The maximum rate of Rubisco carboxylation (V_cmax) determines leaf photosynthetic capacity and is a key parameter for estimating the terrestrial carbon cycle, but its spatial information is lacking, hindering global ecological research. Here, we convert leaf chlorophyll content (LCC) retrieved from satellite data to V_cmax, based on plants' optimal distribution of nitrogen between light harvesting and carboxylation pathways. We also derive V_cmax from satellite (GOME-2) observations of sun-induced chlorophyll fluorescence (SIF) as a proxy of leaf photosynthesis using a data assimilation technique. These two independent global V_cmax products agree well ($r^{\mathrm{2}}=\mathrm{0.79},\mathrm{RMSE}=\mathrm{15.46}\mathrm{\mu }$ mol m⁻² s⁻¹, P<0.001) and compare well with 3672 ground-based measurements ($r^{\mathrm{2}}=\mathrm{0.69},\mathrm{RMSE}=\mathrm{13.8}\mathrm{\mu }$ mol m⁻² s⁻¹ and P<0.001 for SIF; $r^{\mathrm{2}}=\mathrm{0.55},\mathrm{RMSE}=\mathrm{18.28}\mathrm{\mu }$ mol m⁻² s⁻¹ and P<0.001 for LCC). The LCC-derived V_cmax product is also used to constrain the retrieval of V_cmax from TROPical Ozone Mission (TROPOMI) SIF data to produce an optimized V_cmax product using both SIF and LCC information. The global distributions of these products are compatible with V_cmax computed from an ecological optimality theory using meteorological variables, but importantly reveal additional information on the influence of land cover, irrigation, soil pH, and leaf nitrogen on leaf photosynthetic capacity. These satellite-based approaches and spatial V_cmax products are primed to play a major role in global ecosystem research. The three remote sensing V_cmax products based on SIF, LCC, and SIF+LCC are available at https://doi.org/10.5281/zenodo.6466968 (Chen et al., 2022), and the code for implementing the ecological optimality theory is available at https://github.com/SmithEcophysLab/optimal_vcmax_R and https://doi.org/10.5281/zenodo.5899564 (last access: 31 August 2022) (Smith et al., 2022).

Globally shaded leaves contribute to more than a half of the total increase in gross primary production (GPP; 7.6 Pg C) for 1982–2016. During 1982–2016, the fraction of shaded GPP increases by 1.1% (p < 0.01) in tropical forests and decreases by 1.4% (p < 0.01) and 1.8% (p < 0.01) in evergreen needleleaf and deciduous needleleaf boreal forests, respectively, suggesting an ecological niche of certain canopy structure for ecosystems to achieve maximum GPP. Unlike transpiration from sunlit leaves that has a turning point in the trend in 2003, global transpiration from shaded leaves steadily increased at the rate of 34 km³/year (p < 0.0001) during 1982–2016. Our study therefore suggests that shaded leaves have an increasing role in buffering the adverse impact of climate change and extremes. Further studies are still needed to reduce the uncertainties in reported trends arisen from climate forcing data, leaf area index, and land cover and land change products.

Diagnostic terrestrial biosphere models (TBMs) forced by remote sensing observations have been a principal tool for providing benchmarks on global gross primary productivity (GPP) and evapotranspiration (ET). However, these models often estimate GPP and ET at coarse daily or monthly steps, hindering analysis of ecosystem dynamics at the diurnal (hourly) scales, and prescribe some essential parameters (i.e., the Ball–Berry slope (m) and the maximum carboxylation rate at 25 °C ($V_{\mathrm{cmax}}^{\mathrm{25}}$ )) as constant, inducing uncertainties in the estimates of GPP and ET. In this study, we present hourly estimations of global GPP and ET datasets at a 0.25° resolution from 2001 to 2020 simulated with a widely used diagnostic TBM – the Biosphere–atmosphere Exchange Process Simulator (BEPS). We employed eddy covariance observations and machine learning approaches to derive and upscale the seasonally varied m and $V_{\mathrm{cmax}}^{\mathrm{25}}$ for carbon and water fluxes. The estimated hourly GPP and ET are validated against flux observations, remote sensing, and machine learning-based estimates across multiple spatial and temporal scales. The correlation coefficients (R²) and slopes between hourly tower-measured and modeled fluxes are R²=0.83, regression slope =0.92 for GPP, and R²=0.72, regression slope =1.04 for ET. At the global scale, we estimated a global mean GPP of 137.78±3.22 Pg C yr⁻¹ (mean ± 1 SD) with a positive trend of 0.53 Pg C yr⁻² (p<0.001), and an ET of $\mathrm{89.03}\pm \mathrm{0.82}\times {\mathrm{10}}^{\mathrm{3}}$ km³ yr⁻¹ with a slight positive trend of 0.10×10³ km³ yr⁻² (p<0.001) from 2001 to 2020. The spatial pattern of our estimates agrees well with other products, with R²=0.77–0.85 and R²=0.74–0.90 for GPP and ET, respectively. Overall, this new global hourly dataset serves as a “handshake” among process-based models, remote sensing, and the eddy covariance flux network, providing a reliable long-term estimate of global GPP and ET with diurnal patterns and facilitating studies related to ecosystem functional properties, global carbon, and water cycles. The hourly GPP and ET estimates are available at https://doi.org/10.57760/sciencedb.ecodb.00163 (Leng et al., 2023a) and the accumulated daily datasets are available at https://doi.org/10.57760/sciencedb.ecodb.00165 (Leng et al., 2023b).