Cross-ECV consistency at global scale: LAI and FAPAR changes

A framework is proposed for assessing the physical consistency between two terrestrial Essential Climate Variables (ECVs) products retrieved from Earth Observation at global scale. The methodology assessed the level of agreement between the temporal variations of Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR). The simultaneous changes were classified according to their sign, magnitude and level of confidence, whereby the respective products uncertainties were taken into consideration. A set of proposed agreement metrics were used to identify temporal and spatial biases of non-coherency, non-significance, sensitivity and the overall level of agreement of the temporal changes between two ECVs. We applied the methodology using the Joint Research Center (JRC) Two-stream Inversion Package (TIP) products at 1 km, those provided by the Copernicus Global Land Service (CGLS) based on the SPOT/VGT and Proba-V at 1 km, and the MODIS MCD15A3 at 500 m. In addition, the same analysis was applied with aggregated products at a larger scale over Southern Africa. We found that the CGLS LAI and FAPAR products lacked consistency in their spatial and temporal changes and were severely affected by trends. The MCD15A3 products were characterized by the highest number of non-coherent changes between the two ECVs but temporal inconsistencies were mainly located over the eastern hemisphere. The JRC-TIP products were highly consistent. The results showed the advantages of physically-based retrieval algorithms, in both JRC-TIP and MODIS products, and indicated also that, except for MODIS over forests, aggregated products using an uncertainty-based weighted average led to higher agreement between the ECVs changes.

Mangrove Forest Cover and Phenology with Landsat Dense Time Series in Central Queensland, Australia

Wetlands are one of the most biologically productive ecosystems. Wetland ecosystem services, ranging from provision of food security to climate change mitigation, are enormous, far outweighing those of dryland ecosystems per hectare. However, land use change and water regulation infrastructure have reduced connectivity in many river systems and with floodplain and estuarine wetlands. Mangrove forests are critical communities for carbon uptake and storage, pollution control and detoxification, and regulation of natural hazards. Although the clearing of mangroves in Australia is strictly regulated, Great Barrier Reef catchments have suffered landscape modifications and hydrological alterations that can kill mangroves. We used remote sensing datasets to investigate land cover change and both intra- and inter-annual seasonality in mangrove forests in a large estuarine region of Central Queensland, Australia, which encompasses a national park and Ramsar Wetland, and is adjacent to the Great Barrier Reef World Heritage site. We built a time series using spectral, auxiliary, and phenology variables with Landsat surface reflectance products, accessed in Google Earth Engine. Two land cover classes were generated (mangrove versus non-mangrove) in a Random Forest classification. Mangroves decreased by 1480 hectares (−2.31%) from 2009 to 2019. The overall classification accuracies and Kappa coefficient for 2008–2010 and 2018–2020 land cover maps were 95% and 95%, respectively. Using an NDVI-based time series we examined intra- and inter-annual seasonality with linear and harmonic regression models, and second with TIMESAT metrics of mangrove forests in three sections of our study region. Our findings suggest a relationship between mangrove growth phenology along with precipitation anomalies and severe tropical cyclone occurrence over the time series. The detection of responses to extreme events is important to improve understanding of the connections between climate, extreme weather events, and biodiversity in estuarine and mangrove ecosystems.

Accurate Simulation of Both Sensitivity and Variability for Amazonian Photosynthesis: Is It Too Much to Ask?

Estimates of Amazon rainforest gross primary productivity (GPP) differ by a factor of 2 across a suite of three statistical and 18 process models. This wide spread contributes uncertainty to predictions of future climate. We compare the mean and variance of GPP from these models to that of GPP at six eddy covariance (EC) towers. Only one model's mean GPP across all sites falls within a 99% confidence interval for EC GPP, and only one model matches EC variance. The strength of model response to climate drivers is related to model ability to match the seasonal pattern of the EC GPP. Models with stronger seasonal swings in GPP have stronger responses to rain, light, and temperature than does EC GPP. The model to data comparison illustrates a trade-off inherent to deterministic models between accurate simulation of a mean (average) and accurate responsiveness to drivers. The trade-off exists because all deterministic models simplify processes and lack at least some consequential driver or interaction. If a model's sensitivities to included drivers and their interactions are accurate, then deterministically predicted outcomes have less variability than is realistic. If a GPP model has stronger responses to climate drivers than found in data, model predictions may match the observed variance and seasonal pattern but are likely to overpredict GPP response to climate change. High or realistic variability of model estimates relative to reference data indicate that the model is hypersensitive to one or more drivers.

Dynamics of CO2 fluxes and controlling environmental factors in sugarcane (C4)-wheat (C3) ecosystem of dry sub-humid region in India

In this study, CO₂ exchange over sugarcane and wheat growing season was quantified by continuous measurement of CO₂ fluxes using eddy covariance (EC) system from January 2014 to June 2015. We also elaborated on the response of CO₂ fluxes to environmental variables. The results show that the ecosystem has seasonal and diurnal dynamics of CO₂ with a distinctive U-shaped curve in both growing seasons with maximal CO₂ absorption reaching up to -8.94 g C m^-2 day^-1 and -6.08 g C m^-2 day^-1 over sugarcane and wheat crop, respectively. The ecosystem as a whole acted as a carbon sink during the active growing season while it exhibits a carbon source prior to sowing and post-harvesting of crops. The cumulative net ecosystem exchange (NEE), gross primary productivity (GPP), and ecosystem respiration (R_eco) were -923.04, 3316.65, and 2433.18 g C m^-2 over the sugarcane growing season while the values were -192.30, 621.47, and 488.34 g C m^-2 over the wheat growing season. The sesbania (green manure) appeared to be a carbon source once it is incorporated into soil. The response of day-time NEE to photosynthetically active radiation (PAR) under two vapor pressure deficit (VPD) sections (0-20 h Pa and 20-40 h Pa) seems more effective over sugarcane (R² = 0.41-0.61) as compared to the wheat crop (R² = 0.25-0.40). A decrease in net CO₂ uptake was observed under higher VPD conditions. Similarly, night-time NEE was exponentially related to temperature at different soil moisture conditions and showed higher response to optimum soil moisture conditions for sugarcane (R² = 0.87, 0.33 ≤ SWC < 0.42 m³ m^-3) and wheat (R² = 0.75, 0.31 ≤ SWC < 0.37 m³ m^-3) crop seasons. The response of daily averaged NEE to environmental variables through path analysis indicates that PAR was the dominant predictor with the direct path coefficient of -0.65 and -0.74 over sugarcane and wheat growing season, respectively. Satellite-based GPP products from Moderate Resolution Imaging Spectroradiometer (GPP_MOD) and Vegetation Photosynthetic model (GPP_VPM) were also compared with the GPP obtained from EC (GPP_EC) technique. The seasonal dynamics of GPP_EC and GPP_VPM agreed well with each other. This study covers the broad aspects ranging from micro-meteorology to remote sensing over C4-C3 cropping system.

Leaf surface water, not plant water stress, drives diurnal variation in tropical forest canopy water content

Variation in canopy water content (CWC) that can be detected from microwave remote sensing of vegetation optical depth (VOD) has been proposed as an important measure of vegetation water stress. However, the contribution of leaf surface water (LW_s ), arising from dew formation and rainfall interception, to CWC is largely unknown, particularly in tropical forests and other high-humidity ecosystems. We compared VOD data from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) and CWC predicted by a plant hydrodynamics model at four tropical sites in Brazil spanning a rainfall gradient. We assessed how LW_s influenced the relationship between VOD and CWC. The analysis indicates that while CWC is strongly correlated with VOD (R²  = 0.62 across all sites), LW_s accounts for 61-76% of the diurnal variation in CWC despite being < 10% of CWC. Ignoring LW_s weakens the near-linear relationship between CWC and VOD and reduces the consistency in diurnal variation. The contribution of LW_s to CWC variation, however, decreases at longer, seasonal to inter-annual, time scales. Our results demonstrate that diurnal patterns of dew formation and rainfall interception can be an important driver of diurnal variation in CWC and VOD over tropical ecosystems and therefore should be accounted for when inferring plant diurnal water stress from VOD measurements.

What is quantitative plant biology?

Quantitative plant biology is an interdisciplinary field that builds on a long history of biomathematics and biophysics. Today, thanks to high spatiotemporal resolution tools and computational modelling, it sets a new standard in plant science. Acquired data, whether molecular, geometric or mechanical, are quantified, statistically assessed and integrated at multiple scales and across fields. They feed testable predictions that, in turn, guide further experimental tests. Quantitative features such as variability, noise, robustness, delays or feedback loops are included to account for the inner dynamics of plants and their interactions with the environment. Here, we present the main features of this ongoing revolution, through new questions around signalling networks, tissue topology, shape plasticity, biomechanics, bioenergetics, ecology and engineering. In the end, quantitative plant biology allows us to question and better understand our interactions with plants. In turn, this field opens the door to transdisciplinary projects with the society, notably through citizen science.

Temporal variability in tree responses to elevated atmospheric CO2

At leaf level, elevated atmospheric CO₂ concentration (eCO₂ ) results in stimulation of carbon net assimilation and reduction of stomatal conductance. However, a comprehensive understanding of the impact of eCO₂ at larger temporal (seasonal and annual) and spatial (from leaf to whole-tree) scales is still lacking. Here, we review overall trends, magnitude and drivers of dynamic tree responses to eCO₂ , including carbon and water relations at the leaf and the whole-tree level. Spring and early season leaf responses are most susceptible to eCO₂ and are followed by a down-regulation towards the onset of autumn. At the whole-tree level, CO₂ fertilization causes consistent biomass increments in young seedlings only, whereas mature trees show a variable response. Elevated CO₂ -induced reductions in leaf stomatal conductance do not systematically translate into limitation of whole-tree transpiration due to the unpredictable response of canopy area. Reduction in the end-of-season carbon sink demand and water-limiting strategies are considered the main drivers of seasonal tree responses to eCO₂ . These large temporal and spatial variabilities in tree responses to eCO₂ highlight the risk of predicting tree behavior to eCO₂ based on single leaf-level point measurements as they only reveal snapshots of the dynamic responses to eCO₂ .

Linking plant hydraulics and the fast-slow continuum to understand resilience to drought in tropical ecosystems

Tropical ecosystems have the highest levels of biodiversity, cycle more water and absorb more carbon than any other terrestrial ecosystem on Earth. Consequently, these ecosystems are extremely important components of Earth's climatic system and biogeochemical cycles. Plant hydraulics is an essential discipline to understand and predict the dynamics of tropical vegetation in scenarios of changing water availability. Using published plant hydraulic data we show that the trade-off between drought avoidance (expressed as deep-rooting, deciduousness and capacitance) and hydraulic safety (P50 - the water potential when plants lose 50% of their maximum hydraulic conductivity) is a major axis of physiological variation across tropical ecosystems. We also propose a novel and independent axis of hydraulic trait variation linking vulnerability to hydraulic failure (expressed as the hydraulic safety margin (HSM)) and growth, where inherent fast-growing plants have lower HSM compared to slow-growing plants. We surmise that soil nutrients are fundamental drivers of tropical community assembly determining the distribution and abundance of the slow-safe/fast-risky strategies. We conclude showing that including either the growth-HSM or the resistance-avoidance trade-off in models can make simulated tropical rainforest communities substantially more vulnerable to drought than similar communities without the trade-off. These results suggest that vegetation models need to represent hydraulic trade-off axes to accurately project the functioning and distribution of tropical ecosystems.

Patterns and mechanisms of spatial variation in tropical forest productivity, woody residence time, and biomass

Tropical forests vary widely in biomass carbon (C) stocks and fluxes even after controlling for forest age. A mechanistic understanding of this variation is critical to accurately predicting responses to global change. We review empirical studies of spatial variation in tropical forest biomass, productivity and woody residence time, focusing on mature forests. Woody productivity and biomass decrease from wet to dry forests and with elevation. Within lowland forests, productivity and biomass increase with temperature in wet forests, but decrease with temperature where water becomes limiting. Woody productivity increases with soil fertility, whereas residence time decreases, and biomass responses are variable, consistent with an overall unimodal relationship. Areas with higher disturbance rates and intensities have lower woody residence time and biomass. These environmental gradients all involve both direct effects of changing environments on forest C fluxes and shifts in functional composition - including changing abundances of lianas - that substantially mitigate or exacerbate direct effects. Biogeographic realms differ significantly and importantly in productivity and biomass, even after controlling for climate and biogeochemistry, further demonstrating the importance of plant species composition. Capturing these patterns in global vegetation models requires better mechanistic representation of water and nutrient limitation, plant compositional shifts and tree mortality.

Trends in vegetation productivity related to climate change in China's Pearl River Delta

Climate change will be a powerful stressor on ecosystems and biodiversity in the second half of the 21^st century. In this study, we used the satellite-derived Normalized Difference Vegetation Index (NDVI) to examine a 34-year trend along with the response of vegetation to climate indicators surrounding the world’s largest megacity: the Pearl River Delta (PRD) of China. An overall increasing trend is observed in vegetation productivity metrics over the study period 1982 to 2015. Increase in winter productivity in both natural ecosystems and croplands is more related to increasing temperatures (r = 0.5–0.78), than to changes in rainfall. For growing season productivity, negative correlations with temperature were observed in cropland regions, and some forests in the northern part of PRD region, suggesting high-temperature stress on crop production and forest vegetation. However, increased winter and spring temperatures provide higher opportunities for cropping in winter. During the decade 1995–2004, vegetation productivity metrics showed a reversal in the upward trend. The geographical and biological complexity of the region under significant climatic and development impacts suggests causative factors would be synergistic. These include our observed decrease in sunshine hours, increasing cloud cover associated with atmospheric aerosols from industrial and urban development, direct pollution effects on plant growth, and exceedance of high temperature growth thresholds.

Rapid nitrogen fixation contributes to a similar growth and photosynthetic rate of Robinia pseudoacacia supplied with different levels of nitrogen

Robinia pseudoacacia L. is a legume species that is widely used in afforestation, which has high N2 fixation capacity and rapid growth rate. Both nitrogen (N) supply and phenology affect plant growth, photosynthesis and leaf senescence. The aim of this study was to determine how N supply affects N2 fixation, leaf photosynthesis and senescence of R. pseudoacacia at different phenological stages. Seedlings of R. pseudoacacia were supplied with different levels of 15N-labelled NH4NO3 solution, with seedlings of Sophora japonica Linn. as reference plants to calculate the percentage of N derived from the atmospheric N2 (%Ndfa). Compared with plants supplied with a high N level, those with a low N supply had a higher %Ndfa at an early developmental stage. Nitrogen fixation compensated the effect of a low N supply on plant growth in R. pseudoacacia. A high N supply decreased biomass allocation to lateral roots and nodules, and increased the relative growth rate of plant height as well as specific leaf area. The eighth mature compound leaf of R. pseudoacacia tended to have a higher net photosynthetic rate than the fourth leaf, and the leaves still maintained a moderate photosynthetic rate in early autumn. Plants tended to allocate more biomass to leaves at an early developmental stage and to stems and roots at a later developmental stage (3 months old). The N level did not affect leaf photosynthesis at different phenological stages, primarily due to (i) a high %Ndfa under low N supply at early growing stage, and a similar high %Ndfa under all N supplies at a late growing stage, and (ii) the delayed greening phenotype of expanding leaves to save nutrients for mature leaves.

Does economic optimisation explain LAI and leaf trait distributions across an Amazon soil moisture gradient?

Leaf area index (LAI) underpins terrestrial ecosystem functioning, yet our ability to predict LAI remains limited. Across Amazon forests, mean LAI, LAI seasonal dynamics and leaf traits vary with soil moisture stress. We hypothesise that LAI variation can be predicted via an optimality-based approach, using net canopy C export (NCE, photosynthesis minus the C cost of leaf growth and maintenance) as a fitness proxy. We applied a process-based terrestrial ecosystem model to seven plots across a moisture stress gradient with detailed in situ measurements, to determine nominal plant C budgets. For each plot, we then compared observations and simulations of the nominal (i.e. observed) C budget to simulations of alternative, experimental budgets. Experimental budgets were generated by forcing the model with synthetic LAI timeseries (across a range of mean LAI and LAI seasonality) and different leaf trait combinations (leaf mass per unit area, lifespan, photosynthetic capacity and respiration rate) operating along the leaf economic spectrum. Observed mean LAI and LAI seasonality across the soil moisture stress gradient maximised NCE, and were therefore consistent with optimality-based predictions. Yet, the predictive power of an optimality-based approach was limited due to the asymptotic response of simulated NCE to mean LAI and LAI seasonality. Leaf traits fundamentally shaped the C budget, determining simulated optimal LAI and total NCE. Long-lived leaves with lower maximum photosynthetic capacity maximised simulated NCE under aseasonal high mean LAI, with the reverse found for short-lived leaves and higher maximum photosynthetic capacity. The simulated leaf trait LAI trade-offs were consistent with observed distributions. We suggest that a range of LAI strategies could be equally economically viable at local level, though we note several ecological limitations to this interpretation (e.g. between-plant competition). In addition, we show how leaf trait trade-offs enable divergence in canopy strategies. Our results also allow an assessment of the usefulness of optimality-based approaches in simulating primary tropical forest functioning, evaluated against in situ data.

New generation geostationary satellite observations support seasonality in greenness of the Amazon evergreen forests

Assessing the seasonal patterns of the Amazon rainforests has been difficult because of the paucity of ground observations and persistent cloud cover over these forests obscuring optical remote sensing observations. Here, we use data from a new generation of geostationary satellites that carry the Advanced Baseline Imager (ABI) to study the Amazon canopy. ABI is similar to the widely used polar orbiting sensor, the Moderate Resolution Imaging Spectroradiometer (MODIS), but provides observations every 10-15 min. Our analysis of NDVI data collected over the Amazon during 2018-19 shows that ABI provides 21-35 times more cloud-free observations in a month than MODIS. The analyses show statistically significant changes in seasonality over 85% of Amazon forest pixels, an area about three times greater than previously reported using MODIS data. Though additional work is needed in converting the observed changes in seasonality into meaningful changes in canopy dynamics, our results highlight the potential of the new generation geostationary satellites to help us better understand tropical ecosystems, which has been a challenge with only polar orbiting satellites.

Integrating high resolution drone imagery and forest inventory to distinguish canopy and understory trees and quantify their contributions to forest structure and dynamics

Tree growth and survival differ strongly between canopy trees (those directly exposed to overhead light), and understory trees. However, the structural complexity of many tropical forests makes it difficult to determine canopy positions. The integration of remote sensing and ground-based data enables this determination and measurements of how canopy and understory trees differ in structure and dynamics. Here we analyzed 2 cm resolution RGB imagery collected by a Remotely Piloted Aircraft System (RPAS), also known as drone, together with two decades of bi-annual tree censuses for 2 ha of old growth forest in the Central Amazon. We delineated all crowns visible in the imagery and linked each crown to a tagged stem through field work. Canopy trees constituted 40% of the 1244 inventoried trees with diameter at breast height (DBH) > 10 cm, and accounted for ~70% of aboveground carbon stocks and wood productivity. The probability of being in the canopy increased logistically with tree diameter, passing through 50% at 23.5 cm DBH. Diameter growth was on average twice as large in canopy trees as in understory trees. Growth rates were unrelated to diameter in canopy trees and positively related to diameter in understory trees, consistent with the idea that light availability increases with diameter in the understory but not the canopy. The whole stand size distribution was best fit by a Weibull distribution, whereas the separate size distributions of understory trees or canopy trees > 25 cm DBH were equally well fit by exponential and Weibull distributions, consistent with mechanistic forest models. The identification and field mapping of crowns seen in a high resolution orthomosaic revealed new patterns in the structure and dynamics of trees of canopy vs. understory at this site, demonstrating the value of traditional tree censuses with drone remote sensing.

A Novel Approach to Modelling Mangrove Phenology from Satellite Images: A Case Study from Northern Australia

Around the world, the effects of changing plant phenology are evident in many ways: from earlier and longer growing seasons to altering the relationships between plants and their natural pollinators. Plant phenology is often monitored using satellite images and parametric methods. Parametric methods assume that ecosystems have unimodal phenologies and that the phenology model is invariant through space and time. In evergreen ecosystems such as mangrove forests, these assumptions may not hold true. Here we present a novel, data-driven approach to extract plant phenology from Landsat imagery using Generalized Additive Models (GAMs). Using GAMs, we created models for six different mangrove forests across Australia. In contrast to parametric methods, GAMs let the data define the shape of the phenological curve, hence showing the unique characteristics of each study site. We found that the Enhanced Vegetation Index (EVI) model is related to leaf production rate (from in situ data), leaf gain and net leaf production (from the published literature). We also found that EVI does not respond immediately to leaf gain in most cases, but has a two- to three-month lag. We also identified the start of season and peak growing season dates at our field site. The former occurs between September and October and the latter May and July. The GAMs allowed us to identify dual phenology events in our study sites, indicated by two instances of high EVI and two instances of low EVI values throughout the year. We contribute to a better understanding of mangrove phenology by presenting a data-driven method that allows us to link physical changes of mangrove forests with satellite imagery. In the future, we will use GAMs to (1) relate phenology to environmental variables (e.g., temperature and rainfall) and (2) predict phenological changes.

Climate-Driven Variability and Trends in Plant Productivity Over Recent Decades Based on Three Global Products

Variability in climate exerts a strong influence on vegetation productivity (gross primary productivity; GPP), and therefore has a large impact on the land carbon sink. However, no direct observations of global GPP exist, and estimates rely on models that are constrained by observations at various spatial and temporal scales. Here, we assess the consistency in GPP from global products which extend for more than three decades; two observation-based approaches, the upscaling of FLUXNET site observations (FLUXCOM) and a remote sensing derived light use efficiency model (RS-LUE), and from a suite of terrestrial biosphere models (TRENDYv6). At local scales, we find high correlations in annual GPP among the products, with exceptions in tropical and high northern latitudes. On longer time scales, the products agree on the direction of trends over 58% of the land, with large increases across northern latitudes driven by warming trends. Further, tropical regions exhibit the largest interannual variability in GPP, with both rainforests and savannas contributing substantially. Variability in savanna GPP is likely predominantly driven by water availability, although temperature could play a role via soil moisture-atmosphere feedbacks. There is, however, no consensus on the magnitude and driver of variability of tropical forests, which suggest uncertainties in process representations and underlying observations remain. These results emphasize the need for more direct long-term observations of GPP along with an extension of in situ networks in underrepresented regions (e.g., tropical forests). Such capabilities would support efforts to better validate relevant processes in models, to more accurately estimate GPP.

An optimality-based model explains seasonal variation in C3 plant photosynthetic capacity

The maximum rate of carboxylation (V_cmax ) is an essential leaf trait determining the photosynthetic capacity of plants. Existing approaches for estimating V_cmax at large scale mainly rely on empirical relationships with proxies such as leaf nitrogen/chlorophyll content or hyperspectral reflectance, or on complicated inverse models from gross primary production or solar-induced fluorescence. A novel mechanistic approach based on the assumption that plants optimize resource investment coordinating with environment and growth has been shown to accurately predict C3 plant V_cmax based on mean growing season environmental conditions. However, the ability of optimality theory to explain seasonal variation in V_cmax has not been fully investigated. Here, we adapt an optimality-based model to simulate daily V_cmax,25C (V_cmax at a standardized temperature of 25°C) by incorporating the effects of antecedent environment, which affects current plant functioning, and dynamic light absorption, which coordinates with plant functioning. We then use seasonal V_cmax,25C field measurements from 10 sites across diverse ecosystems to evaluate model performance. Overall, the model explains about 83% of the seasonal variation in C3 plant V_cmax,25C across the 10 sites, with a medium root mean square error of 12.3 μmol m^-2  s^-1 , which suggests that seasonal changes in V_cmax,25C are consistent with optimal plant function. We show that failing to account for acclimation to antecedent environment or coordination with dynamic light absorption dramatically decreases estimation accuracy. Our results show that optimality-based approach can accurately reproduce seasonal variation in canopy photosynthetic potential, and suggest that incorporating such theory into next-generation trait-based terrestrial biosphere models would improve predictions of global photosynthesis.

Amazon rainforest photosynthesis increases in response to atmospheric dryness

Earth system models predict that increases in atmospheric and soil dryness will reduce photosynthesis in the Amazon rainforest, with large implications for the global carbon cycle. Using in situ observations, solar-induced fluorescence, and nonlinear machine learning techniques, we show that, in reality, this is not necessarily the case: In many of the wettest parts of this region, photosynthesis and biomass tend to increase with increased atmospheric dryness, despite the associated reductions in canopy conductance to CO₂ These results can be largely explained by changes in canopy properties, specifically, new leaves flushed during the dry season have higher photosynthetic capacity than the leaves they replace, compensating for the negative stomatal response to increased dryness. As atmospheric dryness will increase with climate change, our study highlights the importance of reframing how we represent the response of ecosystem photosynthesis to atmospheric dryness in very wet regions, to accurately quantify the land carbon sink.

Directional change in leaf dry matter δ 13C during leaf development is widespread in C3 plants

BACKGROUND AND AIMS

The stable carbon isotope ratio of leaf dry matter (δ 13Cp) is generally a reliable recorder of intrinsic water-use efficiency in C3 plants. Here, we investigated a previously reported pattern of developmental change in leaf δ 13Cp during leaf expansion, whereby emerging leaves are initially 13C-enriched compared to mature leaves on the same plant, with their δ 13Cp decreasing during leaf expansion until they eventually take on the δ 13Cp of other mature leaves.

METHODS

We compiled data to test whether the difference between mature and young leaf δ 13Cp differs between temperate and tropical species, or between deciduous and evergreen species. We also tested whether the developmental change in δ 13Cp is indicative of a concomitant change in intrinsic water-use efficiency. To gain further insight, we made online measurements of 13C discrimination (∆ 13C) in young and mature leaves.

KEY RESULTS

We found that the δ 13Cp difference between mature and young leaves was significantly larger for deciduous than for evergreen species (-2.1 ‰ vs. -1.4 ‰, respectively). Counter to expectation based on the change in δ 13Cp, intrinsic water-use efficiency did not decrease between young and mature leaves; rather, it did the opposite. The ratio of intercellular to ambient CO2 concentrations (ci/ca) was significantly higher in young than in mature leaves (0.86 vs. 0.72, respectively), corresponding to lower intrinsic water-use efficiency. Accordingly, instantaneous ∆ 13C was also higher in young than in mature leaves. Elevated ci/ca and ∆ 13C in young leaves resulted from a combination of low photosynthetic capacity and high day respiration rates.

CONCLUSION

The decline in leaf δ 13Cp during leaf expansion appears to reflect the addition of the expanding leaf's own 13C-depleted photosynthetic carbon to that imported from outside the leaf as the leaf develops. This mixing of carbon sources results in an unusual case of isotopic deception: less negative δ 13Cp in young leaves belies their low intrinsic water-use efficiency.

Approaches of Satellite Remote Sensing for the Assessment of Above-Ground Biomass across Tropical Forests: Pan-tropical to National Scales

Tropical forests are acknowledged for providing important ecosystem services and are renowned as “the lungs of the planet Earth” due to their role in the exchange of gasses—particularly inhaling CO2 and breathing out O2—within the atmosphere. Overall, the forests provide 50% of the total plant biomass of the Earth, which accounts for 450–650 PgC globally. Understanding and accurate estimates of tropical forest biomass stocks are imperative in ascertaining the contribution of the tropical forests in global carbon dynamics. This article provides a review of remote-sensing-based approaches for the assessment of above-ground biomass (AGB) across the tropical forests (global to national scales), summarizes the current estimate of pan-tropical AGB, and discusses major advancements in remote-sensing-based approaches for AGB mapping. The review is based on the journal papers, books and internet resources during the 1980s to 2020. Over the past 10 years, a myriad of research has been carried out to develop methods of estimating AGB by integrating different remote sensing datasets at varying spatial scales. Relationships of biomass with canopy height and other structural attributes have developed a new paradigm of pan-tropical or global AGB estimation from space-borne satellite remote sensing. Uncertainties in mapping tropical forest cover and/or forest cover change are related to spatial resolution; definition adapted for ‘forest’ classification; the frequency of available images; cloud covers; time steps used to map forest cover change and post-deforestation land cover land use (LCLU)-type mapping. The integration of products derived from recent Synthetic Aperture Radar (SAR) and Light Detection and Ranging (LiDAR) satellite missions with conventional optical satellite images has strong potential to overcome most of these uncertainties for recent or future biomass estimates. However, it will remain a challenging task to map reference biomass stock in the 1980s and 1990s and consequently to accurately quantify the loss or gain in forest cover over the periods. Aside from these limitations, the estimation of biomass and carbon balance can be enhanced by taking account of post-deforestation forest recovery and LCLU type; land-use history; diversity of forest being recovered; variations in physical attributes of plants (e.g., tree height; diameter; and canopy spread); environmental constraints; abundance and mortalities of trees; and the age of secondary forests. New methods should consider peak carbon sink time while developing carbon sequestration models for intact or old-growth tropical forests as well as the carbon sequestration capacity of recovering forest with varying levels of floristic diversity.

Optimal leaf life strategies determine Vc,max dynamic during ontogeny

Leaf photosynthetic properties, for example the maximum carboxylation velocity or V_c,max , change with leaf age due to ontogenetic processes. This study introduces an optimal dynamic allocation scheme to model changes in leaf-level photosynthetic capacity as a function of leaf biochemical constraints (costs of synthesis and defence), nitrogen availability and other environmental factors (e.g. light). The model consists of a system of equations describing RuBisCO synthesis and degradation within chloroplasts, defence and ageing at leaf levels, nitrogen transfer and carbon budget at plant levels. Model results show that optimal allocation principles explained RuBisCO dynamics with leaf age. An approximated analytical solution can reproduce the basic pattern of RuBisCO and V_c,max in rice and in two tropical tree species. The model also reveals leaf life complementarities that remained unexplained in previous approaches, as the interplay between V_c,max at maturation, life span and the decline in photosynthetic capacity with age. Furthermore, it explores the role of defence, which is not implemented in current models. This framework covers some of the existing gaps in integrating multiple processes across plant organs (chloroplast, leaf and whole plant) and is a first-step towards representing mechanistically leaf ontogenetic processes into physiological and ecosystem models.

Allometric constraints and competition enable the simulation of size structure and carbon fluxes in a dynamic vegetation model of tropical forests (LM3PPA-TV)

Tropical forests are a key determinant of the functioning of the Earth system, but remain a major source of uncertainty in carbon cycle models and climate change projections. In this study, we present an updated land model (LM3PPA-TV) to improve the representation of tropical forest structure and dynamics in Earth system models (ESMs). The development and parameterization of LM3PPA-TV drew on extensive datasets on tropical tree traits and long-term field censuses from Barro Colorado Island (BCI), Panama. The model defines a new plant functional type (PFT) based on the characteristics of shade-tolerant, tropical tree species, implements a new growth allocation scheme based on realistic tree allometries, incorporates hydraulic constraints on biomass accumulation, and features a new compartment for tree branches and branch fall dynamics. Simulation experiments reproduced observed diurnal and seasonal patterns in stand-level carbon and water fluxes, as well as mean canopy and understory tree growth rates, tree size distributions, and stand-level biomass on BCI. Simulations at multiple sites captured considerable variation in biomass and size structure across the tropical forest biome, including observed responses to precipitation and temperature. Model experiments suggested a major role of water limitation in controlling geographic variation forest biomass and structure. However, the failure to simulate tropical forests under extreme conditions and the systematic underestimation of forest biomass in Paleotropical locations highlighted the need to incorporate variation in hydraulic traits and multiple PFTs that capture the distinct floristic composition across tropical domains. The continued pressure on tropical forests from global change demands models which are able to simulate alternative successional pathways and their pace to recovery. LM3PPA-TV provides a tool to investigate geographic variation in tropical forests and a benchmark to continue improving the representation of tropical forests dynamics and their carbon storage potential in ESMs.

Concurrent and lagged effects of spring greening on seasonal carbon gain and water loss across the Northern Hemisphere

Spring greening has been widely observed across the Northern Hemisphere (NH) using a remotely sensed vegetation index (e.g., the normalized difference vegetation index, NDVI). However, there is still a debate on the ecological effects of spring greening on seasonal carbon and water budgets. This study jointly investigated the concurrent and lagged effects of spring greening on carbon gain (gross primary productivity, GPP) and water loss (evapotranspiration, ET) in the summer-active ecosystems at mid and high latitudes of NH using remote sensing and multimodel ensemble data during 1982-2013. The results showed that the collective promotion of spring greening to concurrent GPP and ET is widespread despite variations in magnitude and significance. Both beneficial and adverse lagged effects of spring greening on summer GPP commonly appear with an obvious spatial heterogeneity and difference among climate-plant types. However, the expected significant suppression of spring greening to summer GPP was rarely observed even in the areas where spring ET was significantly promoted by spring greening. Nevertheless, when drought was taken into account, the response patterns of spring water use to spring greening varied to some extent, and the adverse lagged effect of spring greening to summer GPP appeared or strengthened in some regions, especially during the years with dry summer. Given the predicted warming of the climate and more frequent climatic extremes, the adverse effect of spring greening should be given more attention.

Fine-root dynamics vary with soil depth and precipitation in a low-nutrient tropical forest in the Central Amazonia

A common assumption in tropical ecology is that root systems respond rapidly to climatic cues but that most of that response is limited to the uppermost layer of the soil, with relatively limited changes in deeper layers. However, this assumption has not been tested directly, preventing models from accurately predicting the response of tropical forests to environmental change.We measured seasonal dynamics of fine roots in an upper-slope plateau in Central Amazonia mature forest using minirhizotrons to 90 cm depth, which were calibrated with fine roots extracted from soil cores.Root productivity and mortality in surface soil layers were positively correlated with precipitation, whereas root standing length was greater during the dry periods at the deeper layers. Contrary to historical assumptions, a large fraction of fine-root standing biomass (46%) and productivity (41%) was found in soil layers deeper than 30 cm. Furthermore, root turnover decreased linearly with soil depth.Our findings demonstrate a relationship between fine-root dynamics and precipitation regimes in Central Amazonia. Our results also emphasize the importance of deeper roots for accurate estimates of primary productivity and the interaction between roots and carbon, water, and nutrients.

Plant Traits Help Explain the Tight Relationship between Vegetation Indices and Gross Primary Production

Remotely-sensed Vegetation Indices (VIs) are often tightly correlated with terrestrial ecosystem CO2 uptake (Gross Primary Production or GPP). These correlations have been exploited to infer GPP at local to global scales and over half-hour to decadal periods, though the underlying mechanisms remain incompletely understood. We used satellite remote sensing and eddy covariance observations at 10 sites across a California climate gradient to explore the relationships between GPP, the Enhanced Vegetation Index (EVI), the Normalized Difference Vegetation Index (NDVI), and the Near InfraRed Vegetation (NIRv) index. EVI and NIRv were linearly correlated with GPP across both space and time, whereas the relationship between NDVI and GPP was less general. We explored these interactions using radiative transfer and GPP models forced with in-situ plant trait and soil reflectance observations. GPP ultimately reflects the product of Leaf Area Index (LAI) and leaf level CO2 uptake (Aleaf); a VI that is sensitive mainly to LAI will lack generality across ecosystems that differ in Aleaf. EVI and NIRv showed a strong, multiplicative sensitivity to LAI and Leaf Mass per Area (LMA). LMA was correlated with Aleaf, and EVI and NIRv consequently mimic GPP’s multiplicative sensitivity to LAI and Aleaf, as mediated by LMA. NDVI was most sensitive to LAI, and was relatively insensitive to leaf properties over realistic conditions; NDVI lacked EVI and NIRv’s sensitivity to both LAI and Aleaf. These findings carry implications for understanding the limitations of current VIs for predicting GPP, and also for devising strategies to improve predictions of GPP.

Sun-Angle Effects on Remote-Sensing Phenology Observed and Modelled Using Himawari-8

Satellite remote sensing of vegetation at regional to global scales is undertaken at considerable variations in solar zenith angle (SZA) across space and time, yet the extent to which these SZA variations matter for the retrieval of phenology remains largely unknown. Here we examined the effect of seasonal and spatial variations in SZA on retrieving vegetation phenology from time series of the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) across a study area in southeastern Australia encompassing forest, woodland, and grassland sites. The vegetation indices (VI) data span two years and are from the Advanced Himawari Imager (AHI), which is onboard the Japanese Himawari-8 geostationary satellite. The semi-empirical RossThick-LiSparse-Reciprocal (RTLSR) bidirectional reflectance distribution function (BRDF) model was inverted for each spectral band on a daily basis using 10-minute reflectances acquired by H-8 AHI at different sun-view geometries for each site. The inverted RTLSR model was then used to forward calculate surface reflectance at three constant SZAs (20°, 40°, 60°) and one seasonally varying SZA (local solar noon), all normalised to nadir view. Time series of NDVI and EVI adjusted to different SZAs at nadir view were then computed, from which phenological metrics such as start and end of growing season were retrieved. Results showed that NDVI sensitivity to SZA was on average nearly five times greater than EVI sensitivity. VI sensitivity to SZA also varied among sites (biome types) and phenological stages, with NDVI sensitivity being higher during the minimum greenness period than during the peak greenness period. Seasonal SZA variations altered the temporal profiles of both NDVI and EVI, with more pronounced differences in magnitude among NDVI time series normalised to different SZAs. When using VI time series that allowed SZA to vary at local solar noon, the uncertainties in estimating start, peak, end, and length of growing season introduced by local solar noon varying SZA VI time series, were 7.5, 3.7, 6.5, and 11.3 days for NDVI, and 10.4, 11.9, 6.5, and 8.4 days for EVI respectively, compared to VI time series normalised to a constant SZA. Furthermore, the stronger SZA dependency of NDVI compared with EVI, resulted in up to two times higher uncertainty in estimating annual integrated VI, a commonly used remote-sensing proxy for vegetation productivity. Since commonly used satellite products are not generally normalised to a constant sun-angle across space and time, future studies to assess the sun-angle effects on satellite applications in agriculture, ecology, environment, and carbon science are urgently needed. Measurements taken by new-generation geostationary (GEO) satellites offer an important opportunity to refine this assessment at finer temporal scales. In addition, studies are needed to evaluate the suitability of different BRDF models for normalising sun-angle across a broad spectrum of vegetation structure, phenological stages and geographic locations. Only through continuous investigations on how sun-angle variations affect spatiotemporal vegetation dynamics and what is the best strategy to deal with it, can we achieve a more quantitative remote sensing of true signals of vegetation change across the entire globe and through time.

OCO-2 Solar-Induced Chlorophyll Fluorescence Variability across Ecoregions of the Amazon Basin and the Extreme Drought Effects of El Niño (2015–2016)

Amazonian ecosystems are major biodiversity hotspots and carbon sinks that may lose species to extinction and become carbon sources due to extreme dry or warm conditions. We investigated the seasonal patterns of high-resolution solar-induced chlorophyll fluorescence (SIF) measured by the satellite Orbiting Carbon Observatory-2 (OCO-2) across the Amazonian ecoregions to assess the area´s phenology and extreme drought vulnerability. SIF is an indicator of the photosynthetic activity of chlorophyll molecules and is assumed to be directly related to gross primary production (GPP). We analyzed SIF variability in the Amazon basin during the period between September 2014 and December 2018. In particular, we focused on the SIF drought response under the extreme drought period during the strong El Niño in 2015–2016, as well as the 6-month drought peak period. During the drought´s peak months, the SIF decreased and increased with different intensities across the ecoregions of the Amazonian moist broadleaf forest (MBF) biome. Under a high temperature, a high vapor pressure deficit, and extreme drought conditions, the SIF presented differences from −31.1% to +17.6%. Such chlorophyll activity variations have been observed in plant-level measurements of active fluorescence in plants undergoing physiological responses to water or heat stress. Thus, it is plausible that the SIF variations in the ecoregions’ ecosystems occurred as a result of water and heat stress, and arguably because of drought-driven vegetation mortality and collateral effects in their species composition and community structures. The SIF responses to drought at the ecoregional scale indicate that there are different levels of resilience to drought across MBF ecosystems that the currently used climate- and biome-region scales do not capture. Finally, we identified monthly SIF values of 32 ecoregions, including non-MBF biomes, which may give the first insights into the photosynthetic activity dynamics of Amazonian ecoregions.

Does the leaf economic spectrum hold within plant functional types? A Bayesian multivariate trait meta-analysis

The leaf economic spectrum is a widely studied axis of plant trait variability that defines a trade-off between leaf longevity and productivity. While this has been investigated at the global scale, where it is robust, and at local scales, where deviations from it are common, it has received less attention at the intermediate scale of plant functional types (PFTs). We investigated whether global leaf economic relationships are also present within the scale of plant functional types (PFTs) commonly used by Earth System models, and the extent to which this global-PFT hierarchy can be used to constrain trait estimates. We developed a hierarchical multivariate Bayesian model that assumes separate means and covariance structures within and across PFTs and fit this model to seven leaf traits from the TRY database related to leaf longevity, morphology, biochemistry, and photosynthetic metabolism. Although patterns of trait covariation were generally consistent with the leaf economic spectrum, we found three approximate tiers to this consistency. Relationships among morphological and biochemical traits (specific leaf area [SLA], N, P) were the most robust within and across PFTs, suggesting that covariation in these traits is driven by universal leaf construction trade-offs and stoichiometry. Relationships among metabolic traits (dark respiration [R_d ], maximum RuBisCo carboxylation rate [V_c,max ], maximum electron transport rate [J_max ]) were slightly less consistent, reflecting in part their much sparser sampling (especially for high-latitude PFTs), but also pointing to more flexible plasticity in plant metabolistm. Finally, relationships involving leaf lifespan were the least consistent, indicating that leaf economic relationships related to leaf lifespan are dominated by across-PFT differences and that within-PFT variation in leaf lifespan is more complex and idiosyncratic. Across all traits, this covariance was an important source of information, as evidenced by the improved imputation accuracy and reduced predictive uncertainty in multivariate models compared to univariate models. Ultimately, our study reaffirms the value of studying not just individual traits but the multivariate trait space and the utility of hierarchical modeling for studying the scale dependence of trait relationships.

Whole-plant optimality predicts changes in leaf nitrogen under variable CO2 and nutrient availability

Vegetation nutrient limitation is essential for understanding ecosystem responses to global change. In particular, leaf nitrogen (N) is known to be plastic under changed nutrient limitation. However, models can often not capture these observed changes, leading to erroneous predictions of whole-ecosystem stocks and fluxes. We hypothesise that an optimality approach can improve representation of leaf N content compared to existing empirical approaches. Unlike previous optimality-based approaches, which adjust foliar N concentrations based on canopy carbon export, we use a maximisation criterion based on whole-plant growth, and allow for a lagged response of foliar N to this maximisation criterion to account for the limited plasticity of this plant trait. We test these model variants at a range of Free-Air CO₂ Enrichment and N fertilisation experimental sites. We show that a model based solely on canopy carbon export fails to reproduce observed patterns and predicts decreasing leaf N content with increased N availability. However, an optimal model which maximises total plant growth can correctly reproduce the observed patterns. The optimality model we present here is a whole-plant approach which reproduces biologically realistic changes in leaf N and can thereby improve ecosystem-level predictions under transient conditions.

The response of stomatal conductance to seasonal drought in tropical forests

Stomata regulate CO₂ uptake for photosynthesis and water loss through transpiration. The approaches used to represent stomatal conductance (g_s ) in models vary. In particular, current understanding of drivers of the variation in a key parameter in those models, the slope parameter (i.e. a measure of intrinsic plant water-use-efficiency), is still limited, particularly in the tropics. Here we collected diurnal measurements of leaf gas exchange and leaf water potential (Ψ_leaf ), and a suite of plant traits from the upper canopy of 15 tropical trees in two contrasting Panamanian forests throughout the dry season of the 2016 El Niño. The plant traits included wood density, leaf-mass-per-area (LMA), leaf carboxylation capacity (V_c,max ), leaf water content, the degree of isohydry, and predawn Ψ_leaf . We first investigated how the choice of four commonly used leaf-level g_s models with and without the inclusion of Ψ_leaf as an additional predictor variable influence the ability to predict g_s , and then explored the abiotic (i.e. month, site-month interaction) and biotic (i.e. tree-species-specific characteristics) drivers of slope parameter variation. Our results show that the inclusion of Ψ_leaf did not improve model performance and that the models that represent the response of g_s to vapor pressure deficit performed better than corresponding models that respond to relative humidity. Within each g_s model, we found large variation in the slope parameter, and this variation was attributable to the biotic driver, rather than abiotic drivers. We further investigated potential relationships between the slope parameter and the six available plant traits mentioned above, and found that only one trait, LMA, had a significant correlation with the slope parameter (R²  = 0.66, n = 15), highlighting a potential path towards improved model parameterization. This study advances understanding of g_s dynamics over seasonal drought, and identifies a practical, trait-based approach to improve modeling of carbon and water exchange in tropical forests.

Gas exchange and water-use efficiency in plant canopies

In this review, I first address the basics of gas exchange, water-use efficiency and carbon isotope discrimination in C₃ plant canopies. I then present a case study of water-use efficiency in northern Australian tree species. In general, C₃ plants face a trade-off whereby increasing stomatal conductance for a given set of conditions will result in a higher CO₂ assimilation rate, but a lower photosynthetic water-use efficiency. A common garden experiment suggested that tree species which are able to establish and grow in drier parts of northern Australia have a capacity to use water rapidly when it is available through high stomatal conductance, but that they do so at the expense of low water-use efficiency. This may explain why community-level carbon isotope discrimination does not decrease as steeply with decreasing rainfall on the North Australian Tropical Transect as has been observed on some other precipitation gradients. Next, I discuss changes in water-use efficiency that take place during leaf expansion in C₃ plant leaves. Leaf phenology has recently been recognised as a significant driver of canopy gas exchange in evergreen forest canopies, and leaf expansion involves changes in both photosynthetic capacity and water-use efficiency. Following this, I discuss the role of woody tissue respiration in canopy gas exchange and how photosynthetic refixation of respired CO₂ can increase whole-plant water-use efficiency. Finally, I discuss the role of water-use efficiency in driving terrestrial plant responses to global change, especially the rising concentration of atmospheric CO₂ . In coming decades, increases in plant water-use efficiency caused by rising CO₂ are likely to partially mitigate impacts on plants of drought stress caused by global warming.

Dynamics of photosynthetic responses in 10 rubber tree (Hevea brasiliensis) clones in Colombian Amazon: Implications for breeding strategies

The rubber tree [Hevea brasiliensis (Willd. Ex Adr. de Juss.) Muell.-Arg] is the main source of natural rubber in the world. However, in the Amazon region, its production is reduced by biotic and abiotic limitations, which have prompted breeding programs in order to identify desirable agronomic and physiological indicators. The objective of this study was to analyze the temporal dynamics of photosynthetic responses based on the parameters of leaf gas exchange and chlorophyll a fluorescence in 10 rubber tree clones during the immature phase (pre-tapping) in three large-scale clone trials, during daily cycles and under two climatic periods (dry and rainy) in the Caquetá region (Colombian Amazon). The variables A, LT, Φ_PSII, ETR and qP were significantly higher in the dry period, where the highest values of PAR, AT and VPD were seen. In San Vicente del Caguán and Florencia, the highest averages were estimated for A, E and g_s, as compared with Belén de los Andaquíes. In Florencia, the highest fluorescence parameters of chlorophyll a were recorded. At 9:00 h and 12:00 h, the highest means of A, E, Φ_PSII and ETR were observed. The majority of the clones displayed the highest F_v/F_m mean (0.82–0.84) in the dry period. The clones FX 4098, FDR 4575, MDF 180, GU198 and FDR 5788 represent genotypes with the best photosynthetic performance (greater photosynthetic rates and better ability of the photosynthetic apparatus to capture, use and dissipate light energy). These desirable genotypes constitute a promising gene pool for expanding the genetic resource of rubber trees in the Colombian Amazon.

Leaf economics and plant hydraulics drive leaf : wood area ratios

Biomass and area ratios between leaves, stems and roots regulate many physiological and ecological processes. The Huber value H_v (sapwood area/leaf area ratio) is central to plant water balance and drought responses. However, its coordination with key plant functional traits is poorly understood, and prevents developing trait-based prediction models. Based on theoretical arguments, we hypothesise that global patterns in H_v of terminal woody branches can be predicted from variables related to plant trait spectra, that is plant hydraulics and size and leaf economics. Using a global compilation of 1135 species-averaged H_v , we show that H_v varies over three orders of magnitude. Higher H_v are seen in short small-leaved low-specific leaf area (SLA) shrubs with low K_s in arid relative to tall large-leaved high-SLA trees with high K_s in moist environments. All traits depend on climate but climatic correlations are stronger for explanatory traits than H_v . Negative isometry is found between H_v and K_s , suggesting a compensation to maintain hydraulic supply to leaves across species. This work identifies the major global drivers of branch sapwood/leaf area ratios. Our approach based on widely available traits facilitates the development of accurate models of above-ground biomass allocation and helps predict vegetation responses to drought.

From the Arctic to the tropics: multibiome prediction of leaf mass per area using leaf reflectance

Leaf mass per area (LMA) is a key plant trait, reflecting tradeoffs between leaf photosynthetic function, longevity, and structural investment. Capturing spatial and temporal variability in LMA has been a long-standing goal of ecological research and is an essential component for advancing Earth system models. Despite the substantial variation in LMA within and across Earth's biomes, an efficient, globally generalizable approach to predict LMA is still lacking. We explored the capacity to predict LMA from leaf spectra across much of the global LMA trait space, with values ranging from 17 to 393 g m^-2 . Our dataset contained leaves from a wide range of biomes from the high Arctic to the tropics, included broad- and needleleaf species, and upper- and lower-canopy (i.e. sun and shade) growth environments. Here we demonstrate the capacity to rapidly estimate LMA using only spectral measurements across a wide range of species, leaf age and canopy position from diverse biomes. Our model captures LMA variability with high accuracy and low error (R²  = 0.89; root mean square error (RMSE) = 15.45 g m^-2 ). Our finding highlights the fact that the leaf economics spectrum is mirrored by the leaf optical spectrum, paving the way for this technology to predict the diversity of LMA in ecosystems across global biomes.

Integrating LiDAR, Multispectral and SAR Data to Estimate and Map Canopy Height in Tropical Forests

Developing accurate methods to map vegetation structure in tropical forests is essential to protect their biodiversity and improve their carbon stock estimation. We integrated LIDAR (Light Detection and Ranging), multispectral and SAR (Synthetic Aperture Radar) data to improve the prediction and mapping of canopy height (CH) at high spatial resolution (30 m) in tropical forests in South America. We modeled and mapped CH estimated from aircraft LiDAR surveys as a ground reference, using annual metrics derived from multispectral and SAR satellite imagery in a dry forest, a moist forest, and a rainforest of tropical South America. We examined the effect of the three forest types, five regression algorithms, and three predictor groups on the modelling and mapping of CH. Our CH models reached errors ranging from 1.2–3.4 m in the dry forest and 5.1–7.4 m in the rainforest and explained variances from 94–60% in the dry forest and 58–12% in the rainforest. Our best models show higher accuracies than previous works in tropical forests. The average accuracy of the five regression algorithms decreased from dry forests (2.6 m +/− 0.7) to moist (5.7 m +/− 0.4) and rainforests (6.6 m +/− 0.7). Random Forest regressions produced the most accurate models in the three forest types (1.2 m +/− 0.05 in the dry, 4.9 m +/− 0.14 in the moist, and 5.5 m +/− 0.3 the rainforest). Model performance varied considerably across the three predictor groups. Our results are useful for CH spatial prediction when GEDI (Global Ecosystem Dynamics Investigation lidar) data become available.

Cryptic phenology in plants: Case studies, implications, and recommendations

Plant phenology-the timing of cyclic or recurrent biological events in plants-offers insight into the ecology, evolution, and seasonality of plant-mediated ecosystem processes. Traditionally studied phenologies are readily apparent, such as flowering events, germination timing, and season-initiating budbreak. However, a broad range of phenologies that are fundamental to the ecology and evolution of plants, and to global biogeochemical cycles and climate change predictions, have been neglected because they are "cryptic"-that is, hidden from view (e.g., root production) or difficult to distinguish and interpret based on common measurements at typical scales of examination (e.g., leaf turnover in evergreen forests). We illustrate how capturing cryptic phenology can advance scientific understanding with two case studies: wood phenology in a deciduous forest of the northeastern USA and leaf phenology in tropical evergreen forests of Amazonia. Drawing on these case studies and other literature, we argue that conceptualizing and characterizing cryptic plant phenology is needed for understanding and accurate prediction at many scales from organisms to ecosystems. We recommend avenues of empirical and modeling research to accelerate discovery of cryptic phenological patterns, to understand their causes and consequences, and to represent these processes in terrestrial biosphere models.

TROPOMI reveals dry-season increase of solar-induced chlorophyll fluorescence in the Amazon forest

Photosynthesis of the Amazon rainforest plays an important role in the regional and global carbon cycles, but, despite considerable in situ and space-based observations, it has been intensely debated whether there is a dry-season increase in greenness and photosynthesis of the moist tropical Amazonian forests. Solar-induced chlorophyll fluorescence (SIF), which is emitted by chlorophyll, has a strong positive linear relationship with photosynthesis at the canopy scale. Recent advancements have allowed us to observe SIF globally with Earth observation satellites. Here we show that forest SIF did not decrease in the early dry season and increased substantially in the late dry season and early part of wet season, using SIF data from the Tropospheric Monitoring Instrument (TROPOMI), which has unprecedented spatial resolution and near-daily global coverage. Using in situ CO₂ eddy flux data, we also show that cloud cover rarely affects photosynthesis at TROPOMI's midday overpass, a time when the forest canopy is most often light-saturated. The observed dry-season increases of forest SIF are not strongly affected by sun-sensor geometry, which was attributed as creating a pseudo dry-season green-up in the surface reflectance data. Our results provide strong evidence that greenness, SIF, and photosynthesis of the tropical Amazonian forest increase during the dry season.

High ecosystem stability of evergreen broadleaf forests under severe droughts

Global increase in drought occurrences threatens the stability of terrestrial ecosystem functioning. Evergreen broadleaf forests (EBFs) keep leaves throughout the year, and therefore could experience higher drought risks than other biomes. However, the recent temporal variability of global vegetation productivity or land carbon sink is mainly driven by non-evergreen ecosystems, such as semiarid grasslands, croplands, and boreal forests. Thus, we hypothesize that EBFs have higher stability than other biomes under the increasingly extreme droughts. Here we use long-term Standardized Precipitation and Evaporation Index (SPEI) data and satellite-derived Enhanced Vegetation Index (EVI) products to quantify the temporal stability (ratio of mean annual EVI to its SD), resistance (ability to maintain its original levels during droughts), and resilience (rate of EVI recovering to pre-drought levels) at biome and global scales. We identified significantly increasing trends of annual drought severity (SPEI range: -0.08 to -1.80), area (areal fraction range: 2%-19%), and duration (month range: 7.9-9.1) in the EBF biome over 2000-2014. However, EBFs showed the highest resistance of EVI to droughts, but no significant differences in resilience of EVI to droughts were found among biomes (forests, grasslands, savannas, and shrublands). Global resistance and resilience of EVI to droughts were largely affected by temperature and solar radiation. These findings suggest that EBFs have higher stability than other biomes despite the greater drought exposure. Thus, the conservation of EBFs is critical for stabilizing global vegetation productivity and land carbon sink under more-intense climate extremes in the future.

Leaf reflectance spectroscopy captures variation in carboxylation capacity across species, canopy environment and leaf age in lowland moist tropical forests

Understanding the pronounced seasonal and spatial variation in leaf carboxylation capacity (V_c,max ) is critical for determining terrestrial carbon cycling in tropical forests. However, an efficient and scalable approach for predicting V_c,max is still lacking. Here the ability of leaf spectroscopy for rapid estimation of V_c,max was tested. V_c,max was estimated using traditional gas exchange methods, and measured reflectance spectra and leaf age in leaves sampled from tropical forests in Panama and Brazil. These data were used to build a model to predict V_c,max from leaf spectra. The results demonstrated that leaf spectroscopy accurately predicts V_c,max of mature leaves in Panamanian tropical forests (R²  = 0.90). However, this single-age model required recalibration when applied to broader leaf demographic classes (i.e. immature leaves). Combined use of spectroscopy models for V_c,max and leaf age enabled construction of the V_c,max -age relationship solely from leaf spectra, which agreed with field observations. This suggests that the spectroscopy technique can capture the seasonal variability in V_c,max , assuming sufficient sampling across diverse species, leaf ages and canopy environments. This finding will aid development of remote sensing approaches that can be used to characterize V_c,max in moist tropical forests and enable an efficient means to parameterize and evaluate terrestrial biosphere models.

Improved estimates of global terrestrial photosynthesis using information on leaf chlorophyll content

The terrestrial biosphere plays a critical role in mitigating climate change by absorbing anthropogenic CO₂ emissions through photosynthesis. The rate of photosynthesis is determined jointly by environmental variables and the intrinsic photosynthetic capacity of plants (i.e. maximum carboxylation rate; Vcmax25 ). A lack of an effective means to derive spatially and temporally explicit Vcmax25 has long hampered efforts towards estimating global photosynthesis accurately. Recent work suggests that leaf chlorophyll content (Chl_leaf ) is strongly related to Vcmax25 , since Chl_leaf and Vcmax25 are both correlated with photosynthetic nitrogen content. We used medium resolution satellite images to derive spatially and temporally explicit Chl_leaf , which we then used to parameterize Vcmax25 within a terrestrial biosphere model. Modelled photosynthesis estimates were evaluated against measured photosynthesis at 124 eddy covariance sites. The inclusion of Chl_leaf in a terrestrial biosphere model improved the spatial and temporal variability of photosynthesis estimates, reducing biases at eddy covariance sites by 8% on average, with the largest improvements occurring for croplands (21% bias reduction) and deciduous forests (15% bias reduction). At the global scale, the inclusion of Chl_leaf reduced terrestrial photosynthesis estimates by 9 PgC/year and improved the correlations with a reconstructed solar-induced fluorescence product and a gridded photosynthesis product upscaled from tower measurements. We found positive impacts of Chl_leaf on modelled photosynthesis for deciduous forests, croplands, grasslands, savannas and wetlands, but mixed impacts for shrublands and evergreen broadleaf forests and negative impacts for evergreen needleleaf forests and mixed forests. Our results highlight the potential of Chl_leaf to reduce the uncertainty of global photosynthesis but identify challenges for incorporating Chl_leaf in future terrestrial biosphere models.

Leaf mechanical strength and photosynthetic capacity vary independently across 57 subtropical forest species with contrasting light requirements

Leaf mechanical strength and photosynthetic capacity are critical plant life-history traits associated with tolerance and growth under various biotic and abiotic stresses. In principle, higher mechanical resistance achieved via higher relative allocation to cell walls should slow photosynthetic rates. However, interspecific relationships among these two leaf functions have not been reported. We measured leaf traits of 57 dominant woody species in a subtropical evergreen forest in China, focusing especially on photosynthetic rates, mechanical properties, and leaf lifespan (LLS). These species were assigned to two ecological strategy groups: shade-tolerant species and light-demanding species. On average, shade-tolerant species had longer LLS, higher leaf mechanical strength but lower photosynthetic rates, and exhibited longer LLS for a given leaf mass per area (LMA) or mechanical strength than light-demanding species. Depending on the traits and the basis of expression (per area or per mass), leaf mechanical resistance and photosynthetic capacity were either deemed unrelated, or only weakly negatively correlated. We found only weak support for the proposed trade-off between leaf biomechanics and photosynthesis among co-occurring woody species. This suggests there is considerable flexibility in these properties, and the observed relationships may result more so from trait coordination than any physically or physiologically enforced trade-off.

Phenology and Seasonal Ecosystem Productivity in an Amazonian Floodplain Forest

Several studies have explored the linkages between phenology and ecosystem productivity across the Amazon basin. However, few studies have focused on flooded forests, which correspond to c.a. 14% of the basin. In this study, we assessed the seasonality of ecosystem productivity (gross primary productivity, GPP) from eddy covariance measurements, environmental drivers and phenological patterns obtained from the field (leaf litter mass) and satellite measurements (enhanced vegetation index (EVI) from the Moderate Resolution Imaging Spectroradiometer/multi-angle implementation correction (MODIS/MAIAC)) in an Amazonian floodplain forest. We found that ecosystem productivity is limited by soil moisture in two different ways. During the flooded period, the excess of water limits GPP (Spearman’s correlation; rho = −0.22), while during non-flooded months, GPP is positively associated with soil moisture (rho = 0.34). However, GPP is maximized when cumulative water deficit (CWD) increases (rho = 0.81), indicating that GPP is dependent on the amount of water available. EVI was positively associated with leaf litter mass (Pearson’s correlation; r = 0.55) and with GPP (r = 0.50), suggesting a coupling between new leaf production and the phenology of photosynthetic capacity, decreasing both at the peak of the flooded period and at the end of the dry season. EVI was able to describe the inter-annual variations on forest responses to environmental drivers, which have changed during an observed El Niño-Southern Oscillation (ENSO) year (2015/2016).

Quantifying Leaf Phenology of Individual Trees and Species in a Tropical Forest Using Unmanned Aerial Vehicle (UAV) Images

Tropical forests exhibit complex but poorly understood patterns of leaf phenology. Understanding species- and individual-level phenological patterns in tropical forests requires datasets covering large numbers of trees, which can be provided by Unmanned Aerial Vehicles (UAVs). In this paper, we test a workflow combining high-resolution RGB images (7 cm/pixel) acquired from UAVs with a machine learning algorithm to monitor tree and species leaf phenology in a tropical forest in Panama. We acquired images for 34 flight dates over a 12-month period. Crown boundaries were digitized in images and linked with forest inventory data to identify species. We evaluated predictions of leaf cover from different models that included up to 14 image features extracted for each crown on each date. The models were trained and tested with visual estimates of leaf cover from 2422 images from 85 crowns belonging to eight species spanning a range of phenological patterns. The best-performing model included both standard color metrics, as well as texture metrics that quantify within-crown variation, with r2 of 0.84 and mean absolute error (MAE) of 7.8% in 10-fold cross-validation. In contrast, the model based only on the widely-used Green Chromatic Coordinate (GCC) index performed relatively poorly (r2 = 0.52, MAE = 13.6%). These results highlight the utility of texture features for image analysis of tropical forest canopies, where illumination changes may diminish the utility of color indices, such as GCC. The algorithm successfully predicted both individual-tree and species patterns, with mean r2 of 0.82 and 0.89 and mean MAE of 8.1% and 6.0% for individual- and species-level analyses, respectively. Our study is the first to develop and test methods for landscape-scale UAV monitoring of individual trees and species in diverse tropical forests. Our analyses revealed undescribed patterns of high intraspecific variation and complex leaf cover changes for some species.

Measuring Vegetation Phenology with Near-Surface Remote Sensing in a Temperate Deciduous Forest: Effects of Sensor Type and Deployment

Near-surface remote sensing is an effective tool for in situ monitoring of canopy phenology, but the uncertainties involved in sensor-types and their deployments are rarely explored. We comprehensively compared three types of sensor (i.e., digital camera, spectroradiometer, and routine radiometer) at different inclination- and azimuth-angles in monitoring canopy phenology of a temperate deciduous forest in Northeast China for three years. The results showed that the greater contribution of understory advanced the middle of spring (MOS) for large inclination-angle of camera and spectroradiometer. The length of growing season estimated by camera from the east direction extended 11 d than that from the north direction in 2015 due to the spatial heterogeneity, but there was no significant difference in 2016 and 2018.The difference infield of view of sensors caused the MOS and the middle of fall, estimated by camera, to lag a week behind those by spectroradiometer and routine radiometer. Overall, the effect of azimuth-angle was greater than that of inclination-angle or sensor-type. Our assessments of the sensor types and their deployments are critical for the long-term accurate monitoring of phenology at the site scale and the regional/global-integration of canopy phenology data.

Seasonal and drought-related changes in leaf area profiles depend on height and light environment in an Amazon forest

Seasonal dynamics in the vertical distribution of leaf area index (LAI) may impact the seasonality of forest productivity in Amazonian forests. However, until recently, fine-scale observations critical to revealing ecological mechanisms underlying these changes have been lacking. To investigate fine-scale variation in leaf area with seasonality and drought we conducted monthly ground-based LiDAR surveys over 4 yr at an Amazon forest site. We analysed temporal changes in vertically structured LAI along axes of both canopy height and light environments. Upper canopy LAI increased during the dry season, whereas lower canopy LAI decreased. The low canopy decrease was driven by highly illuminated leaves of smaller trees in gaps. By contrast, understory LAI increased concurrently with the upper canopy. Hence, tree phenological strategies were stratified by height and light environments. Trends were amplified during a 2015-2016 severe El Niño drought. Leaf area low in the canopy exhibited behaviour consistent with water limitation. Leaf loss from short trees in high light during drought may be associated with strategies to tolerate limited access to deep soil water and stressful leaf environments. Vertically and environmentally structured phenological processes suggest a critical role of canopy structural heterogeneity in seasonal changes in Amazon ecosystem function.