Comparative assessment of leaf photosynthetic capacity datasets for estimating terrestrial gross primary productivity.

The maximum Rubisco carboxylation rate normalized to 25 °C (V) is a key parameter in terrestrial biosphere models for simulating carbon cycling. Recently, global distributions of V have been derived through various methods and different data, including field measurements, ecological optimality theory (EOT), leaf chlorophyll content (LCC), and solar-induced chlorophyll fluorescence (SIF). However, direct validation poses challenges due to high uncertainty arising from limited ground-based observations. This study conducted an indirect evaluation of four V datasets by assessing the accuracy of gross primary productivity (GPP) simulated using the Biosphere-atmosphere Exchange Process Simulator (BEPS) at both site and global scales. Results indicate that, compared to utilizing V fixed by plant functional types (PFT) derived from field measurements, incorporating V derived from SIF and LCC (SIF + LCC), or solely LCC, into BEPS significantly reduces simulated errors in the annual total GPP, with a 23.2 %-25.1 % decrease in the average absolute bias across 196 FLUXNET2015 sites. Daily GPP for evergreen needleleaf forests, deciduous broadleaf forests, shrublands, grasslands, and croplands shows a 7.8 %-27.6 % decrease in absolute bias, primarily attributed to reduced simulation errors during off-peak seasons of vegetation growth. Conversely, the annual total GPP error simulated using EOT-derived V increases slightly (2.2 %) compared to that simulated using PFT-fixed V. This is primarily due to a significant overestimation in evergreen broadleaf forests and underestimation in croplands, despite slight increased accuracy for other PFTs. The global annual GPP simulated using V with seasonal variations (i.e., LCC V and SIF + LCC V) yields a 4.3 %-7.3 % decrease compared to that simulated using PFT-fixed V. Compared to FLUXCOM and GOSIF GPP products, the GPP simulated based on SIF + LCC V and LCC V demonstrates better consistency (R = 0.91-0.93, RMSE = 314.2-376.6 g C m yr). This study underscores the importance of accurately characterizing the spatiotemporal variations in V for the accurate simulation of global vegetation productivity.