► The sensitivity of vegetation phenology to climate change varies among biomes. ► Key weaknesses in our current understanding of phenology drivers are identified. ► Phenology controls many feedbacks ...of vegetation to the climate system. ► The size and seasonality of these feedbacks will shift as phenology shifts. ► Models that couple the land surface to the climate system need better phenology.
Vegetation phenology is highly sensitive to climate change. Phenology also controls many feedbacks of vegetation to the climate system by influencing the seasonality of albedo, surface roughness length, canopy conductance, and fluxes of water, energy, CO2 and biogenic volatile organic compounds. In this review, we first discuss the environmental drivers of phenology, and the impacts of climate change on phenology, in different biomes. We then examine the vegetation-climate feedbacks that are mediated by phenology, and assess the potential impact on these feedbacks of shifts in phenology driven by climate change. We finish with an overview of phenological modeling and we suggest ways in which models might be improved using existing data sets. Several key weaknesses in our current understanding emerge from this analysis. First, we need a better understanding of the drivers of phenology, particularly in under-studied biomes (e.g. tropical forests). We do not have a mechanistic understanding of the role of photoperiod, even in well-studied biomes. In all biomes, the factors controlling senescence and dormancy are not well-documented. Second, for the most part (i.e. with the exception of phenology impacts on CO2 exchange) we have only a qualitative understanding of the feedbacks between vegetation and climate that are mediated by phenology. We need to quantify the magnitude of these feedbacks, and ensure that they are accurately reproduced by models. Third, we need to work towards a new understanding of phenological processes that enables progress beyond the modeling paradigms currently in use. Accurate representation of phenological processes in models that couple the land surface to the climate system is particularly important, especially when such models are being used to predict future climate.
Abstract
Global vegetation and associated ecosystem services critically depend on soil moisture availability which has decreased in many regions during the last three decades. While spatial patterns ...of vegetation sensitivity to global soil water have been recently investigated, long-term changes in vegetation sensitivity to soil water availability are still unclear. Here we assess global vegetation sensitivity to soil moisture during 1982-2017 by applying explainable machine learning with observation-based leaf area index (LAI) and hydro-climate anomaly data. We show that LAI sensitivity to soil moisture significantly increases in many semi-arid and arid regions. LAI sensitivity trends are associated with multiple hydro-climate and ecological variables, and strongest increasing trends occur in the most water-sensitive regions which additionally experience declining precipitation. State-of-the-art land surface models do not reproduce this increasing sensitivity as they misrepresent water-sensitive regions and sensitivity strength. Our sensitivity results imply an increasing ecosystem vulnerability to water availability which can lead to exacerbated reductions in vegetation carbon uptake under future intensified drought, consequently amplifying climate change.
Quantifying global photosynthesis remains a challenge due to a lack of accurate remote sensing proxies. Solar-induced chlorophyll fluorescence (SIF) has been shown to be a good indicator of ...photosynthetic activity across various spatial scales. However, a global and spatially challenging estimate of terrestrial gross primary production (GPP) based on satellite SIF remains unresolved due to the confounding effects of species-specific physical and physiological traits and external factors, such as canopy structure or photosynthetic pathway (C3 or C4). Here we analyze an ensemble of far-red SIF data from OCO-2 satellite and ground observations at multiple sites, using the spectral invariant theory to reduce the effects of canopy structure and to retrieve a structure-corrected total canopy SIF emission (SIFtotal). We find that the relationships between observed canopy-leaving SIF and ecosystem GPP vary significantly among biomes. In contrast, the relationships between SIFtotal and GPP converge around two unique models, one for C3 and one for C4 plants. We show that the two single empirical models can be used to globally scale satellite SIF observations to terrestrial GPP. We obtain an independent estimate of global terrestrial GPP of 129.56 ± 6.54 PgC/year for the 2015–2017 period, which is consistent with the state-of-the-art data- and process-oriented models. The new GPP product shows improved sensitivity to previously undetected ‘hotspots’ of productivity, being able to resolve the double-peak in GPP due to rotational cropping systems. We suggest that the direct scheme to estimate GPP presented here, which is based on satellite SIF, may open up new possibilities to resolve the dynamics of global terrestrial GPP across space and time.
•An ensemble of far-red SIF from ground and OCO-2 was compared with in situ GPP.•BRF data can be used to reduce the effects of canopy structure on SIF.•BRF data is used to derive total canopy SIF emission (SIFtotal) for OCO-2.•SIFtotal and GPP relationships converge two unique models for C3 and C4 plants.•SIFtotal-based model yields an estimate of GPP of 129.56 PgC/year for 2015–2017.
We present the R package bigleaf (version 0.6.5), an open source toolset for the derivation of meteorological, aerodynamic, and physiological ecosystem properties from eddy covariance (EC) flux ...observations and concurrent meteorological measurements. A 'big-leaf' framework, in which vegetation is represented as a single, uniform layer, is employed to infer bulk ecosystem characteristics top-down from the measured fluxes. Central to the package is the calculation of a bulk surface/canopy conductance (Gs/Gc) and a bulk aerodynamic conductance (Ga), with the latter including formulations for the turbulent and canopy boundary layer components. The derivation of physical land surface characteristics such as surface roughness parameters, wind profile, aerodynamic and radiometric surface temperature, surface vapor pressure deficit (VPD), potential evapotranspiration (ET), imposed and equilibrium ET, as well as vegetation-atmosphere decoupling coefficients, is described. The package further provides calculation routines for physiological ecosytem properties (stomatal slope parameters, stomatal sensitivity to VPD, bulk intercellular CO2 concentration, canopy photosynthetic capacity), energy balance characteristics (closure, biochemical energy), ancillary meteorological variables (psychrometric constant, saturation vapor pressure, air density, etc.), customary unit interconversions and data filtering. The target variables can be calculated with a different degree of complexity, depending on the amount of available site-specific information. The utilities of the package are demonstrated for three single-level (above-canopy) eddy covariance sites representing a temperate grassland, a temperate needle-leaf forest, and a Mediterranean evergreen broadleaf forest. The routines are further tested for a two-level EC site (tree and grass layer) located in a Mediterranean oak savanna. The limitations and the ecophysiological interpretation of the derived ecosystem properties are discussed and practical guidelines are given. The package provides the basis for a consistent, physically sound, and reproducible characterization of biometeorological conditions and ecosystem physiology, and is applicable to EC sites across vegetation types and climatic conditions with minimal ancillary data requirements.
Background
Information on the spatial distribution of aboveground biomass (AGB) over large areas is needed for understanding and managing processes involved in the carbon cycle and supporting ...international policies for climate change mitigation and adaption. Furthermore, these products provide important baseline data for the development of sustainable management strategies to local stakeholders. The use of remote sensing data can provide spatially explicit information of AGB from local to global scales. In this study, we mapped national Mexican forest AGB using satellite remote sensing data and a machine learning approach. We modelled AGB using two scenarios: (1) extensive national forest inventory (NFI), and (2) airborne Light Detection and Ranging (LiDAR) as reference data. Finally, we propagated uncertainties from field measurements to LiDAR-derived AGB and to the national wall-to-wall forest AGB map.
Results
The estimated AGB maps (NFI- and LiDAR-calibrated) showed similar goodness-of-fit statistics (R
2
, Root Mean Square Error (RMSE)) at three different scales compared to the independent validation data set. We observed different spatial patterns of AGB in tropical dense forests, where no or limited number of NFI data were available, with higher AGB values in the LiDAR-calibrated map. We estimated much higher uncertainties in the AGB maps based on two-stage up-scaling method (i.e., from field measurements to LiDAR and from LiDAR-based estimates to satellite imagery) compared to the traditional field to satellite up-scaling. By removing LiDAR-based AGB pixels with high uncertainties, it was possible to estimate national forest AGB with similar uncertainties as calibrated with NFI data only.
Conclusions
Since LiDAR data can be acquired much faster and for much larger areas compared to field inventory data, LiDAR is attractive for repetitive large scale AGB mapping. In this study, we showed that two-stage up-scaling methods for AGB estimation over large areas need to be analyzed and validated with great care. The uncertainties in the LiDAR-estimated AGB propagate further in the wall-to-wall map and can be up to 150%. Thus, when a two-stage up-scaling method is applied, it is crucial to characterize the uncertainties at all stages in order to generate robust results. Considering the findings mentioned above LiDAR can be used as an extension to NFI for example for areas that are difficult or not possible to access.
•Leaf chlorophyll (Chl) can be retrieved from remote sensing (Landsat) data.•Semi-mechanistic relationships are derived between Chl and photosynthetic capacity.•Chl is embedded within the ...photosynthesis scheme of the Community Land Model.•Results supports the use of Chl as a proxy for photosynthetic capacity.•Results advocate the potential of Chl for constraining model simulations of GPP.
This study investigates the utility of in situ and satellite-based leaf chlorophyll (Chl) estimates for quantifying leaf photosynthetic capacity and for constraining model simulations of Gross Primary Productivity (GPP) over a corn field in Maryland, U.S.A. The maximum rate of carboxylation (Vmax) represents a key control on leaf photosynthesis within the widely employed C3 and C4 photosynthesis models proposed by Farquhar et al. (1980) and Collatz et al. (1992), respectively. A semi-mechanistic relationship between Vmax25 (Vmax normalized to 25°C) and Chl is derived based on interlinkages between Vmax25, Rubisco enzyme kinetics, leaf nitrogen, and Chl reported in the experimental literature. The resulting linear Vmax25−Chl relationship is embedded within the photosynthesis scheme of the Community Land Model (CLM), thereby bypassing the use of fixed plant functional type (PFT) specific Vmax25 values. The effect of the updated parameterization on simulated carbon fluxes is tested over a corn field growing season using: (1) a detailed Chl time-series established on the basis of intensive field measurements and (2) Chl estimates derived from Landsat imagery using the REGularized canopy reFLECtance (REGFLEC) tool. Validations against flux tower observations demonstrate benefit of using Chl to parameterize Vmax25 to account for variations in nitrogen availability imposed by severe environmental conditions. The use of Vmax25 that varied seasonally as a function of satellite-based Chl, rather than a fixed PFT-specific value, significantly improved the agreement with observed tower fluxes with Pearson's correlation coefficient (r) increasing from 0.88 to 0.93 and the root-mean-square-deviation decreasing from 4.77 to 3.48μmolm−2s−1. The results support the use of Chl as a proxy for photosynthetic capacity using generalized relationships between Vmax25 and Chl, and advocate the potential of satellite retrieved Chl for constraining simulations of GPP in space and time.
•Phenopix is a new R package for phenology from digital images of vegetation.•It contains the most up-to-date processing techniques along with novelties.•Novelties include the combination of ...different fit/phenophase extraction methods.•Pixel-by-pixel analysis allows for the extraction of spatially explicit phenology.•The software is open source and freely available at the R-forge website.
In this paper we extensively describe new software available as a R package that allows for the extraction of phenological information from time-lapse digital photography of vegetation cover. The phenopix R package includes all steps in data processing. It enables the user to: draw a region of interest (ROI) on an image; extract red green and blue digital numbers (DN) from a seasonal series of images; depict greenness index trajectories; fit a curve to the seasonal trajectories; extract relevant phenological thresholds (phenophases); extract phenophase uncertainties.
The software capabilities are illustrated by analyzing one year of data from a selection of seven sites belonging to the PhenoCam network (http://phenocam.sr.unh.edu/), including an unmanaged subalpine grassland, a tropical grassland, a deciduous needle-leaf forest, three deciduous broad-leaf temperate forests and an evergreen needle-leaf forest. One of the novelties introduced by the package is the spatially explicit, pixel-based analysis, which potentially allows to extract within-ecosystem or within-individual variability of phenology. We examine the relationship between phenophases extracted by the traditional ROI-averaged and the novel pixel-based approaches, and further illustrate potential applications of pixel-based image analysis available in the phenopix R package.
The global land surface absorbs about a third of anthropogenic emissions each year, due to the difference between two key processes: ecosystem photosynthesis and respiration. Despite the importance ...of these two processes, it is not possible to measure either at the ecosystem scale during the daytime. Eddy-covariance measurements are widely used as the closest 'quasi-direct' ecosystem-scale observation from which to estimate ecosystem photosynthesis and respiration. Recent research, however, suggests that current estimates may be biased by up to 25%, due to a previously unaccounted for process: the inhibition of leaf respiration in the light. Yet the extent of inhibition remains debated, and implications for estimates of ecosystem-scale respiration and photosynthesis remain unquantified. Here, we quantify an apparent inhibition of daytime ecosystem respiration across the global FLUXNET eddy-covariance network and identify a pervasive influence that varies by season and ecosystem type. We develop partitioning methods that can detect an apparent ecosystem-scale inhibition of daytime respiration and find that diurnal patterns of ecosystem respiration might be markedly different than previously thought. The results call for the re-evaluation of global terrestrial carbon cycle models and also suggest that current global estimates of photosynthesis and respiration may be biased, some on the order of magnitude of anthropogenic fossil fuel emissions.
We use eddy covariance measurements of net ecosystem productivity (NEP) from 21 FLUXNET sites (153 site-years of data) to investigate relationships between phenology and productivity (in terms of ...both NEP and gross ecosystem photosynthesis, GEP) in temperate and boreal forests. Results are used to evaluate the plausibility of four different conceptual models. Phenological indicators were derived from the eddy covariance time series, and from remote sensing and models. We examine spatial patterns (across sites) and temporal patterns (across years); an important conclusion is that it is likely that neither of these accurately represents how productivity will respond to future phenological shifts resulting from ongoing climate change. In spring and autumn, increased GEP resulting from an ‘extra’ day tends to be offset by concurrent, but smaller, increases in ecosystem respiration, and thus the effect on NEP is still positive. Spring productivity anomalies appear to have carry-over effects that translate to productivity anomalies in the following autumn, but it is not clear that these result directly from phenological anomalies. Finally, the productivity of evergreen needleleaf forests is less sensitive to phenology than is productivity of deciduous broadleaf forests. This has implications for how climate change may drive shifts in competition within mixed-species stands.
Abstract
The response of vegetation physiology to drought at large spatial scales is poorly understood due to a lack of direct observations. Here, we study vegetation drought responses related to ...photosynthesis, evaporation, and vegetation water content using remotely sensed data, and we isolate physiological responses using a machine learning technique. We find that vegetation functional decreases are largely driven by the downregulation of vegetation physiology such as stomatal conductance and light use efficiency, with the strongest downregulation in water-limited regions. Vegetation physiological decreases in wet regions also result in a discrepancy between functional and structural changes under severe drought. We find similar patterns of physiological drought response using simulations from a soil–plant–atmosphere continuum model coupled with a radiative transfer model. Observation-derived vegetation physiological responses to drought across space are mainly controlled by aridity and additionally modulated by abnormal hydro-meteorological conditions and vegetation types. Hence, isolating and quantifying vegetation physiological responses to drought enables a better understanding of ecosystem biogeochemical and biophysical feedback in modulating climate change.
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