Wu, J. et al. Leaf development and demography explain photosynthetic seasonality in Amazon evergreen forests. Science 351, 972–976 (2016).

Article 
PubMed 
CAS 

Google Scholar
 

Chavana-Bryant, C. et al. Leaf age effects on the spectral predictability of leaf traits in Amazonian canopy trees. Sci. Total Environ. 666, 1301–1315 (2019).

Article 
PubMed 
CAS 

Google Scholar
 

Niinemets, Ü, Cescatti, A., Rodeghiero, M. & Tosens, T. Leaf internal diffusion conductance limits photosynthesis more strongly in older leaves of Mediterranean evergreen broad-leaved species. Plant Cell Environ. 28, 1552–1566 (2005).

Article 

Google Scholar
 

Sobrado, M. A. Leaf age effects on photosynthetic rate, transpiration rate and nitrogen content in a tropical dry forest. Physiol. Plant. 90, 210–215 (1994).

Article 

Google Scholar
 

De Moura, Y. M. et al. Spectral analysis of amazon canopy phenology during the dry season using a tower hyperspectral camera and modis observations. ISPRS J. Photogramm. Remote Sens. 131, 52–64 (2017).

Article 

Google Scholar
 

Meir, P. et al. Acclimation of photosynthetic capacity to irradiance in tree canopies in relation to leaf nitrogen concentration and leaf mass per unit area. Plant Cell Environ. 25, 343–357 (2002).

Article 

Google Scholar
 

Aguirre-Gutiérrez, J. et al. Canopy functional trait variation across Earth’s tropical forests. Nature 641, 129–136 (2025).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Niinemets, Ü Leaf age dependent changes in within-canopy variation in leaf functional traits: a meta-analysis. J. Plant Res. 129, 313–338 (2016).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Reich, P. B. et al. Controls on declining carbon balance with leaf age among 10 woody species in Australian woodland: do leaves have zero daily net carbon balances when they die?. New Phytol. 183, 153–166 (2009).

Article 
PubMed 
CAS 

Google Scholar
 

Field, C. & Mooney, H. A. Leaf age and seasonal effects on light, water, and nitrogen use efficiency in a California shrub. Oecologia 56, 348–355 (1983).

Article 
PubMed 
CAS 

Google Scholar
 

Niinemets, Ü Photosynthesis and resource distribution through plant canopies. Plant Cell Environ. 30, 1052–1071 (2007).

Article 
PubMed 
CAS 

Google Scholar
 

Wang, H. et al. Leaf economics fundamentals explained by optimality principles. Sci. Adv. 9, eadd5667 (2023).

Article 
PubMed 
PubMed Central 
CAS 

Google Scholar
 

Beer, C. et al. Terrestrial gross carbon dioxide uptake: global distribution and covariation with climate. Science 329, 834–838 (2010).

Article 
PubMed 
CAS 

Google Scholar
 

Menezes, J. et al. Changes in leaf functional traits with leaf age: when do leaves decrease their photosynthetic capacity in Amazonian trees?. Tree Physiol. 42, 922–938 (2022).

Article 
PubMed 
CAS 

Google Scholar
 

Botía, S. et al. The CO2 record at the Amazon Tall Tower Observatory: a new opportunity to study processes on seasonal and inter-annual scales. Glob. Change Biol. 28, 588–611 (2022).

Article 

Google Scholar
 

Song, G. et al. Monitoring leaf phenology in moist tropical forests by applying a superpixel-based deep learning method to time-series images of tree canopies. ISPRS J. Photogramm. Remote Sens. 183, 19–33 (2022).

Article 

Google Scholar
 

Lopes, A. P. et al. Leaf flush drives dry season green-up of the Central Amazon. Remote Sens. Environ. 182, 90–98 (2016).

Article 

Google Scholar
 

Saleska, S. R. et al. Dry-season greening of Amazon forests. Nature 531, E4–E5 (2016).

Article 
PubMed 
CAS 

Google Scholar
 

Huete, A. R. et al. Amazon rainforests green-up with sunlight in dry season. Geophys. Res. Lett. 33, L06405 (2006).

Article 

Google Scholar
 

Doughty, C. E. & Goulden, M. L. Seasonal patterns of tropical forest leaf area index and CO2 exchange. J. Geophys. Res. Biogeosci. 113, G00B06 (2008).

Article 

Google Scholar
 

Brando, P. M. et al. Seasonal and interannual variability of climate and vegetation indices across the Amazon. Proc. Natl Acad. Sci. USA 107, 14685–14690 (2010).

Article 
PubMed 
PubMed Central 
CAS 

Google Scholar
 

Detto, M. et al. Resource acquisition and reproductive strategies of tropical forest in response to the El Niño–Southern Oscillation. Nat. Commun. 9, 913 (2018).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Wang, S. et al. Estimation of leaf photosynthetic capacity from leaf chlorophyll content and leaf age in a subtropical evergreen coniferous plantation. J. Geophys. Res. Biogeosci. 125, e2019JG005020 (2020).

Article 

Google Scholar
 

Xue, M. et al. Pantropical moist forests are converging towards a middle leaf longevity. Nat Commun. https://doi.org/10.1038/s41467-026-68989-x (2026)

Myneni, R. B. et al. Large seasonal swings in leaf area of Amazon rainforests. Proc. Natl Acad. Sci. USA 104, 4820–4823 (2007).

Article 
PubMed 
PubMed Central 
CAS 

Google Scholar
 

De Weirdt, M. et al. Seasonal leaf dynamics for tropical evergreen forests in a process-based global ecosystem model. Geosci. Model Dev. 5, 1091–1108 (2012).

Article 

Google Scholar
 

Albert, L. P. et al. Age-dependent leaf physiology and consequences for crown-scale carbon uptake during the dry season in an Amazon evergreen forest. New Phytol. 219, 870–884 (2018).

Article 
PubMed 
CAS 

Google Scholar
 

Reich, P. B. et al. Leaf demography and phenology in Amazonian rain forest: a census of 40,000 leaves of 23 tree species. Ecol. Monogr. 74, 3–23 (2004).

Article 

Google Scholar
 

Albert, L. P. et al. Cryptic phenology in plants: case studies, implications, and recommendations. Glob. Change Biol. 25, 3591–3608 (2019).

Article 

Google Scholar
 

Yang, X. et al. A comprehensive framework for seasonal controls of leaf abscission and productivity in evergreen broadleaved tropical and subtropical forests. Innovation 2, 100154 (2021).

PubMed 
PubMed Central 

Google Scholar
 

Chen, X. et al. Novel representation of leaf phenology improves simulation of Amazonian evergreen forest photosynthesis in a land surface model. J. Adv. Model. Earth Syst. 12, e2018MS001565 (2020).

Article 

Google Scholar
 

Zhang, Y. et al. Coupled estimation of 500 m and 8-day resolution global evapotranspiration and gross primary production in 2002–2017. Remote Sens. Environ. 222, 165–182 (2019).

Article 

Google Scholar
 

Yang, H. et al. Climatic and biotic factors influencing regional declines and recovery of tropical forest biomass from the 2015/16 El Niño. Proc. Natl Acad. Sci. USA 119, e2101388119 (2022).

Article 
PubMed 
PubMed Central 
CAS 

Google Scholar
 

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).

Article 

Google Scholar
 

Kapos, V. et al. in Forests in Sustainable Mountain Development: A State of Knowledge Report for 2000. Task Force on Forests in Sustainable Mountain Development https://doi.org/10.1079/9780851994468.0004 (CABI, 2000).

Liu, L. et al. Tropical tall forests are more sensitive and vulnerable to drought than short forests. Glob. Change Biol. 28, 1583–1595 (2022).

Article 
CAS 

Google Scholar
 

Han, Q. et al. Leaf-age effects on seasonal variability in photosynthetic parameters and its relationships with leaf mass per area and leaf nitrogen concentration within a Pinus densiflora crown. Tree Physiol. 28, 551–558 (2008).

Article 
PubMed 
CAS 

Google Scholar
 

Xu, X. et al. Variations of leaf longevity in tropical moist forests predicted by a trait-driven carbon optimality model. Ecol. Lett. 20, 1097–1106 (2017).

Article 
PubMed 

Google Scholar
 

Chen, J. M. et al. Global datasets of leaf photosynthetic capacity for ecological and earth system research. Earth Syst. Sci. Data 14, 4077–4093 (2022).

Article 

Google Scholar
 

Papastefanou, P. et al. Recent extreme drought events in the Amazon rainforest: assessment of different precipitation and evapotranspiration datasets and drought indicators. Biogeosciences 19, 3843–3861 (2022).

Article 

Google Scholar
 

O’Neill, B. C. et al. The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geosci. Model Dev. 9, 3461–3482 (2016).

Article 

Google Scholar
 

Warszawski, L. et al. The Inter-Sectoral Impact Model Intercomparison Project (ISI–MIP): project framework. Proc. Natl Acad. Sci. USA 111, 3228–3232 (2014).

Article 
PubMed 
CAS 

Google Scholar
 

Restrepo-Coupe, N. et al. Do dynamic global vegetation models capture the seasonality of carbon fluxes in the Amazon basin? A data-model intercomparison. Glob. Change Biol. 23, 191–208 (2017).

Article 

Google Scholar
 

Xu, L. et al. Satellite observation of tropical forest seasonality: spatial patterns of carbon exchange in Amazonia. Environ. Res. Lett. 10, 084005 (2015).

Article 

Google Scholar
 

Wu, J. et al. Biological processes dominate seasonality of remotely sensed canopy greenness in an Amazon evergreen forest. New Phytol. 217, 1507–1520 (2018).

Article 
PubMed 

Google Scholar
 

Piao, S. et al. Characteristics, drivers and feedbacks of global greening. Nat. Rev. Earth Environ. 1, 14–27 (2019).

Article 

Google Scholar
 

Chen, X. et al. Vapor pressure deficit and sunlight explain seasonality of leaf phenology and photosynthesis across Amazonian evergreen broadleaved forest. Glob. Biogeochem. Cycles 35, e2020GB006893 (2021).

Article 
CAS 

Google Scholar
 

Santos De Lima, L. et al. Severe droughts reduce river navigability and isolate communities in the Brazilian Amazon. Commun. Earth Environ. 5, 370 (2024).

Article 

Google Scholar
 

Santos, V. A. H. F. dos et al. Causes of reduced leaf-level photosynthesis during strong El Niño drought in a Central Amazon forest. Glob. Change Biol. 24, 4266–4279 (2018).

Article 

Google Scholar
 

Green, J. K. et al. Amazon rainforest photosynthesis increases in response to atmospheric dryness. Sci. Adv. 6, eabb7232 (2020).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Wright, I. J. et al. The worldwide leaf economics spectrum. Nature 428, 821–827 (2004).

Article 
PubMed 
CAS 

Google Scholar
 

Kono, Y. et al. Initial hydraulic failure followed by late-stage carbon starvation leads to drought-induced death in the tree Trema orientalis. Commun. Biol. 2, 8 (2019).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Fan, Y. et al. Hydrologic regulation of plant rooting depth. Proc. Natl Acad. Sci. USA 114, 10572–10577 (2017).

Article 
PubMed 
PubMed Central 
CAS 

Google Scholar
 

Tao, S. et al. Increasing and widespread vulnerability of intact tropical rainforests to repeated droughts. Proc. Natl Acad. Sci. USA 119, e2116626119 (2022).

Article 
PubMed 
PubMed Central 
CAS 

Google Scholar
 

Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).

Article 
PubMed 
CAS 

Google Scholar
 

Su, Y. et al. Asymmetric influence of forest cover gain and loss on land surface temperature. Nat. Clim. Change 13, 823–831 (2023).

Article 

Google Scholar
 

Chuvieco, E. et al. ESA Fire Climate Change Initiative (Fire_cci): MODIS Fire_cci Burned Area Pixel product, version 5.1. Centre for Environmental Data Analysis https://doi.org/10.5285/58f00d8814064b79a0c49662ad3af537 (2018).

Vancutsem, C. et al. Long-term (1990–2019) monitoring of forest cover changes in the humid tropics. Sci. Adv. 7, eabe1603 (2021).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Gyimah, R. & Nakao, T. Early growth and photosynthetic responses to light in seedlings of three tropical species differing in successional strategies. New For. 33, 217–236 (2007).

Article 

Google Scholar
 

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).

Article 
PubMed 
CAS 

Google Scholar
 

Chen, J. M. et al. Daily canopy photosynthesis model through temporal and spatial scaling for remote sensing applications. Ecol. Model. 124, 99–119 (1999).

Article 
CAS 

Google Scholar
 

Chen, J. M. et al. Vegetation structural change since 1981 significantly enhanced the terrestrial carbon sink. Nat. Commun. 10, 4259 (2019).

Article 
PubMed 
PubMed Central 

Google Scholar
 

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).

Article 

Google Scholar
 

Fisher, R. A. et al. Taking off the training wheels: the properties of a dynamic vegetation model without climate envelopes, CLM4.5(ED). Geosci. Model Dev. 8, 3593–3619 (2015).

Article 

Google Scholar
 

Chen, J. M. et al. Leaf area index measurements at Fluxnet–Canada forest sites. Agric. For. Meteorol. 140, 257–268 (2006).

Article 

Google Scholar
 

Wang, R. et al. Seasonality of leaf area index and photosynthetic capacity for better estimation of carbon and water fluxes in evergreen conifer forests. Agric. For. Meteorol. 279, 107708 (2019).

Article 

Google Scholar
 

Yang, X. et al. A gridded dataset of a leaf-age-dependent leaf area index seasonality product over tropical and subtropical evergreen broadleaved forests. Earth Syst. Sci. Data 15, 2601–2622 (2023).

Article 

Google Scholar
 

Medlyn, B. E. et al. Reconciling the optimal and empirical approaches to modelling stomatal conductance: reconciling optimal and empirical stomatal models. Glob. Change Biol. 17, 2134–2144 (2011).

Article 

Google Scholar
 

Bernacchi, C. J. et al. Modelling C3 photosynthesis from the chloroplast to the ecosystem. Plant Cell Environ. 36, 1641–1657 (2013).

Article 
PubMed 
CAS 

Google Scholar
 

Zhao, K., Popescu, S. & Nelson, R. Lidar remote sensing of forest biomass: a scale-invariant estimation approach using airborne lasers. Remote Sens. Environ. 113, 182–196 (2009).

Article 

Google Scholar
 

Chavana-Bryant, C. et al. Leaf aging of Amazonian canopy trees as revealed by spectral and physiochemical measurements. New Phytol. 214, 1049–1063 (2017).

Article 
PubMed 
CAS 

Google Scholar
 

Seyednasrollah, B. et al. Publisher correction: tracking vegetation phenology across diverse biomes using version 2.0 of the PhenoCam dataset. Sci. Data 6, 261 (2019).

Article 
PubMed 
PubMed Central 

Google Scholar
 

Derived monthly leaf demography for the 134 vine-free upper canopy tree crowns that exhibited at least two leaf flush events during Jul2013–Nov2018. ATTO https://doi.org/10.17871/ATTO.230.4.842 (2025).

Coley, P. D. Herbivory and defensive characteristics of tree species in a lowland tropical forest. Ecol. Monogr. 53, 209–229 (1983).

Article 

Google Scholar
 

Reich, P. B. et al. Leaf lifespan as a determinant of leaf structure and function among 23 amazonian tree species. Oecologia 86, 16–24 (1991).

Article 
PubMed 
CAS 

Google Scholar
 

Reich, P. B. et al. Generality of leaf trait relationships: a test across six biomes. Ecology 80, 1955–1969 (1999).

Article 

Google Scholar
 

Lugo, A. E. Comparison of tropical tree plantations with secondary forests of similar age. Ecol. Monogr. 62, 2–41 (1992).

Article 

Google Scholar
 

Santiago, L. S. & Wright, S. J. Leaf functional traits of tropical forest plants in telation to growth form. Funct. Ecol. 21, 19–27 (2007).

Article 

Google Scholar
 

Chave, J. et al. Above-ground biomass and productivity in a rain forest of eastern South America. J. Trop. Ecol. 24, 355–366 (2008).

Article 

Google Scholar
 

Kattge, J. et al. TRY plant trait database—enhanced coverage and open access. Glob. Change Biol. 26, 119–188 (2020).

Article 

Google Scholar
 

Wang, F. et al. Contrasting age-dependent leaf acclimation strategies drive vegetation greening across deciduous broadleaf forests in mid- to high latitudes. Nat. Plants 11, 1748–1758 (2025).

Article 
PubMed 

Google Scholar
 

Janssen, T. et al. Drought effects on leaf fall, leaf flushing and stem growth in the Amazon forest: reconciling remote sensing data and field observations. Biogeosciences 18, 4445–4472 (2021).

Article 
CAS 

Google Scholar
 

Zhang, H. et al. Accurate representation of leaf longevity is important for simulating ecosystem carbon cycle. Basic Appl. Ecol. 17, 396–407 (2016).

Article 
CAS 

Google Scholar
 

Bradley, A. V. et al. Relationships between phenology, radiation and precipitation in the Amazon region. Glob. Change Biol. 17, 2245–2260 (2011).

Article 

Google Scholar
 

Gloor, M. et al. Recent Amazon climate as background for possible ongoing and future changes of Amazon humid forests. Glob. Biogeochem. Cycles 29, 1384–1399 (2015).

Article 
CAS 

Google Scholar
 

Meyer, H. et al. Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation. Environ. Model. Softw. 101, 1–9 (2018).

Article 

Google Scholar
 

Ploton, P. et al. Spatial validation reveals poor predictive performance of large-scale ecological mapping models. Nat. Commun. 11, 4540 (2020).

Article 
PubMed 
PubMed Central 
CAS 

Google Scholar
 

Hollunder, R. K. et al. Vapor pressure deficit drives the mortality of understorey woody plants during drought recovery in the Atlantic forest. J. Veg. Sci. 35, e13222 (2024).

Article 

Google Scholar
 

McDowell, N. et al. Drivers and mechanisms of tree mortality in moist tropical forests. New Phytol. 219, 851–869 (2023).

Article 

Google Scholar
 

Esquivel-Muelbert, A. et al. Tree mode of death and mortality risk factors across Amazon forests. Nat. Commun. 11, 5515 (2020).

Article 
PubMed 
PubMed Central 
CAS 

Google Scholar
 

Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).

Article 

Google Scholar
 

Friedman, J. H. Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001).

Article 

Google Scholar
 

Su, Y. et al. Pervasive but biome-dependent relationship between fragmentation and resilience in forests. Nat. Ecol. Evol. 9, 1670–1684 (2025).

Article 
PubMed 

Google Scholar
 

Ziehn, T. et al. The Australian Earth System Model: ACCESS-ESM1.5. J. South. Hemisph. Earth Syst. Sci. 70, 193–214 (2020).

Article 

Google Scholar
 

Swart, N. C. et al. CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP. Earth System Grid Federation https://doi.org/10.22033/ESGF/CMIP6.1317 (2019).

Lovato, T., Peano, D. & Butenschön, M. CMCC CMCC-ESM2 model output prepared for CMIP6 ScenarioMIP. Earth System Grid Federation https://doi.org/10.22033/ESGF/CMIP6.13168 (2021).

Voldoire, A. CNRM-CERFACS CNRM-CM6-1 model output prepared for CMIP6 ScenarioMIP. Earth System Grid Federation https://doi.org/10.22033/ESGF/CMIP6.1384 (2019).

Voldoire, A. CNRM-CERFACS CNRM-CM6-1-HR model output prepared for CMIP6 ScenarioMIP. Earth System Grid Federation https://doi.org/10.22033/ESGF/CMIP6.1388 (2019).

Seferian, R. CNRM-CERFACS CNRM-ESM2-1 model output prepared for CMIP6 ScenarioMIP. Earth System Grid Federation https://doi.org/10.22033/ESGF/CMIP6.1395 (2019).

Dunne, J. P. et al. The GFDL Earth System Model Version 4.1 (GFDL-ESM 4.1): overall coupled model description and simulation characteristics. J. Adv. Model. Earth Syst. 12, e2019MS002015 (2020).

Article 

Google Scholar
 

NASA Goddard Institute for Space Studies (NASA/GISS). IPCC DDC: NASA-GISS GISS-E2.1G model output prepared for CMIP6 ScenarioMIP. World Data Center for Climate (WDCC) at DKRZ https://doi.org/10.26050/WDCC/AR6.C6SPGIGEG (2023).

Volodin, E. et al. IPCC DDC: INM INM-CM4-8 model output prepared for CMIP6 ScenarioMIP. World Data Center for Climate (WDCC) at DKRZ https://doi.org/10.26050/WDCC/AR6.C6SPINIC8 (2023).

Volodin, E. et al. IPCC DDC: INM INM-CM5-0 model output prepared for CMIP6 ScenarioMIP. World Data Center for Climate (WDCC) at DKRZ https://doi.org/10.26050/WDCC/AR6.C6SPINIC0 (2023).

Boucher, O. et al. IPCC DDC: IPSL IPSL-CM6A-LR model output prepared for CMIP6 ScenarioMIP. World Data Center for Climate (WDCC) at DKRZ https://doi.org/10.26050/WDCC/AR6.C6SPIPICL (2023).

Tachiiri, K. et al. IPCC DDC: MIROC MIROC-ES2L model output prepared for CMIP6 ScenarioMIP. World Data Center for Climate (WDCC) at DKRZ https://doi.org/10.26050/WDCC/AR6.C6SPMIMIL (2023).

Schupfner, M. et al. IPCC DDC: DKRZ MPI-ESM1.2-HR model output prepared for CMIP6 ScenarioMIP. World Data Center for Climate (WDCC) at DKRZ https://doi.org/10.26050/WDCC/AR6.C6SPDKME2 (2023).

Yukimoto, S. et al. IPCC DDC: MRI MRI-ESM2.0 model output prepared for CMIP6 ScenarioMIP. World Data Center for Climate (WDCC) at DKRZ https://doi.org/10.26050/WDCC/AR6.C6SPMRME0 (2023).

Good, P. et al. IPCC DDC: MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP. World Data Center for Climate (WDCC) at DKRZ https://doi.org/10.26050/WDCC/AR6.C6SPMOU0 (2023).

Lange, S. Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1. 0). Geosci. Model Dev. 12, 3055–3070 (2019).

Lange, S. ISIMIP3BASD (version 2.4.1). Zenodo https://doi.org/10.5281/zenodo.3898426 (2021).

Wenger, S. J. & Olden, J. D. Assessing transferability of ecological models: an underappreciated aspect of statistical validation. Methods Ecol. Evol. 3, 260–267 (2012).

Article 

Google Scholar
 

Xueqin, Y. & Xiuzhi, C. High-resolution leaf age-dependent LAI time-series dataset across Amazon rainforests Zenodo https://zenodo.org/records/18271110 (2025).

Yang, X. et al. Code to support ‘Amazon rainforests are rejuvenating their canopies by producing more photosynthetically efficient young leaves under climate change’. Zenodo https://doi.org/10.5281/zenodo.18269018 (2026).

Malhi, Y. et al. Exploring the likelihood and mechanism of a climate-change-induced dieback of the Amazon rainforest. Proc. Natl Acad. Sci. USA 106, 20610–20615 (2009).

Article 
PubMed 
PubMed Central 
CAS 

Google Scholar
 

Xu, X. et al. Climate regime shift and forest loss amplify fire in Amazonian forests. Glob. Change Biol. 26, 5874–5885 (2020).

Article 

Google Scholar
 

Tang, H. & Dubayah, R. Light-driven growth in Amazon evergreen forests explained by seasonal variations of vertical canopy structure. Proc. Natl Acad. Sci. USA 114, 2640–2644 (2017).

Article 
PubMed 
PubMed Central 
CAS 

Google Scholar