheric inversions infer surface carbon fluxes by assimilating atmospheric CO 2 observations and rely critically on prior net ecosystem exchange (NEE) estimates from terrestrial biosphere models (TBMs). Their reliability is therefore intrinsically tied to how well TBMs represent key physiological processes. One such process is photosynthesis, which is controlled by the maximum photosynthetic capacity at the optimum temperature 25°C ( V cmax25 ). Conventional TBM parameterizations typically assign fixed V cmax25 values to vegetation types, overlooking substantial spatial and seasonal variability in vegetation productivity. Here we integrate a dynamic V cmax25 data set derived from satellite solar‐induced fluorescence and leaf chlorophyll content into prior NEE simulations of a TBM within an atmospheric inversion framework. Incorporating this observationally informed V cmax25 parameterization yields more realistic spatial patterns of prior NEE and subsequently improves the accuracy of the posterior estimates after assimilating satellite vertically averaged CO 2 retrievals. The enhanced model performance is demonstrated by both prior and posterior simulations of atmospheric CO 2 concentrations against independent observations. Our findings underscore that integrating remote‐sensing constraints on photosynthetic capacity parameterization is essential to reducing uncertainties in top‐down estimates of the terrestrial carbon sink. Key Points Integrating a satellite‐derived leaf photosynthetic capacity data set yields more realistic bottom‐up net ecosystem exchange (NEE) estimates Atmospheric inversions driven by the improved prior NEE achieve enhanced performance when evaluated against independent CO 2 observations