Wang, H. et al. Towards a universal model for carbon dioxide uptake by plants. Nat. Plants 3, 734–741 (2017).
Chen, C. et al. CO2 fertilization of terrestrial photosynthesis inferred from site to global scales. Proc. Natl Acad. Sci. USA 119, e2115627119 (2022).
Ruehr, S. et al. Evidence and attribution of the enhanced land carbon sink. Nat. Rev. Earth Environ. 4, 518–534 (2023).
Myneni, R. B. et al. Global products of vegetation leaf area and fraction absorbed PAR from one year of MODIS data. Remote Sens. Environ. 83, 214–231 (2002).
Chen, C. et al. China and India lead in greening of the world through land-use management. Nat. Sustain. 2, 122–129 (2019).
Piao, S. et al. Characteristics, drivers and feedbacks of global greening. Nat. Rev. Earth Environ. 1, 14–27 (2020).
Pu, J. et al. Sensor-independent LAI/FPAR CDR: reconstructing a global sensor-independent climate data record of MODIS and VIIRS LAI/FPAR from 2000 to 2022. Earth Syst. Sci. Data 16, 15–34 (2024).
Keenan, T. F. et al. Recent pause in the growth rate of atmospheric CO2 due to enhanced terrestrial carbon uptake. Nat Communications 7, 13428 (2016).
Keenan, T. F. & Riley, W. J. Greening of the land surface in the world’s cold regions consistent with recent warming. Nat. Clim. Change 8, 825–828 (2018).
Pugh, T. A. et al. Important role of forest disturbances in the global biomass turnover and carbon sinks. Nat. Geosci. 12, 730–735 (2019).
Haverd, V. et al. Higher than expected CO2 fertilization inferred from leaf to global observations. Glob. Chang. Biol. 26, 2390–2402 (2020).
Walker, A. P. et al. Integrating the evidence for a terrestrial carbon sink caused by increasing atmospheric CO2. N. Phytol. 5, 2413–2445 (2020).
Keenan, T. F. et al. A constraint on historic growth in global photosynthesis due to rising CO2. Nat. Clim. Chang. 13, 1376–1381 (2023).
Winkler, A. J. et al. Carbon system state determines warming potential of emissions. PLoS ONE 19, e0306128 (2024).
Zhu, Z. et al. Greening of the Earth and its drivers. Nat. Clim. Change 6, 791–795 (2016).
Wang, S. et al. Drylands contribute disproportionately to observed global productivity Increases. Science Bulletin 68, 224–232 (2023).
Poulter, B. et al. Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle. Nature 509, 600 (2014).
Xu, L. et al. Widespread decline in greenness of Amazonian vegetation due to the 2010 drought. Geophys. Res. Lett. 38, L07402 (2011).
Doughty, C. E. et al. Drought impact on forest carbon dynamics and fluxes in Amazonia. Nature 519, 78–82 (2015).
Yang, J. et al. Amazon drought and forest response: largely reduced forest photosynthesis but slightly increased canopy greenness during the extreme drought of 2015/2016. Glob. Change Biol. 24, 1919–1934 (2018).
Thornley, J. H. Instantaneous canopy photosynthesis: analytical expressions for sun and shade leaves based on exponential light decay down the canopy and an acclimated non-rectangular hyperbola for leaf photosynthesis. Ann. Bot. 89, 451–458 (2002).
Brodersen, C. R. & Vogelmann, T. C. Do changes in light direction affect absorption profiles in leaves? Funct. Plant Biol. 37, 403–412 (2010).
Li, X. & Xiao, J. Mapping photosynthesis solely from solar-induced chlorophyll fluorescence: A global, fine-resolution dataset of gross primary production derived from OCO−2. Remote Sens 11, 2563 (2019).
Stich, S. et al. Trends and Drivers of Terrestrial Sources and Sinks of Carbon Dioxide: An Overview of the TRENDY Project. Glob. Biogeochem. Cycles 38, e2024GB008102 (2024).
Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).
Chen, Y. et al. The direct and indirect effects of the environmental factors on global terrestrial gross primary productivity over the past four decades. Environ. Res. Lett. 19, 014052 (2023).
Jung, M. et al. The FLUXCOM ensemble of global land-atmosphere energy fluxes. Sci Data 6, 74 (2019).
Nelson, J. A. et al. X-BASE: the first terrestrial carbon and water flux products from an extended data-driven scaling framework, FLUXCOM-X. Biogeosciences 21, 5079–5115 (2024).
Cai, W. & Prentice, I. C. Recent trends in gross primary production and their drivers: analysis and modelling at flux-site and global scales. Environ. Res. Lett. 15, 124050 (2020).
O’Sullivan, M. et al. Climate-driven variability and trends in plant productivity over recent decades based on three global products. Glob. Biogeochem. Cyc. 34, e2020GB006613 (2020).
Myneni, R. B. et al. Increased plant growth in the northern high latitudes from 1981 to 1991. Nature 386, 698–702 (1997).
Zhou, L. et al. Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999. J. Geophys. Res. Atmos. 106, 20069–20083 (2001).
Park, T. et al. Changes in growing season duration and productivity of northern vegetation inferred from long-term remote sensing data. Environ. Res. Lett. 11, 084001 (2016).
Winkler, A. J. et al. Slowdown of the greening trend in natural vegetation with further rise in atmospheric CO2. Biogeosciences 18, 4985–5010 (2021).
Farquhar, G. D., von Caemmerer, S. & Berry, J. A. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149, 78–90 (1980).
Gutschick, V. P. Photosynthesis model for C3 leaves incorporating CO2 transport, propagation of radiation, and biochemistry. 1. Kinetics and their parameterization. Photosynthetica 18, 549–568 (1984).
Collatz, G. J. et al. Physiological and environmental-regulation of stomatal conductance, photosynthesis and transpiration – a model that includes a laminar boundary-layer. Agric. For. Meteorol. 54, 107–136 (1991).
Stocker, B. D. et al. P-model v1.0: an optimality-based light use efficiency model for simulating ecosystem gross primary production. Geosci. Model Dev. 13, 1545–1581 (2020).
Liang, S. et al. The global land surface satellite (GLASS) product suite. Bull. Am. Meteorol. Soc. 102, E323–E337 (2021).
Yan, K. et al. HiQ LAI: A high-quality reprocessed MODIS leaf area index dataset with better spatiotemporal consistency from 2000 to 2002. Earth Syst. Sci. Data 16, 1601–1622 (2024).
Cao, S. et al. Spatiotemporally consistent global dataset of the GIMMS leaf area index (GIMMS LAI4g) from 1982 to 2020. Earth Syst. Sci. Data 15, 4877–4899 (2023).
Ma, H. & Liang, S. Development of the GLASS 250-m leaf area index product (version 6) from MODIS data using the bidirectional LSTM deep learning model. Remote Sens. Environ. 273, 112985 (2022).
Wei, F. et al. Divergent trends of ecosystem-scale photosynthetic efficiency between arid and humid lands across the globe. Glob. Ecol. Biogeogr. 31, 1824–1837 (2022).
Dardel, C. et al. Re-greening Sahel: 30 years of remote sensing data and field observations (Mali, Niger). Remote Sens. Environ. 140, 350–364 (2014).
Brandt, M. et al. Ground- and satellite-based evidence of the biophysical mechanisms behind the greening Sahel. Glob. Change Biol. 21, 1610–1620 (2015).
Yuan, W. et al. Increased atmospheric vapor pressure deficit reduces global vegetation growth. Sci. Adv. 5, eaax1396 (2019).
Fatichi, S. et al. Partitioning direct and indirect effects reveals the response of water-limited ecosystems to elevated CO2. Proc. Natl Acad. Sci. 113, 12757–12762 (2016).
Knyazikhin, Y. et al. Synergistic algorithm for estimating vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from MODIS and MISR data. J. Geophys. Res. 103, 32257–32276 (1998).
Norby, R. J. et al. Forest response to elevated CO2 is conserved across a broad range of productivity. Proc. Natl Acad. Sci. USA 102, 18052–18056 (2005).
Kaptué, A. T., Prihodko, L. & Hanan, N. P. On regreening and degradation in Sahelian watersheds. Proc. Natl Acad. Sci. USA 112, 12133–12138 (2015).
Min, J. et al. Understanding spatial patterns in the drivers of greenness trends in the Sahel-Sudano-Guinean region. Big Earth Data 7, 298–317 (2023).
Zhang, Y. et al. Multiple afforestation programs accelerate the greenness in the ‘Three North’ region of China from 1982 to 2013. Ecol. Indic. 61, 404–412 (2016).
Lu, F. et al. Effects of national ecological restoration projects on carbon sequestration in China from 2001 to 2010. Proc. Natl Acad. Sci. USA 115, 4039–4044 (2018).
Ray, D. K. & Foley, J. A. Increasing global crop harvest frequency: recent trends and future directions. Environ. Res. Lett. 8, 044041 (2013).
Mueller, N. D. et al. Closing yield gaps through nutrient and water management. Nature 490, 254–257 (2012).
Running, S. W. & Zhao, M. Daily GPP and Annual NPP (MOD17A2/A3) Products NASA Earth Observing System MODIS Land Algorithm—User’s Guide V3. 28 (MODIS Land Team, 2015).
Yuan, W. et al. Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes. Agric. For. Meteorol. 143, 189–207 (2007).
Prentice, I. C. et al. Balancing the costs of carbon gain and water transport: testing a new theoretical framework for plant functional ecology. Ecol. Lett. 17, 82–91 (2014).
Korson, L., Drost-hansen, W. & Millero, F. J. Viscosity of water various temperatures. J. Phys. Chem. 73, 34–39 (1969).
Bao, S. et al. Environment-sensitivity functions for gross primary productivity in light use efficiency models. Agric. For. Meteorol. 312, 108708 (2022).
Kalliokoski, T. et al. Decomposing sources of uncertainty in climate change projections of boreal forest primary production. Agric. For. Meteorol. 262, 192–205 (2018).
Sulla-Menashe, D. & Friedl, M. A. User Guide to Collection 6 MODIS Land Cover (MCD12Q1 and MCD12C1) Product (USGS, 2018).
Pastorello, G. et al. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Sci. Data 7, 225 (2020).
Zheng, Y. et al. Improved estimate of global gross primary production for reproducing its long-term variation, 1982–2017. Earth Syst. Sci. Data 12, 2725–2746 (2020).
Wang, L. et al. Evaluation of the latest MODIS GPP products across multiple biomes using global eddy covariance flux data. Remote Sens 9, 418 (2017).
Zhu, W., Zhao, C. & Xie, Z. An end-to-end satellite-based GPP estimation model devoid of meteorological and land cover data. Agric. For. Meteorol. 331, 109337 (2023).
Bai, Y. et al. Different satellite products revealing variable trends in global gross primary production. J. Geophys. Res. 128, e2022JG006918 (2023).
Baret, F. et al. Evaluation of the representativeness of networks of sites for the global validation and intercomparison of land biophysical products: proposition of the CEOS-BELMANIP. IEEE Trans. Geosci. Remote Sens 44, 1794–1803 (2006).
Gier, B. K. et al. Representation of the terrestrial carbon cycle in CMIP6. Biogeosciences 21, 5321–5360 (2024).
Yan, K. et al. Evaluation of MODIS LAI/FPAR Product Collection 6. Part 1: Consistency and Improvements. Remote Sens 8, 350 (2016).
Yan, K. et al. Evaluation of MODIS LAI/FPAR Product Collection 6. Part 2: Validation and Intercomparison. Remote Sens 8, 460 (2016).
Jin, H. et al. Intercomparison and validation of MODIS and GLASS leaf area index (LAI) products over mountain areas: A case study in southwestern China. Int. J. Appl. Earth Obs. Geoinf. 55, 52–67 (2017).
Zhang, X. et al. An insight into the internal consistency of MODIS global leaf area index products. IEEE Trans. Geosci. 62, 1–16 (2024).
Arneth, A. et al. Terrestrial biogeochemical feedbacks in the climate system. Nat. Geosci. 3, 525–532 (2010).
Heinze, C. et al. ESD reviews: climate feedbacks in the Earth system and prospects for their evaluation. Earth Syst. Dyn. 10, 379–452 (2019).