•Seven light use efficiency models were compared at global eddy covariance towers.•Performance of seven models differed substantially among ecosystem types.•It is needed to improve LUE models by ...integrating impacts of diffuse radiation and reliable water stress equations.
Simulating gross primary productivity (GPP) of terrestrial ecosystems has been a major challenge in quantifying the global carbon cycle. Many different light use efficiency (LUE) models have been developed recently, but our understanding of the relative merits of different models remains limited. Using CO2 flux measurements from multiple eddy covariance sites, we here compared and assessed major algorithms and performance of seven LUE models (CASA, CFix, CFlux, EC-LUE, MODIS, VPM and VPRM). Comparison between simulated GPP and estimated GPP from flux measurements showed that model performance differed substantially among ecosystem types. In general, most models performed better in capturing the temporal changes and magnitude of GPP in deciduous broadleaf forests and mixed forests than in evergreen broadleaf forests and shrublands. Six of the seven LUE models significantly underestimated GPP during cloudy days because the impacts of diffuse radiation on light use efficiency were ignored in the models. CFlux and EC-LUE exhibited the lowest root mean square error among all models at 80% and 75% of the sites, respectively. Moreover, these two models showed better performance than others in simulating interannual variability of GPP. Two pairwise comparisons revealed that the seven models differed substantially in algorithms describing the environmental regulations, particularly water stress, on GPP. This analysis highlights the need to improve representation of the impacts of diffuse radiation and water stress in the LUE models.
Northern hemisphere evergreen forests assimilate a significant fraction of global atmospheric CO₂ but monitoring large-scale changes in gross primary production (GPP) in these systems is challenging. ...Recent advances in remote sensing allow the detection of solar-induced chlorophyll fluorescence (SIF) emission from vegetation, which has been empirically linked to GPP at large spatial scales. This is particularly important in evergreen forests, where traditional remote-sensing techniques and terrestrial biosphere models fail to reproduce the seasonality of GPP. Here, we examined the mechanistic relationship between SIF retrieved from a canopy spectrometer system and GPP at a winter-dormant conifer forest, which has little seasonal variation in canopy structure, needle chlorophyll content, and absorbed light. Both SIF and GPP track each other in a consistent, dynamic fashion in response to environmental conditions. SIF and GPP are well correlated (R² = 0.62–0.92) with an invariant slope over hourly to weekly timescales. Large seasonal variations in SIF yield capture changes in photoprotective pigments and photosystem II operating efficiency associated with winter acclimation, highlighting its unique ability to precisely track the seasonality of photosynthesis. Our results underscore the potential of new satellite-based SIF products (TROPOMI, OCO-2) as proxies for the timing and magnitude of GPP in evergreen forests at an unprecedented spatiotemporal resolution.
Recent successes in passive remote sensing of far-red solar-induced chlorophyll fluorescence (SIF) have spurred the development and integration of
canopy-level fluorescence models in global ...terrestrial biosphere models (TBMs) for climate and carbon cycle research. The interaction of fluorescence
with photochemistry at the leaf and canopy scales provides opportunities to diagnose and constrain model simulations of photosynthesis and related
processes, through direct comparison to and assimilation of tower, airborne, and satellite data. TBMs describe key processes related to the absorption of
sunlight, leaf-level fluorescence emission, scattering, and reabsorption throughout the canopy. Here, we analyze simulations from an ensemble of
process-based TBM–SIF models (SiB3 – Simple Biosphere Model, SiB4, CLM4.5 – Community Land Model, CLM5.0, BETHY – Biosphere Energy Transfer Hydrology, ORCHIDEE – Organizing Carbon and Hydrology In Dynamic Ecosystems, and BEPS – Boreal Ecosystems Productivity Simulator) and the SCOPE (Soil Canopy Observation Photosynthesis Energy) canopy radiation and vegetation model at a subalpine
evergreen needleleaf forest near Niwot Ridge, Colorado. These models are forced with local meteorology and analyzed against tower-based continuous
far-red SIF and gross-primary-productivity-partitioned (GPP) eddy covariance data at diurnal and synoptic scales during the growing season
(July–August 2017). Our primary objective is to summarize the site-level state of the art in TBM–SIF modeling over a relatively short time period
(summer) when light, canopy structure, and pigments are similar, setting the stage for regional- to global-scale analyses. We find that these models
are generally well constrained in simulating photosynthetic yield but show strongly divergent patterns in the simulation of absorbed photosynthetic
active radiation (PAR), absolute GPP and fluorescence, quantum yields, and light response at the leaf and canopy scales. This study highlights the need for
mechanistic modeling of nonphotochemical quenching in stressed and unstressed environments and improved the representation of light absorption (APAR),
distribution of light across sunlit and shaded leaves, and radiative transfer from the leaf to the canopy scale.
The Great Lakes Surface Temperature (GLST) is the key to understanding the effects of climate change on the Great Lakes (GL). This study provides the first techniques to retrieve pixel-based GLST ...under all sky conditions by merging skin temperature derived from the MODIS Land Surface Temperature (MOD11L2) and the MODIS Cloud product (MOD06L2) from 6 July 2001 to 31 December 2014, resulting in 18,807 scenes in total 9373 (9434) scenes for MOD11L2 (MOD06L2). The pixel-based GLST under all sky conditions was well-correlated with the in situ observations (R2 = 0.9102) with a cool bias of -1.10 degree C and a root mean square error (RMSE) of 1.39 degree C. The study also presents the long-term trends of GLST. Contrary to expectations, it decreased slightly due to the impact of an anomalously cold winter in 2013-2014.
We apply and compare three widely applicable methods for estimating ecosystem transpiration (T) from eddy covariance (EC) data across 251 FLUXNET sites globally. All three methods are based on the ...coupled water and carbon relationship, but they differ in assumptions and parameterizations. Intercomparison of the three daily T estimates shows high correlation among methods (R between .89 and .94), but a spread in magnitudes of T/ET (evapotranspiration) from 45% to 77%. When compared at six sites with concurrent EC and sap flow measurements, all three EC‐based T estimates show higher correlation to sap flow‐based T than EC‐based ET. The partitioning methods show expected tendencies of T/ET increasing with dryness (vapor pressure deficit and days since rain) and with leaf area index (LAI). Analysis of 140 sites with high‐quality estimates for at least two continuous years shows that T/ET variability was 1.6 times higher across sites than across years. Spatial variability of T/ET was primarily driven by vegetation and soil characteristics (e.g., crop or grass designation, minimum annual LAI, soil coarse fragment volume) rather than climatic variables such as mean/standard deviation of temperature or precipitation. Overall, T and T/ET patterns are plausible and qualitatively consistent among the different water flux partitioning methods implying a significant advance made for estimating and understanding T globally, while the magnitudes remain uncertain. Our results represent the first extensive EC data‐based estimates of ecosystem T permitting a data‐driven perspective on the role of plants’ water use for global water and carbon cycling in a changing climate.
While transpiration (T) from plants has been studied for centuries, it is difficult to measure at ecosystem scale. We explore three new methods for estimating T from existing eddy covariance data from FLUXNET, providing insights into how plants use water at over 251 sites across the globe. Though there is still work to be done to constrain the magnitude of T, we show that this new dataset represents a significant step toward bridging the gap between individual plant measurements and global estimates of plant water use. Photo credit to Tiana Wilene Hammer.
This study provides the first technique to investigate the turbulent fluxes over the Great Lakes from July 2001 to December 2014 using a combination of data from satellite remote sensing, reanalysis ...data sets, and direct measurements. Turbulent fluxes including latent heat flux (QE) and sensible heat flux (QH) were estimated using the bulk aerodynamic approach, then compared with the direct eddy covariance measurements from the rooftop of three lighthouses-Stannard Rock Lighthouse (SR) in Lake Superior, White Shoal Lighthouse (WS) in Lake Michigan, and Spectacle Reef Lighthouse (SP) in Lake Huron. The relationship between modeled and measured QE and QH were in a good statistical agreement, for QE, R2 varied from 0.41 (WS), 0.74 (SR), and 0.87 (SP) with RMSE of 5.68, 6.93, and 4.67 W times m-2, respectively, while QH, R2 ranged from 0.002 (WS), 0.8030 (SP) and 0.94 (SR) with RMSE of 6.97, 4.39 and 4.90 W times m-2 respectively. Both monthly mean QE and QH were highest in January for all lakes except Lake Ontario, which was highest in early December. The turbulent fluxes then sharply drop in March and are negligible during June and July. The evaporation processes continue again in August.
Photosynthesis by terrestrial plants represents the majority of CO2 uptake on Earth, yet it is difficult to measure directly from space. Estimation of gross primary production (GPP) from remote ...sensing indices represents a primary source of uncertainty, in particular for observing seasonal variations in evergreen forests. Recent vegetation remote sensing techniques have highlighted spectral regions sensitive to dynamic changes in leaf/needle carotenoid composition, showing promise for tracking seasonal changes in photosynthesis of evergreen forests. However, these have mostly been investigated with intermittent field campaigns or with narrow-band spectrometers in these ecosystems. To investigate this potential, we continuously measured vegetation reflectance (400–900 nm) using a canopy spectrometer system, PhotoSpec, mounted on top of an eddy-covariance flux tower in a subalpine evergreen forest at Niwot Ridge, Colorado, USA. We analyzed driving spectral components in the measured canopy reflectance using both statistical and process-based approaches. The decomposed spectral components co-varied with carotenoid content and GPP, supporting the interpretation of the photochemical reflectance index (PRI) and the chlorophyll/carotenoid index (CCI). Although the entire 400–900 nm range showed additional spectral changes near the red edge, it did not provide significant improvements in GPP predictions. We found little seasonal variation in both normalized difference vegetation index (NDVI) and the near-infrared vegetation index (NIRv) in this ecosystem. In addition, we quantitatively determined needle-scale chlorophyll-to-carotenoid ratios as well as anthocyanin contents using full-spectrum inversions, both of which were tightly correlated with seasonal GPP changes. Reconstructing GPP from vegetation reflectance using partial least-squares regression (PLSR) explained approximately 87 % of the variability in observed GPP. Our results linked the seasonal variation in reflectance to the pool size of photoprotective pigments, highlighting all spectral locations within 400–900 nm associated with GPP seasonality in evergreen forests.
•The rainfall interception evaporation process remains poorly understood.•Water budget methods typically produce higher estimates than energy budget methods.•Analysis of FLUXNET micrometeorological ...observations shows several causes.•Eddy-covariance measurements during rainfall should be treated with caution.•Careful data treatment could reconcile water and energy budget derived estimates.
Evaporation from wet canopies (E) can return up to half of incident rainfall back into the atmosphere and is a major cause of the difference in water use between forests and short vegetation. Canopy water budget measurements often suggest values of E during rainfall that are several times greater than those predicted from Penman–Monteith theory. Our literature review identified potential issues with both estimation approaches, producing several hypotheses that were tested using micrometeorological observations from 128 FLUXNET sites world-wide. The analysis shows that FLUXNET eddy-covariance measurements tend to provide unreliable measurements of E during rainfall. However, the other micrometeorological FLUXNET observations do provide clues as to why conventional Penman–Monteith applications underestimate E. Aerodynamic exchange rather than radiation often drives E during rainfall, and hence errors in air humidity measurement and aerodynamic conductance calculation have considerable impact. Furthermore, evaporative cooling promotes a downwards heat flux from the air aloft as well as from the biomass and soil; energy sources that are not always considered. Accounting for these factors leads to E estimates and modelled interception losses that are considerably higher. On the other hand, canopy water budget measurements can lead to overestimates of E due to spatial sampling errors in throughfall and stemflow, underestimation of canopy rainfall storage capacity, and incorrect calculation of rainfall duration. There are remaining questions relating to horizontal advection from nearby dry areas, infrequent large-scale turbulence under stable atmospheric conditions, and the possible mechanical removal of splash droplets by such eddies. These questions have implications for catchment hydrology, rainfall recycling, land surface modelling, and the interpretation of eddy-covariance measurements.
Summary
Evergreen conifer forests are the most prevalent land cover type in North America. Seasonal changes in the color of evergreen forest canopies have been documented with near‐surface remote ...sensing, but the physiological mechanisms underlying these changes, and the implications for photosynthetic uptake, have not been fully elucidated.
Here, we integrate on‐the‐ground phenological observations, leaf‐level physiological measurements, near surface hyperspectral remote sensing and digital camera imagery, tower‐based CO2 flux measurements, and a predictive model to simulate seasonal canopy color dynamics.
We show that seasonal changes in canopy color occur independently of new leaf production, but track changes in chlorophyll fluorescence, the photochemical reflectance index, and leaf pigmentation. We demonstrate that at winter‐dormant sites, seasonal changes in canopy color can be used to predict the onset of canopy‐level photosynthesis in spring, and its cessation in autumn. Finally, we parameterize a simple temperature‐based model to predict the seasonal cycle of canopy greenness, and we show that the model successfully simulates interannual variation in the timing of changes in canopy color.
These results provide mechanistic insight into the factors driving seasonal changes in evergreen canopy color and provide opportunities to monitor and model seasonal variation in photosynthetic activity using color‐based vegetation indices.
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