Accurate estimation of terrestrial gross primary productivity (
Drought is among the most damaging climate extremes, potentially causing significant decline in ecosystem functioning and services at the regional to global scale, thus monitoring of drought events is critically important. Solar‐induced chlorophyll fluorescence (SIF) has been found to strongly correlate with gross primary production on the global scale. Recent advances in the remote sensing of SIF allow for large‐scale, real‐time estimation of photosynthesis using this relationship. However, several studies have used SIF to quantify the impact of drought with mixed results, and the leaf‐level mechanisms linking SIF and photosynthesis are unclear, particularly how the relationship may change under drought. We conducted a drought experiment with 2‐yr old
- NSF-PAR ID:
- 10375730
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Ecological Applications
- Volume:
- 30
- Issue:
- 5
- ISSN:
- 1051-0761
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract GPP ) remains a challenge despite its importance in the global carbon cycle. Chlorophyll fluorescence (ChlF) has been recently adopted to understand photosynthesis and its response to the environment, particularly with remote sensing data. However, it remains unclear how ChlF and photosynthesis are linked at different spatial scales across the growing season. We examined seasonal relationships between ChlF and photosynthesis at the leaf, canopy, and ecosystem scales and explored how leaf‐level ChlF was linked with canopy‐scale solar‐induced chlorophyll fluorescence (SIF ) in a temperate deciduous forest at Harvard Forest, Massachusetts,USA . Our results show that ChlF captured the seasonal variations of photosynthesis with significant linear relationships between ChlF and photosynthesis across the growing season over different spatial scales (R 2 = 0.73, 0.77, and 0.86 at leaf, canopy, and satellite scales, respectively;P < 0.0001). We developed a model to estimateGPP from the tower‐based measurement ofSIF and leaf‐level ChlF parameters. The estimation ofGPP from this model agreed well with flux tower observations ofGPP (R 2 = 0.68;P < 0.0001), demonstrating the potential ofSIF for modelingGPP . At the leaf scale, we found that leafF q ’ /F m ’ , the fraction of absorbed photons that are used for photochemistry for a light‐adapted measurement from a pulse amplitude modulation fluorometer, was the best leaf fluorescence parameter to correlate with canopySIF yield (SIF /APAR ,R 2 = 0.79;P < 0.0001). We also found that canopySIF andSIF ‐derivedGPP (GPPSIF ) were strongly correlated to leaf‐level biochemistry and canopy structure, including chlorophyll content (R 2 = 0.65 for canopyGPPSIF and chlorophyll content;P < 0.0001), leaf area index (LAI ) (R 2 = 0.35 for canopyGPPSIF andLAI ;P < 0.0001), and normalized difference vegetation index (NDVI ) (R 2 = 0.36 for canopyGPPSIF andNDVI ;P < 0.0001). Our results suggest that ChlF can be a powerful tool to track photosynthetic rates at leaf, canopy, and ecosystem scales. -
Solar-Induced Chlorophyll Fluorescence (SIF) can provide key information about the state of photosynthesis and offers the prospect of defining remote sensing-based estimation of Gross Primary Production (GPP). There is strong theoretical support for the link between SIF and GPP and this relationship has been empirically demonstrated using ground-based, airborne, and satellite-based SIF observations, as well as modeling. However, most evaluations have been based on monthly and annual scales, yet the GPP:SIF relations can be strongly influenced by both vegetation structure and physiology. At the monthly timescales, the structural response often dominates but short-term physiological variations can strongly impact the GPP:SIF relations. Here, we test how well SIF can predict the inter-daily variation of GPP during the growing season and under stress conditions, while taking into account the local effect of sites and abiotic conditions. We compare the accuracy of GPP predictions from SIF at different timescales (half-hourly, daily, and weekly), while evaluating effect of adding environmental variables to the relationship. We utilize observations for years 2018–2019 at 31 mid-latitudes, forested, eddy covariance (EC) flux sites in North America and Europe and use TROPOMI satellite data for SIF. Our results show that SIF is a good predictor of GPP, when accounting for inter-site variation, probably due to differences in canopy structure. Seasonally averaged leaf area index, fraction of absorbed photosynthetically active radiation (fPAR) and canopy conductance provide a predictor to the site-level effect. We show that fPAR is the main factor driving errors in the linear model at high temporal resolution. Adding water stress indicators, namely canopy conductance, to a multi-linear SIF-based GPP model provides the best improvement in the model precision at the three considered timescales, showing the importance of accounting for water stress in GPP predictions, independent of the SIF signal. SIF is a promising predictor for GPP among other remote sensing variables, but more focus should be placed on including canopy structure, and water stress effects in the relationship, especially when considering intra-seasonal, and inter- and intra-daily resolutions.more » « less
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Abstract Tropical forest canopies cycle vast amounts of carbon, yet we still have a limited understanding of how these critical ecosystems will respond to climate warming. We implemented in situ leaf‐level + 3°C experimental warming from the understory to the upper canopy of two Puerto Rican tropical tree species,
Guarea guidonia andOcotea sintenisii . After approximately 1 month of continuous warming, we assessed adjustments in photosynthesis, chlorophyll fluorescence, stomatal conductance, leaf traits and foliar respiration. Warming did not alter net photosynthetic temperature response for either species; however, the optimum temperature ofOcotea understory leaf photosynthetic electron transport shifted upward. There was noOcotea respiratory treatment effect, whileGuarea respiratory temperature sensitivity (Q 10) was down‐regulated in heated leaves. The optimum temperatures for photosynthesis (T opt) decreased 3–5°C from understory to the highest canopy position, perhaps due to upper canopy stomatal conductance limitations.Guarea upper canopyT optwas similar to the mean daytime temperatures, whileOcotea canopy leaves often operated aboveT opt. With minimal acclimation to warmer temperatures in the upper canopy, further warming could put these forests at risk of reduced CO2uptake, which could weaken the overall carbon sink strength of this tropical forest. -
null (Ed.)Abstract. At the leaf level, stomata control the exchange of water and carbon across the air–leaf interface. Stomatal conductance is typically modeledempirically, based on environmental conditions at the leaf surface. Recently developed stomatal optimization models show great skills at predictingcarbon and water fluxes at both the leaf and tree levels. However, how well the optimization models perform atlarger scales has not been extensively evaluated. Furthermore, stomatal models are often used with simple single-leaf representations of canopy radiative transfer (RT), such asbig-leaf models. Nevertheless, the single-leaf canopy RT schemes do not have the capability to model optical properties of the leaves nor the entirecanopy. As a result, they are unable to directly link canopy optical properties with light distribution within the canopy to remote sensing dataobserved from afar. Here, we incorporated one optimization-based and two empirical stomatal models with a comprehensive RT model in the landcomponent of a new Earth system model within CliMA, the Climate Modelling Alliance. The model allowed us to simultaneously simulate carbon and waterfluxes as well as leaf and canopy reflectance and fluorescence spectra. We tested our model by comparing our modeled carbon and water fluxes andsolar-induced chlorophyll fluorescence (SIF) to two flux tower observations (a gymnosperm forest and an angiosperm forest) and satellite SIFretrievals, respectively. All three stomatal models quantitatively predicted the carbon and water fluxes for both forests. The optimization model,in particular, showed increased skill in predicting the water flux given the lower error (ca. 14.2 % and 21.8 % improvement for thegymnosperm and angiosperm forests, respectively) and better 1:1 comparison (slope increases from ca. 0.34 to 0.91 for the gymnosperm forest andfrom ca. 0.38 to 0.62 for the angiosperm forest). Our model also predicted the SIF yield, quantitatively reproducing seasonal cycles for bothforests. We found that using stomatal optimization with a comprehensive RT model showed high accuracy in simulating land surface processes. Theever-increasing number of regional and global datasets of terrestrial plants, such as leaf area index and chlorophyll contents, will helpparameterize the land model and improve future Earth system modeling in general.more » « less
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Abstract High temperature and accompanying high vapor pressure deficit often stress plants without causing distinctive changes in plant canopy structure and consequential spectral signatures. Sun‐induced chlorophyll fluorescence (SIF), because of its mechanistic link with photosynthesis, may better detect such stress than remote sensing techniques relying on spectral reflectance signatures of canopy structural changes. However, our understanding about physiological mechanisms of SIF and its unique potential for physiological stress detection remains less clear. In this study, we measured SIF at a high‐temperature experiment, Temperature Free‐Air Controlled Enhancement, to explore the potential of SIF for physiological investigations. The experiment provided a gradient of soybean canopy temperature with 1.5, 3.0, 4.5, and 6.0°C above the ambient canopy temperature in the open field environments. SIF yield, which is normalized by incident radiation and the fraction of absorbed photosynthetically active radiation, showed a high correlation with photosynthetic light use efficiency (
r = 0.89) and captured dynamic plant responses to high‐temperature conditions. SIF yield was affected by canopy structural and plant physiological changes associated with high‐temperature stress (partial correlationr = 0.60 and −0.23). Near‐infrared reflectance of vegetation, only affected by canopy structural changes, was used to minimize the canopy structural impact on SIF yield and to retrieve physiological SIF yield (ΦF) signals. ΦFfurther excludes the canopy structural impact than SIF yield and indicates plant physiological variability, and we found that ΦFoutperformed SIF yield in responding to physiological stress (r = −0.37). Our findings highlight that ΦFsensitively responded to the physiological downregulation of soybean gross primary productivity under high temperature. ΦF, if reliably derived from satellite SIF, can support monitoring regional crop growth and different ecosystems' vegetation productivity under environmental stress and climate change.
