Mid‐to‐high latitude forests play an important role in the terrestrial carbon cycle, but the representation of photosynthesis in boreal forests by current modelling and observational methods is still ...challenging. In particular, the applicability of existing satellite‐based proxies of greenness to indicate photosynthetic activity is hindered by small annual changes in green biomass of the often evergreen tree population and by the confounding effects of background materials such as snow. As an alternative, satellite measurements of sun‐induced chlorophyll fluorescence (SIF) can be used as a direct proxy of photosynthetic activity. In this study, the start and end of the photosynthetically active season of the main boreal forests are analysed using spaceborne SIF measurements retrieved from the GOME‐2 instrument and compared to that of green biomass, proxied by vegetation indices including the Enhanced Vegetation Index (EVI) derived from MODIS data. We find that photosynthesis and greenness show a similar seasonality in deciduous forests. In high‐latitude evergreen needleleaf forests, however, the length of the photosynthetically active period indicated by SIF is up to 6 weeks longer than the green biomass changing period proxied by EVI, with SIF showing a start‐of‐season of approximately 1 month earlier than EVI. On average, the photosynthetic spring recovery as signalled by SIF occurs as soon as air temperatures exceed the freezing point (2–3 °C) and when the snow on the ground has not yet completely melted. These findings are supported by model data of gross primary production and a number of other studies which evaluated in situ observations of CO2 fluxes, meteorology and the physiological state of the needles. Our results demonstrate the sensitivity of space‐based SIF measurements to light‐use efficiency of boreal forests and their potential for an unbiased detection of photosynthetic activity even under the challenging conditions interposed by evergreen boreal ecosystems.
Two new remote sensing vegetation parameters derived from spaceborne spectrometers and simulated with a three dimensional radiative transfer model have been evaluated in terms of their prospects and ...drawbacks for the monitoring of dense vegetation canopies: (i) sun-induced chlorophyll fluorescence (SIF), a unique signal emitted by photosynthetically active vegetation and (ii) the canopy scattering coefficient (CSC), a vegetation parameter derived along with the directional area scattering factor (DASF) and expected to be particularly sensitive to leaf optical properties. Here, we present the first global data set of DASF/CSC and examine the potential of CSC and SIF for providing complementary information on the controversially discussed vegetation seasonality in Amazon forests. A comparison between near-infrared SIF derived from the Global Ozone Monitoring Experiment-2 (GOME-2) instrument and the Orbiting Carbon Observatory-2 (OCO-2) (overpass time in the morning and noon, respectively) reveals the response of SIF to instantaneous photosynthetically active radiation (PAR). Large-scale seasonal swings of GOME-2 SIF amount up to 21% (regarding the annual maximum) and peak in October and around February, while OCO-2 SIF peaks in February. However, both time series agree very well if SIF is normalized by overpass time and wavelength. We further examine anistropic reflectance characteristics with the finding that the hot spot effect significantly impacts observed GOME-2 SIF values. On the contrary, our sensitivity analysis suggests that CSC is highly independent of sun-sensor geometry as well as atmospheric effects. The slight annual variability of CSC (3%) shows no clear seasonal cycle, while a relatively high spatial standard deviation points to a high degree of spatial heterogeneity in our study domain within the central Amazon Basin.
•Very good agreement between normalized SIF from OCO-2 and GOME-2.•SIF shows a similar but less pronounced hot spot effect compared to NIR reflectance.•Canopy scattering coefficient (CSC) seems to reverse the usual saturation effect.•CSC is highly independent of directional and atmospheric effects.
Interannual variations in ecosystem primary productivity are dominated by water availability. Until recently, characterizing the photosynthetic response of different ecosystems to soil moisture ...anomalies was hampered by observational limitations. Here, we use a number of satellite‐based proxies for productivity, including spectral indices, sun‐induced chlorophyll fluorescence, and data‐driven estimates of gross primary production, to reevaluate the relationship between terrestrial photosynthesis and water. In contrast to nonwoody vegetation, we find a resilience of forested ecosystems to reduced soil moisture. Sun‐induced chlorophyll fluorescence and data‐driven gross primary production indicate an increase in photosynthesis as a result of the accompanying higher amounts of light and temperature despite lowered light‐use‐efficiency. Conversely, remote sensing indicators of greenness reach their detection limit and largely remain stable. Our study thus highlights the differential responses of ecosystems along a tree cover gradient and illustrates the importance of differentiating photosynthesis indicators from those of greenness for the monitoring and understanding of ecosystems.
Plain Language Summary
The capacity of vegetation to thrive and to sequester carbon depends on how much water they can have access to. In this work, we evaluate how different types of satellite observations can describe the response of vegetation to changes in soil moisture over the entire planet. The first source of observation measures only the greenness of the land surface, the second measures light that is emitted by pigments in plants which are photosynthetically active (chlorophyll fluorescence), and the third are simulations of gross carbon uptake derived from machine learning techniques. For periods of water shortage all three indicate a reduction of growth in ecosystems with few trees. However, in cold boreal forests, when soil moisture is particularly low, we still detect an increase in photosynthesis due to higher light and temperature conditions, but this is not reflected in the greenness indicator. This work illustrates how lack of water is not necessarily harmful for catching carbon through photosynthesis, but to monitor this effect, we need remote sensing indicators that measure more than just how green the plants are, and fluorescence is likely a good candidate.
Key Points
Satellite‐based plant greenness, chlorophyll fluorescence, and gross photosynthesis are evaluated with respect to soil moisture fluctuations
Ecosystem responses to water availability change sign along a tree cover gradient
We show spaceborne observations of light‐ and temperature‐enhanced forest photosynthesis in times of reduced soil moisture at constant greenness
The CO
Human Emissions project has generated realistic high-resolution 9 km global simulations for atmospheric carbon tracers referred to as nature runs to foster carbon-cycle research applications ...with current and planned satellite missions, as well as the surge of in situ observations. Realistic atmospheric CO
, CH
and CO fields can provide a reference for assessing the impact of proposed designs of new satellites and in situ networks and to study atmospheric variability of the tracers modulated by the weather. The simulations spanning 2015 are based on the Copernicus Atmosphere Monitoring Service forecasts at the European Centre for Medium Range Weather Forecasts, with improvements in various model components and input data such as anthropogenic emissions, in preparation of a CO
Monitoring and Verification Support system. The relative contribution of different emissions and natural fluxes towards observed atmospheric variability is diagnosed by additional tagged tracers in the simulations. The evaluation of such high-resolution model simulations can be used to identify model deficiencies and guide further model improvements.
The productivity of terrestrial vegetation is determined by multiple land surface and atmospheric drivers. Water availability is critical for vegetation productivity, but the role of vertical ...variability of soil moisture (SM) is largely unknown. Here, we analyze dominant controls of global vegetation productivity represented by sun‐induced fluorescence and spectral vegetation indices at the half‐monthly time scale. We apply random forests to predict vegetation productivity from several hydrometeorological variables including multi‐layer SM and quantify the variable importance. Dominant hydrometeorological controls generally vary with latitudes: temperature in higher latitudes, solar radiation in lower latitudes, and root‐zone SM in between. We find that including vertically resolved SM allows a better understanding of vegetation productivity and reveals extended water‐related control. The deep(er) SM control for semi‐arid grasses and shrubs illustrates the potential of deep(er) rooting systems to adapt to water limitation. This study highlights the potential to infer sub‐surface processes from remote sensing observations.
Key Points
Vertically resolved soil moisture (SM) improves the understanding of large‐scale vegetation productivity and yields extended water‐related controls
Data‐driven evidence for the meaningfulness of the long‐standing modeling paradigm of vertical soil layer discretization
Comparatively deep SM is most relevant in semi‐arid areas and for grasses and shrubs
Empirical vegetation indices derived from spectral reflectance data are widely used in remote sensing of the biosphere, as they represent robust proxies for canopy structure, leaf pigment content, ...and, subsequently, plant photosynthetic potential. Here, we generalize the broad family of commonly used vegetation indices by exploiting all higher-order relations between the spectral channels involved. This results in a higher sensitivity to vegetation biophysical and physiological parameters. The presented nonlinear generalization of the celebrated normalized difference vegetation index (NDVI) consistently improves accuracy in monitoring key parameters, such as leaf area index, gross primary productivity, and sun-induced chlorophyll fluorescence. Results suggest that the statistical approach maximally exploits the spectral information and addresses long-standing problems in satellite Earth Observation of the terrestrial biosphere. The nonlinear NDVI will allow more accurate measures of terrestrial carbon source/sink dynamics and potentials for stabilizing atmospheric CO
and mitigating global climate change.
•An approach considering data representativeness for model selection is provided.•CO2 fertilization cannot be neglected in global GPP modeling.•Light saturation and diffuse fraction affect GPP across ...various ecosystems.•Effects of temperature and soil moisture are lagged in cold and arid regions.•Temperature and soil moisture dominate GPP in cold and arid environments.
The sensitivity of photosynthesis to environmental changes is essential for understanding carbon cycle responses to global climate change and for the development of modeling approaches that explains its spatial and temporal variability. We collected a large variety of published sensitivity functions of gross primary productivity (GPP) to different forcing variables to assess the response of GPP to environmental factors. These include the responses of GPP to temperature; vapor pressure deficit, some of which include the response to atmospheric CO2 concentrations; soil water availability (W); light intensity; and cloudiness. These functions were combined in a full factorial light use efficiency (LUE) model structure, leading to a collection of 5600 distinct LUE models. Each model was optimized against daily GPP and evapotranspiration fluxes from 196 FLUXNET sites and ranked across sites based on a bootstrap approach. The GPP sensitivity to each environmental factor, including CO2 fertilization, was shown to be significant, and that none of the previously published model structures performed as well as the best model selected. From daily and weekly to monthly scales, the best model's median Nash-Sutcliffe model efficiency across sites was 0.73, 0.79 and 0.82, respectively, but poorer at annual scales (0.23), emphasizing the common limitation of current models in describing the interannual variability of GPP. Although the best global model did not match the local best model at each site, the selection was robust across ecosystem types. The contribution of light saturation and cloudiness to GPP was observed across all biomes (from 23% to 43%). Temperature and W dominates GPP and LUE but responses of GPP to temperature and W are lagged in cold and arid ecosystems, respectively. The findings of this study provide a foundation towards more robust LUE-based estimates of global GPP and may provide a benchmark for other empirical GPP products.
Much uncertainty remains in measuring the inter‐annual and longer‐term dynamics of vegetation gross and net primary productivity (GPP, NPP) and the connected land carbon sink. Potential for better ...GPP estimation lies in newer satellite products representing different processes or vegetation states, but how they capture interannual GPP dynamics remains to be explored. Here, we differentiate shorter‐ and longer‐term vegetation dynamics and their drivers for several Earth‐observation‐based vegetation proxies and a GPP estimate using time series decomposition. We find that relations between proxies can significantly differ between time scales, along land cover and climate gradients. For GPP estimated at FLUXNET sites, seasonal and multiannual slopes differ by at least 25% for half of the cases investigated, indicating considerable mismatch if multiannual relations were derived from seasonal slopes. Considering time‐scale‐specific sensitivities between proxies of vegetation productivity may improve estimates of interannual variability in vegetation productivity.
Plain Language Summary
How ecosystems will develop in the future is still difficult to predict, in part because the factors that influence ecosystems over several years or decades may differ from those influencing them in the course of a day or a year. Several satellites monitor vegetation growth on Earth and report, for example, leaf greenness or fluorescence. Fluorescence is often superior in tracking seasonal vegetation dynamics, but when it comes to understanding the long‐term imprint of climate, it remains to be clarified how different data products relate to each other. In this study, we compare how much changes in interannual and longer vegetation activity are captured by different satellite products. We find that the relationship between satellite proxies for vegetation differs between monthly, yearly and long‐term scales. These findings may help to better understand and predict vegetation growth and carbon uptake from the atmosphere in the future.
Key Points
Contrary to seasonal time scales, it is poorly understood how different vegetation productivity proxies relate at interannual time scales
Interannual relations between vegetation proxies need to be considered: Seasonal relations do not generally hold at multiannual scale
Time‐scale‐specific slopes between vegetation proxies vary along gradients of tree cover, vegetation type, climate, and across FLUXNET sites
Global collections of synthesized flux tower data such as FLUXNET have accelerated scientific progress beyond the eddy covariance community. However, remaining data issues in FLUXNET data pose ...challenges for users, particularly for multi-site synthesis and modelling activities.
Sun-induced chlorophyll fluorescence (SIF) retrieved from satellite spectrometers can be a highly valuable proxy for photosynthesis. The SIF signal is very small and notoriously difficult to measure, ...requiring sub-nanometre spectral-resolution measurements, which to date are only available from atmospheric spectrometers sampling at low spatial resolution. For example, the widely used SIF dataset derived from the GOME-2 mission is typically provided in 0.5∘ composites. This paper presents a new SIF dataset based on GOME-2 satellite observations with an enhanced spatial resolution of 0.05∘ and an 8 d time step covering the period 2007–2018. It leverages on a proven methodology that relies on using a light-use efficiency (LUE) modelling approach to establish a semi-empirical relationship between SIF and various explanatory variables derived from remote sensing at higher spatial resolution. An optimal set of explanatory variables is selected based on an independent validation with OCO-2 SIF observations, which are only sparsely available but have a high accuracy and spatial resolution. After bias correction, the resulting downscaled SIF data show high spatio-temporal agreement with the first SIF retrievals from the new TROPOMI mission, opening the path towards establishing a surrogate archive for this promising new dataset. We foresee this new SIF dataset becoming a valuable asset for Earth system science in general and for monitoring vegetation productivity in particular. The dataset is available at https://doi.org/10.2905/21935FFC-B797-4BEE-94DA-8FEC85B3F9E1
(Duveiller et al., 2019).
PDF
