CMLR: A Mechanistic Global GPP Dataset Derived from TROPOMIS SIF Observations

Solar - induced chlorophyll fluorescence (SIF) holds great potential for estimating gross primary production (GPP). Nevertheless, currently, there is an absence of open-access global GPP datasets that directly utilize SIF with models clearly expressing the biophysical and biological processes in photosynthesis.

This study presents a new global 0.05° SIF - based GPP dataset named CMLR GPP (canopy - scale Mechanistic Light Reaction model), which is generated using TROPOMI observations. A modified mechanistic light reaction model at the canopy scale was utilized to create this dataset. In the CMLR model, the canopy qL (the opened fraction of photosynthesis II reaction centers) was parameterized by a random forest model.

In the validation dataset, the CMLR GPP estimates exhibited a strong correlation with tower - based GPP (R² = 0.72). Moreover, at the global scale, its performance was comparable to other global datasets such as Boreal Ecosystem Productivity Simulator (BEPS) GPP, FluxSat GPP, and GOSIF (global, OCO - 2 - based SIF product) GPP. Across various normalized difference vegetation index, vapor pressure deficit, and temperature conditions, different plant functional types, and most months of the year, the CMLR GPP maintained high accuracy.

To sum up, CMLR GPP is a novel global GPP dataset established on mechanistic frameworks. Its availability is anticipated to facilitate future research in ecological and geobiological fields.