Linking chlorophyll a fluorescence to photosynthesis for remote sensing applications: mechanisms and challenges

Hyperspectral Indices Developed from Fractional-Order Derivative Spectra Improved Estimation of Leaf Chlorophyll Fluorescence Parameters

Chlorophyll fluorescence (ChlF) parameters offer valuable insights into quantifying energy transfer and allocation at the photosystem level. However, tracking their variation based on reflectance spectral information remains challenging for large-scale remote sensing applications and ecological modeling. Spectral preprocessing methods, such as fractional-order derivatives (FODs), have been demonstrated to have advantages in highlighting spectral features. In this study, we developed and assessed the ability of novel spectral indices derived from FOD spectra and other spectral transformations to retrieve the ChlF parameters of various species and leaf groups. The results obtained showed that the empirical spectral indices were of low reliability in estimating the ChlF parameters. In contrast, the indices developed from low-order FOD spectra demonstrated a significant improvement in estimation. Furthermore, the incorporation of species specificity enhanced the tracking of the non-photochemical quenching (NPQ) of sunlit leaves (R2 = 0.61, r = 0.79, RMSE = 0.15, MAE = 0.13), the fraction of PSII open centers (qL) of shaded leaves (R2 = 0.50, r = 0.71, RMSE = 0.09, MAE = 0.08), and the fluorescence quantum yield (ΦF) of shaded leaves (R2 = 0.71, r = 0.85, RMSE = 0.002, MAE = 0.001). Our study demonstrates the potential of FOD spectra in capturing variations in ChlF parameters. Nevertheless, given the complexity and sensitivity of ChlF parameters, it is prudent to exercise caution when utilizing spectral indices for tracking them.

Temporal and spatial evolution simulation and attribution analysis of vegetation photosynthesis over the past 21 years based on satellite SIF data: a case study from Asia

Photosynthesis in vegetation is one of the key processes in maintaining regional ecological balance and climate stability, and it is of significant importance for understanding the health of regional ecosystems and addressing climate change. Based on 2001-2021 Global OCO-2 Solar-Induced Fluorescence (GOSIF) dataset, this study analyzed spatiotemporal variations in Asian vegetation photosynthesis and its response to climate and human activities. Results show the following: (1) From 2001 to 2021, the overall photosynthetic activity of vegetation in the Asian region has shown an upward trend, exhibiting a stable distribution pattern with higher values in the eastern and southern regions and lower values in the central, western, and northern regions. In specific regions such as the Turgen Plateau in northwestern Kazakhstan, Cambodia, Laos, and northeastern Syria, photosynthesis significantly declined. (2) Meteorological factors influencing photosynthesis exhibit differences based on latitude and vertical zones. In low-latitude regions, temperature is the primary driver, while in mid-latitude areas, solar radiation and precipitation are crucial. High-latitude regions are primarily influenced by temperature, and high-altitude areas depend on precipitation and solar radiation. (3) Human activities (56.44%) have a slightly greater impact on the dynamics of Asian vegetation photosynthesis compared to climate change (43.56%). This research deepens our comprehension of the mechanisms behind the fluctuations in Asian vegetation photosynthesis, offering valuable perspectives for initiatives in environmental conservation, sustainability, and climate research.

Integrating leaf spectral and water status information to effectively track chlorophyll a fluorescence parameters during dehydration

Monitoring changes in chlorophyll a (ChlFa) fluorescence during dehydration can provide insights into plant photosynthetic responses to climate change challenges, which are predicted to increase drought frequency. However, the limited knowledge of how ChlFa parameters respond to water deficit hinders the exploration of the photochemical mechanism of the photosynthetic process and the simulation of photosynthetic fluorescence models. Furthermore, how to track such responses of ChlFa parameters, especially at large scales, remains a challenge. In this study, we attempted to use spectral information reflected from leaves to follow the dynamic response patterns of ChlFa parameters of seven species under prolonged dehydration. The results showed that the investigated ChlFa parameters exhibited significant changes as dehydration progressed, with considerable variability among the different species as well as under different water conditions. This study also demonstrated that the integration of both spectral and water content information can provide an effective method for tracking ChlFa parameters during dehydration, explaining over 90% of the total variance in the measured ChlFa parameters. Collectively, these results should serve as a valuable reference for predicting the response of ChlFa parameters to dehydration and offer a potential method for estimating ChlFa parameters under drought conditions.

Mechanisms of photoprotection in overwintering evergreen conifers: Sustained quenching of chlorophyll fluorescence

Evergreen conifers growing in high-latitude regions must endure prolonged winters that are characterized by sub-zero temperatures combined with light, conditions that can cause significant photooxidative stress. Understanding overwintering mechanisms is crucial for addressing winter adversity in temperate forest ecosystems and enhancing the ability of conifers to adapt to climate change. This review synthesizes the current understanding of the photoprotective mechanisms that conifers employ to mitigate photooxidative stress, particularly non-photochemical "sustained quenching", the mechanism of which is hypothesized to be a recombination or deformation of the original mechanism employed by conifers in response to short-term low temperature and intense light stress in the past. Based on this hypothesis, scattered studies in this field are assembled and integrated into a complete mechanism of sustained quenching embedded in the adaptation process of plant physiology. It also reveals which parts of the whole system have been verified in conifers and which have only been verified in non-conifers, and proposes specific directions for future research. The functional implications of studies of non-coniferous plant species for the study of coniferous trees are also considered, as a wide range of plant responses lead to sustained quenching, even among different conifer species. In addition, the review highlights the challenges of measuring sustained quenching and discusses the application of ultrafast-time-resolved fluorescence and decay-associated spectra for the elucidation of photosynthetic principles.

Decoupling of nitrogen allocation and energy partitioning in rice after flowering

Estimation of energy partitioning at leaf scale, such as fluorescence yield (ΦF) and photochemical yield (ΦP), is crucial to tracking vegetation gross primary productivity (GPP) at global scale. Nitrogen is an important participant in the process of light capture, electron transfer, and carboxylation in vegetation photosynthesis. However, the quantitative relationship between leaf nitrogen allocation and leaf energy partitioning remains unexplored. Here, a field experiment was established to explore growth stage variations in energy partitioning and nitrogen allocation at leaf scale using active fluorescence detection and photosynthetic gas exchange method in rice in the subtropical region of China. We observed a strongly positive correlation between the investment proportion of leaf nitrogen in photosynthetic system and ΦF during the vegetative growth stage. There were significant differences in leaf energy partitioning, leaf nitrogen allocation, and the relationship between ΦF and ΦP before and after flowering. Furthermore, flowering weakened the correlation between the investment proportion of leaf nitrogen in photosynthetic system and ΦF. These findings highlight the crucial role of phenological factors in exploring seasonal photosynthetic dynamics and carbon fixation of ecosystems.

Alternative method for determining leaf CO2 assimilation without gas exchange measurements: Performance, comparison and sensitivity analysis

We present an alternative method to determine leaf CO2 assimilation rate (An ), eliminating the need for gas exchange measurements in proximal and remote sensing. This method combines the Farquhar-von Caemmerer-Berry photosynthesis model with mechanistic light reaction (MLR) theory and leaf energy balance (EB) analysis. The MLR theory estimates the actual electron transport rate (J) by leveraging chlorophyll fluorescence via pulse amplitude-modulated fluorometry for proximal sensing or sun-induced chlorophyll fluorescence measurements for remote sensing, along with spectral reflectance. The EB equation is used to directly estimate stomatal conductance from leaf temperature. In wheat and soybean, the MLR-EB model successfully estimated An variations, including midday depression, under various environmental and phenological conditions. Sensitivity analysis revealed that the leaf boundary layer conductance (gb ) played an equal, if not more, crucial role compared to the variables for J. This was primarily caused by the indirect influence of gb through the EB equation rather than its direct impact on convective CO2 exchange on the leaf. Although the MLR-EB model requires an accurate estimation of gb , it can potentially reduce uncertainties and enhance applicability in photosynthesis assessment when gas exchange measurements are unavailable.

Application of Chlorophyll Fluorescence Analysis Technique in Studying the Response of Lettuce (Lactuca sativa L.) to Cadmium Stress

To reveal the impact of cadmium stress on the physiological mechanism of lettuce, simultaneous determination and correlation analyses of chlorophyll content and photosynthetic function were conducted using lettuce seedlings as the research subject. The changes in relative chlorophyll content, rapid chlorophyll fluorescence induction kinetics curve, and related chlorophyll fluorescence parameters of lettuce seedling leaves under cadmium stress were detected and analyzed. Furthermore, a model for estimating relative chlorophyll content was established. The results showed that cadmium stress at 1 mg/kg and 5 mg/kg had a promoting effect on the relative chlorophyll content, while cadmium stress at 10 mg/kg and 20 mg/kg had an inhibitory effect on the relative chlorophyll content. Moreover, with the extension of time, the inhibitory effect became more pronounced. Cadmium stress affects both the donor and acceptor sides of photosystem II in lettuce seedling leaves, damaging the electron transfer chain and reducing energy transfer in the photosynthetic system. It also inhibits water photolysis and decreases electron transfer efficiency, leading to a decline in photosynthesis. However, lettuce seedling leaves can mitigate photosystem II damage caused by cadmium stress through increased thermal dissipation. The model established based on the energy captured by a reaction center for electron transfer can effectively estimate the relative chlorophyll content of leaves. This study demonstrates that chlorophyll fluorescence techniques have great potential in elucidating the physiological mechanism of cadmium stress in lettuce, as well as in achieving synchronized determination and correlation analyses of chlorophyll content and photosynthetic function.

Ground far-red sun-induced chlorophyll fluorescence and vegetation indices in the US Midwestern agroecosystems

Sun-induced chlorophyll fluorescence (SIF) provides an opportunity to study terrestrial ecosystem photosynthesis dynamics. However, the current coarse spatiotemporal satellite SIF products are challenging for mechanistic interpretations of SIF signals. Long-term ground SIF and vegetation indices (VIs) are important for satellite SIF validation and mechanistic understanding of the relationship between SIF and photosynthesis when combined with leaf- and canopy-level auxiliary measurements. In this study, we present and analyze a total of 15 site-years of ground far-red SIF (SIF at 760 nm, SIF760) and VIs datasets from soybean, corn, and miscanthus grown in the U.S. Corn Belt from 2016 to 2021. We introduce a comprehensive data processing protocol, including different retrieval methods, calibration coefficient adjustment, and nadir SIF footprint upscaling to match the eddy covariance footprint. This long-term ground far-red SIF and VIs dataset provides important and first-hand data for far-red SIF interpretation and understanding the mechanistic relationship between far-red SIF and canopy photosynthesis across various crop species and environmental conditions.

Linking photosynthesis and yield reveals a strategy to improve light use efficiency in a climbing bean breeding population

Photosynthesis drives plant physiology, biomass accumulation, and yield. Photosynthetic efficiency, specifically the operating efficiency of PSII (Fq'/Fm'), is highly responsive to actual growth conditions, especially to fluctuating photosynthetic photon fluence rate (PPFR). Under field conditions, plants constantly balance energy uptake to optimize growth. The dynamic regulation complicates the quantification of cumulative photochemical energy uptake based on the intercepted solar energy, its transduction into biomass, and the identification of efficient breeding lines. Here, we show significant effects on biomass related to genetic variation in photosynthetic efficiency of 178 climbing bean (Phaseolus vulgaris L.) lines. Under fluctuating conditions, the Fq'/Fm' was monitored throughout the growing period using hand-held and automated chlorophyll fluorescence phenotyping. The seasonal response of Fq'/Fm' to PPFR (ResponseG:PPFR) achieved significant correlations with biomass and yield, ranging from 0.33 to 0.35 and from 0.22 to 0.31 in two glasshouse and three field trials, respectively. Phenomic yield prediction outperformed genomic predictions for new environments in four trials under different growing conditions. Investigating genetic control over photosynthesis, one single nucleotide polymorphism (Chr09_37766289_13052) on chromosome 9 was significantly associated with ResponseG:PPFR in proximity to a candidate gene controlling chloroplast thylakoid formation. In conclusion, photosynthetic screening facilitates and accelerates selection for high yield potential.

Knowledge mapping on the sun-induced chlorophyll fluorescence technology research: a scientometric and visualization analysis

The sun-induced chlorophyll fluorescence (SIF) has received increasing attention over the past few years. This scientometric study analyzed the SIF research field, based on publications available in Web of Science Core Collection visualization, and cluster analysis enabled us to map the knowledge domain and intellectual landscape of this field and identify thematic trends, landmark articles, and emerging research themes. In this study, the software VOSviewer and CiteSpace were used to identify the intellectual base and research front using visualization and analysis. Then, we analyzed synthesized networks of co-authorship (author, institution, and country), co-citation (author, document, and journal), and co-occurring keywords. SIF has increased its publication output steadily since 2002. This study provided a visual knowledge map of the major research domains of SIF. Also, explanations and implications of the findings were explored, and the emerging trends were identified. This report provides a thorough overview of the current state and growth trends of SIF research, and it helps researchers and practitioners understand the major areas of attention in this area and provides insights into prospective areas for further study.

Wildfire-induced increases in photosynthesis in boreal forest ecosystems of North America

Observations of the annual cycle of atmospheric CO2 in high northern latitudes provide evidence for an increase in terrestrial metabolism in Arctic tundra and boreal forest ecosystems. However, the mechanisms driving these changes are not yet fully understood. One proposed hypothesis is that ecological change from disturbance, such as wildfire, could increase the magnitude and change the phase of net ecosystem exchange through shifts in plant community composition. Yet, little quantitative work has evaluated this potential mechanism at a regional scale. Here we investigate how fire disturbance influences landscape-level patterns of photosynthesis across western boreal North America. We use Alaska and Canadian large fire databases to identify the perimeters of wildfires, a Landsat-derived land cover time series to characterize plant functional types (PFTs), and solar-induced fluorescence (SIF) from the Orbiting Carbon Observatory-2 (OCO-2) as a proxy for photosynthesis. We analyze these datasets to characterize post-fire changes in plant succession and photosynthetic activity using a space-for-time approach. We find that increases in herbaceous and sparse vegetation, shrub, and deciduous broadleaf forest PFTs during mid-succession yield enhancements in SIF by 8-40% during June and July for 2- to 59-year stands relative to pre-fire controls. From the analysis of post-fire land cover changes within individual ecoregions and modeling, we identify two mechanisms by which fires contribute to long-term trends in SIF. First, increases in annual burning are shifting the stand age distribution, leading to increases in the abundance of shrubs and deciduous broadleaf forests that have considerably higher SIF during early- and mid-summer. Second, fire appears to facilitate a long-term shift from evergreen conifer to broadleaf deciduous forest in the Boreal Plain ecoregion. These findings suggest that increasing fire can contribute substantially to positive trends in seasonal CO2 exchange without a close coupling to long-term increases in carbon storage.

Solar-induced chlorophyll fluorescence captures the effects of elevated ozone on canopy structure and acceleration of senescence in soybean

Solar-induced chlorophyll fluorescence (SIF) provides an opportunity to rapidly and non-destructively investigate how plants respond to stress. Here, we explored the potential of SIF to detect the effects of elevated O3 on soybean in the field where soybean was subjected to ambient and elevated O3 throughout the growing season in 2021. Exposure to elevated O3 resulted in a significant decrease in canopy SIF at 760 nm (SIF760), with a larger decrease in the late growing season (36%) compared with the middle growing season (13%). Elevated O3 significantly decreased the fraction of absorbed photosynthetically active radiation by 8-15% in the middle growing season and by 35% in the late growing stage. SIF760 escape ratio (fesc) was significantly increased under elevated O3 by 5-12% in the late growth stage due to a decrease of leaf chlorophyll content and leaf area index. Fluorescence yield of the canopy was reduced by 5-11% in the late growing season depending on the fesc estimation method, during which leaf maximum carboxylation rate and maximum electron transport were significantly reduced by 29% and 20% under elevated O3. These results demonstrated that SIF could capture the elevated O3 effect on canopy structure and acceleration of senescence in soybean and provide empirical support for using SIF for soybean stress detection and phenotyping.

Improving the estimation accuracy of rapeseed leaf photosynthetic characteristics under salinity stress using continuous wavelet transform and successive projections algorithm

Soil salinization greatly restricts crop production in arid areas for salinity stress can inhibit crop photosynthesis and growth. Chlorophyll fluorescence and photosynthetic gas exchange (CFPGE) parameters are important indicators of crop photosynthesis and have been widely used to evaluate the impacts of salinity stress on crop photosynthesis and growth. Remote sensing technology can quickly and non-destructively obtain crop information under salinity stress, however, at present, the distribution of spectral features of CFPGE parameters in different regions is still unclear. In this study (2019-2020), under salinity stress conditions, the spectral data of rapeseed leaves were acquired and the CFPGE parameters were simultaneously determined. Then, continuous wavelet transformation (CWT) and standard normal variate (SNV) transformation were utilized to preprocess the raw spectral data. After that, a CFPGE parameter estimation model was constructed by using the partial least squares regression (PLSR) algorithm and the support vector machines (SVM) algorithm based on the spectral features in the red region (600-800 nm) and those in the red, blue-green (350-600 nm), and near-infrared (800-2500 nm) regions. The results showed that the spectral features of CFPGE parameters could be extracted by successive projections algorithm (SPA) based on the CWT preprocessing. The CFPGE parameter estimation model constructed based on the spectral features in the red region (675 nm, 680 nm, 688 nm, 749 nm, and 782 nm) had the highest Fv/Fm estimation accuracy on day 30, with R2c, R2p, and RPD of 0.723, 0.585, and 1.68, respectively. Based on this, the spectral features (578 nm, 976 nm, 1088 nm, 1476 nm, and 2250 nm) in the blue-green and near-infrared regions were added in the variables for modeling, which significantly improved the accuracy and stability of the model, with R2c, R2p, and RPD of 0.886, 0.815, and 2.58, respectively. Therefore, the fusion of the spectral features in the red, blue-green, and near-infrared regions could improve the estimation accuracy of rapeseed leaf CFPGE parameters. This study will provide technical reference for rapid estimation of photosynthetic performance of crops under salinity stress in arid and semi-arid areas.

Estimation of photosynthetic dynamics in forests from daily measured fluorescence and PRI data with adjustment for canopy shadow fraction

We conducted year-long measurements of the photochemical reflectance index (PRI) and solar-induced fluorescence in the O2A oxygen band (SIFA) at a Norway spruce forest and a European beech forest to study relationships of these remote sensing variables to photosynthesis by trees in grown forest stands. Measured PRI and SIFA values were linked to changes in forest gross primary productivity (GPP) and light-use efficiency (LUE). Changes in the shadow fraction (αS) within tree crowns influenced PRI and fluorescence signals. In the spruce forest, the quantum yield of SIFA (FYSIFA) decreased around midday together with photosynthesis and GPP. Such decreases in FYSIFA were accompanied by an increase in the αS. In the beech forest, we detected an increase in FYSIFA together with a decrease in αS in the afternoon hours. The overall sensitivity of PRI to LUE was variable according to the season, presumably influenced by complex changes in photosynthetic pigments. PRI and FYSIFA showed weak correlations with canopy LUE; however, when considered together, the correlation was strengthened (R2 was 0.63 and 0.34 in spruce and beech forest, respectively). Our model predicting LUE dynamics includes a diurnal minimum of PRI and canopy αS to make allowances for seasonal changes in photosynthetic pigments and for diurnal variability of the shadow fraction in forests. The incorporation of these correcting factors allowed us to estimate LUE at R2 = 0.68 (spruce) and 0.53 (beech). The modeling equations appeared sensitive to the absorbed photosynthetically active radiation (APAR), but less sensitive to the GPP of these forests. Substituting pigments correction with introducing differential PRI (ΔPRI) into the model did not significantly improve the LUE estimation across the season. Our results show that the joint use of PRI and fluorescence improves LUE and GPP estimation accuracy in both daily and seasonal observations.

Evaluation of drought stress level in Sargent's cherry (Prunus sargentii Rehder) using photosynthesis and chlorophyll fluorescence parameters and proline content analysis

Sargent's cherry trees (Prunus sargentiiRehder) are widely planted as an ornamental, climate change-sensing species. This study investigated changes in the soil moisture content, fresh weight, photosynthesis and chlorophyll fluorescence properties, and the chlorophyll and proline content of four-year-old P. sargentii seedlings after 30 days of drought stress. In the trees subjected to drought stress treatment, soil moisture content decreased, and the fresh weight of the aboveground part of the plant decreased. However, there was no significant difference in the root growth of the dried plants. Among the photosynthesis parameters, Pn MAX, E and gs showed a significant (p  <  0.001) decrease after 15 days in dry-stressed seedlings, but there was no difference between treatments in WUE until 20 days, and there was a significant (p  <  0.001) difference after 24 days. Chlorophyll fluorescence parameters, Fv/Fm, ΦPSII, Rfd, NPQ, and Pn MAX, also increased after 10 days in dry-stressed seedlings, but these changes did not reach statistical significance compared to the control treatment. These results may suggest that drought stress highly correlates with photosynthesis and chlorophyll fluorescence parameters. Chlorophyll content also significantly decreased in the seedlings under drought stress compared with the control treatment. The proline content decreased until the 10th day of drought stress treatment and increased after the 15th day, showing an increase of 10.9% on the 15th day and 57.1% on the 30th day, compared to the control treatment. These results suggest that photosynthesis, chlorophyll fluorescence parameters, and proline content can be used to evaluate drought stress in trees. The results of this study can contribute to the management of forests, such as the irrigation of trees when pore control ability and photosynthesis ability decrease.

Tracking canopy chlorophyll fluorescence with a low-cost light emitting diode platform

Chlorophyll fluorescence measured at the leaf scale through pulse amplitude modulation (PAM) has provided valuable insight into photosynthesis. At the canopy- and satellite-scale, solar-induced fluorescence (SIF) provides a method to estimate the photosynthetic activity of plants across spatiotemporal scales. However, retrieving SIF signal remotely requires instruments with high spectral resolution, making it difficult and often expensive to measure canopy-level steady-state chlorophyll fluorescence under natural sunlight. Considering this, we built a novel low-cost photodiode system that retrieves far-red chlorophyll fluorescence emission induced by a blue light emitting diode (LED) light source, for 2 h at night, above the canopy. Our objective was to determine if an active remote sensing-based night-time photodiode method could track changes in canopy-scale LED-induced chlorophyll fluorescence (LEDIF) during an imposed drought on a broadleaf evergreen shrub, Polygala myrtifolia. Far-red LEDIF (720-740 nm) was retrieved using low-cost photodiodes (LEDIFphotodiode) and validated against measurements from a hyperspectral spectroradiometer (LEDIFhyperspectral). To link the LEDIF signal with physiological drought response, we tracked stomatal conductance (gsw) using a porometer, two leaf-level vegetation indices-photochemical reflectance index and normalized difference vegetation index-to represent xanthophyll and chlorophyll pigment dynamics, respectively, and a PAM fluorimeter to measure photochemical and non-photochemical dynamics. Our results demonstrate a similar performance between the photodiode and hyperspectral retrievals of LEDIF (R2 = 0.77). Furthermore, LEDIFphotodiode closely tracked drought responses associated with a decrease in photochemical quenching (R2 = 0.69), Fv/Fm (R2 = 0.59) and leaf-level photochemical reflectance index (R2 = 0.59). Therefore, the low-cost LEDIFphotodiode approach has the potential to be a meaningful indicator of photosynthetic activity at spatial scales greater than an individual leaf and over time.

Relationship between Photosynthetic CO2 Assimilation and Chlorophyll Fluorescence for Winter Wheat under Water Stress

Solar-induced chlorophyll fluorescence (SIF) has a high correlation with Gross Primary Production (GPP). However, studies focusing on the impact of drought on the SIF-GPP relationship have had mixed results at various scales, and the mechanisms controlling the dynamics between photosynthesis and fluorescence emission under water stress are not well understood. We developed a leaf-scale measurement system to perform concurrent measurements of active and passive fluorescence, and gas-exchange rates for winter wheat experiencing a one-month progressive drought. Our results confirmed that: (1) shifts in light energy allocation towards decreasing photochemistry (the quantum yields of photochemical quenching in PSII decreased from 0.42 to 0.21 under intermediate light conditions) and increasing fluorescence emissions (the quantum yields of fluorescence increased to 0.062 from 0.024) as drought progressed enhance the degree of nonlinearity of the SIF-GPP relationship, and (2) SIF alone has a limited capacity to track changes in the photosynthetic status of plants under drought conditions. However, by incorporating the water stress factor into a SIF-based mechanistic photosynthesis model, we show that drought-induced variations in a variety of key photosynthetic parameters, including stomatal conductance and photosynthetic CO2 assimilation, can be accurately estimated using measurements of SIF, photosynthetically active radiation, air temperature, and soil moisture as inputs. Our findings provide the experimental and theoretical foundations necessary for employing SIF mechanistically to estimate plant photosynthetic activity during periods of drought stress.

Effects of drought stress induced by D-Mannitol on the germination and early seedling growth traits, physiological parameters and phytochemicals content of Tunisian squash (Cucurbita maximaDuch.) landraces

Introduction

Drought stress is one of the most devastating environmental stressors, especially in the arid and semi-arid regions of the world. Considering the major constraints that drought stress poses to crop production and the consequent yield losses in food crops, breeding for climate-resilient crops is an efficient means to mitigate stress conditions.

Materials and methods

This study aimed at evaluating the response of four squash (Cucurbita maxima Duchesne) landraces to drought stress at germination and at plant stage. Drought stress was induced by different concentrations of D-mannitol (-0.24, -0.47 and -0.73 MPa). The tested parameters at germination stage included germination percentage, seedling vigor index, seed water absorbance and seedling growth potential. At the plant stage, leaf chlorophyll and carotenoids content, chlorophyll fluorescence, evapotranspiration, photosynthesis activity and several biomarkers, namely malondialdehyde, proline, total phenols content, total flavonoids content and DPPH radical scavenging activity were evaluated in both roots and leaves.

Results and discussion

Our results indicate a magnitude of drought stress effects reflected via repression of germination and seedling growth as well as adjustments in physiological functions at later growth stages, in a genotype depended manner. Among landraces, "751" and "746" showed better performance, as evidenced by higher seed germination and seedling growth potential even at high stress levels (-0.47 and - 0.73 MPa), whereas "747" was the most sensitive landrace to drought stress at both tested stages. In conclusion, our findings highlight the importance of squash landraces selection for the identification of elite genotypes with increased tolerance to drought stress.

Plant optics: underlying mechanisms in remotely sensed signals for phenotyping applications

Optical-based remote sensing offers great potential for phenotyping vegetation traits and functions for a range of applications including vegetation monitoring and assessment. A key strength of optical-based approaches is the underlying mechanistic link to vegetation physiology, biochemistry, and structure that influences a spectral signal. By exploiting spectral variation driven by plant physiological response to environment, remotely sensed products can be used to estimate vegetation traits and functions. However, oftentimes these products are proxies based on covariance, which can lead to misinterpretation and decoupling under certain scenarios. This viewpoint will discuss (i) the optical properties of vegetation, (ii) applications of vegetation indices, solar-induced fluorescence, and machine-learning approaches, and (iii) how covariance can lead to good empirical proximation of plant traits and functions. Understanding and acknowledging the underlying mechanistic basis of plant optics must be considered as remotely sensed data availability and applications continue to grow. Doing so will enable appropriate application and consideration of limitations for the use of optical-based remote sensing for phenotyping applications.

Terrestrial CO2 exchange diagnosis using a peatland-optimized vegetation photosynthesis and respiration model (VPRM) for the Hudson Bay Lowlands

Satellite-based light use efficiency (LUE) models have been widely used to estimate gross primary production in various terrestrial ecosystems such as forests and croplands, but northern peatlands have received less attention. In particular, the Hudson Bay Lowlands (HBL) which is a massive peatland-rich region in Canada has been largely ignored in previous LUE-based studies. These peatland ecosystems have accumulated large stocks of organic carbon over many millennia, and play a vital role in the global carbon cycle. In this study, we used the satellite data-driven Vegetation Photosynthesis and Respiration Model (VPRM) to examine the suitability of LUE models for carbon flux diagnosis in the HBL. VPRM was driven alternately with the satellite-derived enhanced vegetation index (EVI) and solar-induced chlorophyll fluorescence (SIF). The model parameter values were constrained by eddy covariance (EC) tower observations from the Churchill fen and Attawapiskat River bog sites. The main objectives of the study were to (i) investigate if site-specific parameter optimization improved NEE estimates, (ii) determine which satellite-based proxy of photosynthesis produced more reliable estimates of peatland net carbon exchange, and (iii) examine how LUE and other model parameters vary within and between the study sites. The results indicate that the VPRM mean diurnal and monthly estimates of NEE had significant strong agreements with EC tower fluxes at the two study sites. A comparison of the site-optimized VPRM against a generic peatland-optimized version of the model revealed that the site-optimized VPRM provided better estimates of NEE only during the calibration period at the Churchill fen. The diurnal and seasonal cycles of peatland carbon exchange were better captured by the SIF-driven VPRM, demonstrating that SIF is a more accurate proxy for photosynthesis compared to EVI. Our study suggests that satellite-based LUE models have the potential to be applied on a larger scale to the HBL region.

On the concepts and correct use of radiometric quantities for assessing the light environment and their application to plant research

Light is one of the most important factors for photosynthetic organisms to grow. Historically, the amount of light in plant sciences has been referred to as light intensity, irradiance, photosynthetic active radiation, photon flux, photon flux density, etc. On occasion, all these terms are used interchangeably, yet they refer to different physical units and each metric offers distinct information. Even for experts in the fields of plant photobiology, the use of these terms is confusing, and there is a loose implementation of each concept. This makes the use of radiometric units even more confusing to non-experts when looking for ways to measure light, since they could easily feel overwhelmed by the specialized literature. The use of scientific concepts must be accurate, as ambiguity in the use of radiometric quantities can lead to inconsistencies in analysis, thus decreasing the comparability between experiments and to the formulation of incorrect experimental designs. In this review, we provide a simple yet comprehensive view of the use of radiometric quantities in an effort to clarify their meaning and applications. To facilitate understanding, we adopt a minimum amount of mathematical expressions and provide a historical summary of the use of radiometry (with emphasis on plant sciences), examples of uses, and a review of the available instrumentation for radiometric measurements.

Satellite solar-induced chlorophyll fluorescence tracks physiological drought stress development during 2020 southwest US drought

Monitoring and estimating drought impact on plant physiological processes over large regions remains a major challenge for remote sensing and land surface modeling, with important implications for understanding plant mortality mechanisms and predicting the climate change impact on terrestrial carbon and water cycles. The Orbiting Carbon Observatory 3 (OCO-3), with its unique diurnal observing capability, offers a new opportunity to track drought stress on plant physiology. Using radiative transfer and machine learning modeling, we derive a metric of afternoon photosynthetic depression from OCO-3 solar-induced chlorophyll fluorescence (SIF) as an indicator of plant physiological drought stress. This unique diurnal signal enables a spatially explicit mapping of plants' physiological response to drought. Using OCO-3 observations, we detect a widespread increasing drought stress during the 2020 southwest US drought. Although the physiological drought stress is largely related to the vapor pressure deficit (VPD), our results suggest that plants' sensitivity to VPD increases as the drought intensifies and VPD sensitivity develops differently for shrublands and grasslands. Our findings highlight the potential of using diurnal satellite SIF observations to advance the mechanistic understanding of drought impact on terrestrial ecosystems and to improve land surface modeling.

Negative relationship between photosynthesis and late-stage canopy development and senescence over Tibetan Plateau

Canopy greening, which is associated with significant canopy structure changes, is the most notable signal of ecosystem changes in response to anthropogenic climate change. However, our knowledge of the changing pattern of canopy development and senescence, and its endogenous and climatic drivers is still limited. Here, we used the Normalized Difference Vegetation Index (NDVI) to quantify the changes in the speed of canopy development and senescence over the Tibetan Plateau (TP) during 2000-2018, and used a solar-induced chlorophyll fluorescence dataset as a proxy for photosynthesis, in combination with climate datasets to decipher the endogenous and climatic drivers of the interannual variation in canopy changes. We found that the canopy development during the early green-up stage (April-May) is accelerating at a rate of 0.45-0.8 × 10^-3  month^-1  year^-1 . However, this accelerating canopy development was largely offset by a decelerating canopy development during June and July (-0.61 to -0.51 × 10^-3  month^-1  year^-1 ), leading to the peak NDVI over the TP increasing at a rate of only one fifth of that in northern temperate regions, and less than one tenth of that in the Arctic and boreal regions. During the green-down period, we observed a significant accelerating canopy senescence during October. Photosynthesis was found to be the dominant driver for canopy changes over the TP. Increasing photosynthesis stimulates canopy development during the early green-up stage. However, slower canopy development and accelerated senescence was found with larger photosynthesis in late growth stages. This negative relationship between photosynthesis and canopy development is probably linked to the source-sink balance of plants and shifts in the allocation regime. These results suggest a sink limitation for plant growth over the TP. The impact of canopy greening on the carbon cycle may be more complicated than the source-oriented paradigm used in current ecosystem models.

From remotely sensed solar-induced chlorophyll fluorescence to ecosystem structure, function, and service: Part I-Harnessing theory

Solar-induced chlorophyll fluorescence (SIF) is a remotely sensed optical signal emitted during the light reactions of photosynthesis. The past two decades have witnessed an explosion in availability of SIF data at increasingly higher spatial and temporal resolutions, sparking applications in diverse research sectors (e.g., ecology, agriculture, hydrology, climate, and socioeconomics). These applications must deal with complexities caused by tremendous variations in scale and the impacts of interacting and superimposing plant physiology and three-dimensional vegetation structure on the emission and scattering of SIF. At present, these complexities have not been overcome. To advance future research, the two companion reviews aim to (1) develop an analytical framework for inferring terrestrial vegetation structures and function that are tied to SIF emission, (2) synthesize progress and identify challenges in SIF research via the lens of multi-sector applications, and (3) map out actionable solutions to tackle these challenges and offer our vision for research priorities over the next 5-10 years based on the proposed analytical framework. This paper is the first of the two companion reviews, and theory oriented. It introduces a theoretically rigorous yet practically applicable analytical framework. Guided by this framework, we offer theoretical perspectives on three overarching questions: (1) The forward (mechanism) question-How are the dynamics of SIF affected by terrestrial ecosystem structure and function? (2) The inference question: What aspects of terrestrial ecosystem structure, function, and service can be reliably inferred from remotely sensed SIF and how? (3) The innovation question: What innovations are needed to realize the full potential of SIF remote sensing for real-world applications under climate change? The analytical framework elucidates that process complexity must be appreciated in inferring ecosystem structure and function from the observed SIF; this framework can serve as a diagnosis and inference tool for versatile applications across diverse spatial and temporal scales.

From remotely-sensed solar-induced chlorophyll fluorescence to ecosystem structure, function, and service: Part II-Harnessing data

Although our observing capabilities of solar-induced chlorophyll fluorescence (SIF) have been growing rapidly, the quality and consistency of SIF datasets are still in an active stage of research and development. As a result, there are considerable inconsistencies among diverse SIF datasets at all scales and the widespread applications of them have led to contradictory findings. The present review is the second of the two companion reviews, and data oriented. It aims to (1) synthesize the variety, scale, and uncertainty of existing SIF datasets, (2) synthesize the diverse applications in the sector of ecology, agriculture, hydrology, climate, and socioeconomics, and (3) clarify how such data inconsistency superimposed with the theoretical complexities laid out in (Sun et al., 2023) may impact process interpretation of various applications and contribute to inconsistent findings. We emphasize that accurate interpretation of the functional relationships between SIF and other ecological indicators is contingent upon complete understanding of SIF data quality and uncertainty. Biases and uncertainties in SIF observations can significantly confound interpretation of their relationships and how such relationships respond to environmental variations. Built upon our syntheses, we summarize existing gaps and uncertainties in current SIF observations. Further, we offer our perspectives on innovations needed to help improve informing ecosystem structure, function, and service under climate change, including enhancing in-situ SIF observing capability especially in "data desert" regions, improving cross-instrument data standardization and network coordination, and advancing applications by fully harnessing theory and data.

A Mechanistic Model for Estimating Rice Photosynthetic Capacity and Stomatal Conductance from Sun-Induced Chlorophyll Fluorescence

Enhancing the photosynthetic rate is one of the effective ways to increase rice yield, given that photosynthesis is the basis of crop productivity. At the leaf level, crops' photosynthetic rate is mainly determined by photosynthetic functional traits including the maximum carboxylation rate (V_cmax) and stomatal conductance (gs). Accurate quantification of these functional traits is important to simulate and predict the growth status of rice. In recent studies, the emerging sun-induced chlorophyll fluorescence (SIF) provides us an unprecedented opportunity to estimate crops' photosynthetic traits, owing to its direct and mechanistic links to photosynthesis. Therefore, in this study, we proposed a practical semimechanistic model to estimate the seasonal V_cmax and gs time-series based on SIF. We firstly generated the coupling relationship between the open ratio of photosystem II (qL) and photosynthetically active radiation (PAR), then estimate the electron transport rate (ETR) based on the proposed mechanistic relationship between SIF and ETR. Finally, V_cmax and gs were estimated by linking to ETR based on the principle of evolutionary optimality and the photosynthetic pathway. Validation with field observations showed that our proposed model can estimate V_cmax and gs with high accuracy (R² > 0.8). Compared to simple linear regression model, the proposed model could increase the accuracy of V_cmax estimates by >40%. Therefore, the proposed method effectively enhanced the estimation accuracy of crops' functional traits, which sheds new light on developing high-throughput monitoring techniques to estimate plant functional traits, and also can improve our understating of crops' physiological response to climate change.

Transcriptomics and Metabolomics Analysis Provides Insight into Leaf Color and Photosynthesis Variation of the Yellow-Green Leaf Mutant of Hami Melon (Cucumis melo L.)

Leaf color mutants are ideal materials for studying the regulatory mechanism of chloroplast development and photosynthesis. We isolated a cucumis melo spontaneous mutant (MT), which showed yellow-green leaf phenotype in the whole growing period and could be inherited stably. We compared its leaves with the wild type (WT) in terms of cytology, physiology, transcriptome and metabolism. The results showed that the thylakoid grana lamellae of MT were loosely arranged and fewer in number than WT. Physiological experiments also showed that MT had less chlorophyll content and more accumulation of reactive oxygen species (ROS) than WT. Furthermore, the activity of several key enzymes in C₄ photosynthetic carbon assimilation pathway was more enhanced in MT than WT. Transcriptomic and metabolomic analyses showed that differential expression genes and differentially accumulated metabolites in MT were mainly co-enriched in the pathways related to photosystem-antenna proteins, central carbon metabolism, glutathione metabolism, phenylpropanoid biosynthesis and flavonoid metabolism. We also analyzed several key proteins in photosynthesis and chloroplast transport by Western blot. In summary, the results may provide a new insight into the understanding of how plants respond to the impaired photosynthesis by regulating chloroplast development and photosynthetic carbon assimilation pathways.

TSWIFT: Tower Spectrometer on Wheels for Investigating Frequent Timeseries for high-throughput phenotyping of vegetation physiology

BACKGROUND

Remote sensing instruments enable high-throughput phenotyping of plant traits and stress resilience across scale. Spatial (handheld devices, towers, drones, airborne, and satellites) and temporal (continuous or intermittent) tradeoffs can enable or constrain plant science applications. Here, we describe the technical details of TSWIFT (Tower Spectrometer on Wheels for Investigating Frequent Timeseries), a mobile tower-based hyperspectral remote sensing system for continuous monitoring of spectral reflectance across visible-near infrared regions with the capacity to resolve solar-induced fluorescence (SIF).

RESULTS

We demonstrate potential applications for monitoring short-term (diurnal) and long-term (seasonal) variation of vegetation for high-throughput phenotyping applications. We deployed TSWIFT in a field experiment of 300 common bean genotypes in two treatments: control (irrigated) and drought (terminal drought). We evaluated the normalized difference vegetation index (NDVI), photochemical reflectance index (PRI), and SIF, as well as the coefficient of variation (CV) across the visible-near infrared spectral range (400 to 900 nm). NDVI tracked structural variation early in the growing season, following initial plant growth and development. PRI and SIF were more dynamic, exhibiting variation diurnally and seasonally, enabling quantification of genotypic variation in physiological response to drought conditions. Beyond vegetation indices, CV of hyperspectral reflectance showed the most variability across genotypes, treatment, and time in the visible and red-edge spectral regions.

CONCLUSIONS

TSWIFT enables continuous and automated monitoring of hyperspectral reflectance for assessing variation in plant structure and function at high spatial and temporal resolutions for high-throughput phenotyping. Mobile, tower-based systems like this can provide short- and long-term datasets to assess genotypic and/or management responses to the environment, and ultimately enable the spectral prediction of resource-use efficiency, stress resilience, productivity and yield.

Antagonistic impact on cadmium stress in alfalfa supplemented with nano-zinc oxide and biochar via upregulating metal detoxification

Eco-toxicological estimation of cadmium induced damages by morpho-physiological and cellular response could be an insightful strategy to alleviate negative impact of Cd in agricultural crops. The current study revealed novel patterns of Cd-bioaccumulation and cellular mechanism opted by alfalfa to acquire Cd tolerance under various soil applied zinc oxide nanoparticles (nZnO) doses (0, 30, 60, 90 mg kg^-1), combined with 2% biochar (BC). Herein, the potential impact of these soil amendments was justified by decreased Cd and increased Zn-bioaccumulation into roots by 38 % and 48 % and shoots by 51 % and 72 % respectively, with co-exposure of nZnO with BC. As, the transmission electron microscopy (TEM) and scanning electron microscopy and energy dispersive spectroscopy (SEM-EDS) ultrastructural observations confirmed that Cd-exposure induced stomatal closure, and caused damage to roots and leaves ultrastructure as compared to the control group. On the contrary, the damages to the above-mentioned traits were reversed by a higher nZnO dose, and the impact was further aggravated by adding BC along nZnO. Furthermore, higher nZnO and BC levels efficiently alleviated the Cd-mediated reductions in alfalfa biomass, antioxidant enzymatic response, and gaseous exchange traits than control. Overall, soil application of 90 mg kg^-1 nZnO with BC (2 %) was impactful in averting Cd stress damages and ensuring better plant performance. Thereby, applying soil nZnO and BC emerge as promising green remediation techniques to enhance crop tolerance in Cd-polluted soil.

Improved air quality leads to enhanced vegetation growth during the COVID-19 lockdown in India

The direct effect of pandemic induced lockdown (LD) on environment is widely explored, but its secondary impacts remain largely unexplored. Therefore, we assess the response of surface greenness and photosynthetic activity to the LD-induced improvement of air quality in India. Our analysis reveals a significant improvement in air quality marked by reduced levels of aerosols (AOD, -19.27%) and Particulate Matter (PM 2.5, -23%) during LD (2020)from pre-LD (March-September months for the period 2017-2019). The vegetation exhibits a positive response, reflected by the increase in surface greenness [Enhanced Vegetation Index (EVI, +10.4%)] and photosynthetic activity [Solar Induced Fluorescence (SiF, +11%)], during LD from pre-LD that coincides with two major agricultural seasons of India; Zaid (March-May) and Kharif (June-September). In addition, the croplands show a higher response [two-fold in EVI (14.45%) and four-fold in SiF (17.7%)] than that of forests. The prolonged growing period (phenology) and high rate of photosynthesis (intensification) led to the enhanced greening during LD owing to the reduced atmospheric pollution. This study, therefore, provides new insights into the response of vegetation to the improved air quality, which would give ideas to counter the challenges of food security in the context of climate pollution, and combat global warming by more greening.

Optimality principles explaining divergent responses of alpine vegetation to environmental change

Recent increases in vegetation greenness over much of the world reflect increasing CO₂ globally and warming in cold areas. However, the strength of the response to both CO₂ and warming in those areas appears to be declining for unclear reasons, contributing to large uncertainties in predicting how vegetation will respond to future global changes. Here, we investigated the changes of satellite-observed peak season absorbed photosynthetically active radiation (F_max ) on the Tibetan Plateau between 1982 and 2016. Although climate trends are similar across the Plateau, we identified robust divergent responses (a greening of 0.31 ± 0.14% year^-1 in drier regions and a browning of 0.12 ± 0.08% year^-1 in wetter regions). Using an eco-evolutionary optimality (EEO) concept of plant acclimation/adaptation, we propose a parsimonious modelling framework that quantitatively explains these changes in terms of water and energy limitations. Our model captured the variations in F_max with a correlation coefficient (r) of .76 and a root mean squared error of .12 and predicted the divergent trends of greening (0.32 ± 0.19% year^-1 ) and browning (0.07 ± 0.06% year^-1 ). We also predicted the observed reduced sensitivities of F_max to precipitation and temperature. The model allows us to explain these changes: Enhanced growing season cumulative radiation has opposite effects on water use and energy uptake. Increased precipitation has an overwhelmingly positive effect in drier regions, whereas warming reduces F_max in wetter regions by increasing the cost of building and maintaining leaf area. Rising CO₂ stimulates vegetation growth by enhancing water-use efficiency, but its effect on photosynthesis saturates. The large decrease in the sensitivity of vegetation to climate reflects a shift from water to energy limitation. Our study demonstrates the potential of EEO approaches to reveal the mechanisms underlying recent trends in vegetation greenness and provides further insight into the response of alpine ecosystems to ongoing climate change.

Spectral diversity of photosystem I from flowering plants

Photosystem I and II (PSI and PSII) work together to convert solar energy into chemical energy. Whilst a lot of research has been done to unravel variability of PSII fluorescence in response to biotic and abiotic factors, the contribution of PSI to in vivo fluorescence measurements has often been neglected or considered to be constant. Furthermore, little is known about how the absorption and emission properties of PSI from different plant species differ. In this study, we have isolated PSI from five plant species and compared their characteristics using a combination of optical and biochemical techniques. Differences have been identified in the fluorescence emission spectra and at the protein level, whereas the absorption spectra were virtually the same in all cases. In addition, the emission spectrum of PSI depends on temperature over a physiologically relevant range from 280 to 298 K. Combined, our data show a critical comparison of the absorption and emission properties of PSI from various plant species.

Solar-induced chlorophyll fluorescence imperfectly tracks the temperature response of photosynthesis in winter wheat

Solar-induced fluorescence (SIF) is a promising proxy for photosynthesis, but it is unclear whether it performs well in tracking the gross primary productivity (GPP) under different environmental conditions. In this study, we investigated the dynamics of the two parameters from October 2020 to June 2021 in field-grown winter wheat (Triticum aestivum) and found that the ability of SIF to track GPP was weakened at low temperatures. Accounting for the coupling of light and temperature at a seasonal scale, we found that SIF yield showed a lower temperature sensitivity and had a lower but broader optimal temperature range compared with light-use efficiency (LUE), although both SIF yield and LUE decreased in low-temperature conditions. The discrepancy between the temperature responses of SIF yield and GPP caused an increase in the ratio of SIF/GPP in winter, which indicated the variation in the relationship between them during this period. The results of our study highlight the impact of low temperature on the relationship between SIF and GPP and show the necessity of reconsidering the dynamics of energy distribution inside plants under changing environments.

Rajput V, Kumar Singh R, Guo Y, Kosolapov A, Usova E, Varduny T, Chalenko E, Yadronova O, Dmitriev P, Zaruba T. Chlorophyll fluorometry in evaluating photosynthetic performance: key limitations, possibilities, perspectives and alternatives

Non-destructive methods for the assessment of photosynthetic parameters of plants are widely applied to evaluate rapidly the photosynthetic performance, plant health, and shifts in plant productivity induced by environmental and cultivation conditions. Most of these methods are based on measurements of chlorophyll fluorescence kinetics, particularly on pulse modulation (PAM) fluorometry. In this paper, fluorescence methods are critically discussed in regard to some their possibilities and limitations inherent to vascular plants and microalgae. Attention is paid to the potential errors related to the underestimation of thylakoidal cyclic electron transport and anoxygenic photosynthesis. PAM-methods are also observed considering the color-addressed measurements. Photoacoustic methods are discussed as an alternative and supplement to fluorometry. Novel Fourier modifications of PAM-fluorometry and photoacoustics are noted as tools allowing simultaneous application of a dual or multi frequency measuring light for one sample.

Improved estimation of global gross primary productivity during 1981-2020 using the optimized P model

Accurate estimation of terrestrial gross primary productivity (GPP) is essential for quantifying the net carbon exchange between the atmosphere and biosphere. Light use efficiency (LUE) models are widely used to estimate GPP at different spatial scales. However, difficulties in proper determination of maximum LUE (LUE_max) and downregulation of LUE_max into actual LUE result in uncertainties in GPP estimated by LUE models. The recently developed P model, as a LUE-like model, captures the deep mechanism of photosynthesis and simplifies parameterization. Site level studies have proved the outperformance of P model over LUE models. However, the global application of the P model is still lacking. Thus, the effectiveness of 5 water stress factors integrated into the P model was compared. The optimal P model was used to generate a new long-term (1981-2020) global monthly GPP dataset at a spatial resolution of 0.1° × 0.1°, called PGPP. Validation at globally distributed 109 FLUXNET sites indicated that PGPP is better than three widely-used GPP products. R² between PGPP and observed GPP equals to 0.75, the corresponding root mean squared error (RMSE) and mean absolute error (MAE) equal to 1.77 g C m^-2 d^-1 and 1.28 g C m^-2 d^-1. During the period from 1981 to 2020, PGPP significantly increased in 69.02% of global vegetated regions (p < 0.05). Overall, PGPP provides a new GPP product choice for global ecology studies and the comparison of various water stress factors provides a new idea for the improvement of GPP model in the future.

Effects of Low Temperature on the Relationship between Solar-Induced Chlorophyll Fluorescence and Gross Primary Productivity across Different Plant Function Types

Solar-induced chlorophyll fluorescence (SIF) has been recognized as a proxy of gross primary production (GPP) across various terrestrial biomes. However, the effects of low temperature on SIF and GPP among different plant function types (PFTs) have not yet been well-explored. To gain a better understanding of the relationship between SIF and GPP, we investigated the variation in the GPP/SIF ratio in response to low-temperature conditions using satellite and tower-based datasets. Based on the TROPOMI SIF product and FLUXCOM GPP data, we found that the SIF and GPP exhibited consistent seasonal and spatial patterns, while the GPP/SIF ratio differed for different PFTs. The GPP/SIF ratio for forest types was generally higher than 10 gC·d−1·mw−1·nm·sr, whereas the GPP/SIF ratio for grass and crop types was generally lower than 10 gC·d−1·mw−1·nm·sr. In addition, there were noticeable differences in the seasonal pattern of the GPP/SIF ratio between the selected samples that experienced low-temperature stress (below 10 °C, defined as group A) and those that grew under relatively warm conditions (above 10 °C throughout the year, defined as group B). The GPP/SIF ratio for group A generally exhibited a “hump-shaped” seasonal pattern, and that for group B showed a slightly “bowl-shaped” seasonal pattern, which means it is important to consider the effects of temperature on the SIF-GPP relationship. Through linear regression and correlation analysis, we demonstrate that there was a positive correlation between the GPP/SIF ratio and temperature for group A, with a wide temperature range including low-temperature conditions, indicating that, in this case, temperature affected the SIF–GPP relationship; however, for group B—with a temperature higher than 10 °C throughout the year—the GPP/SIF ratio was not consistently affected by temperature. The response of GPP/SIF to low temperature stress was confirmed by tower-based observations at a C3 cropland (C3CRO) site and a boreal evergreen needleleaf forest (BoENF) site. Although the relationship between the GPP/SIF ratio and temperature differed among PFTs, the GPP/SIF ratio decreased under low-temperature conditions for PFTs. Therefore, the GPP/SIF ratio was not constant and was largely influenced by low temperature for different PFTs, thus highlighting the importance of incorporating temperature into SIF-based GPP estimation.

Exceptional heat and atmospheric dryness amplified losses of primary production during the 2020 U.S. Southwest hot drought

Earth's ecosystems are increasingly threatened by "hot drought," which occurs when hot air temperatures coincide with precipitation deficits, intensifying the hydrological, physiological, and ecological effects of drought by enhancing evaporative losses of soil moisture (SM) and increasing plant stress due to higher vapor pressure deficit (VPD). Drought-induced reductions in gross primary production (GPP) exert a major influence on the terrestrial carbon sink, but the extent to which hotter and atmospherically drier conditions will amplify the effects of precipitation deficits on Earth's carbon cycle remains largely unknown. During summer and autumn 2020, the U.S. Southwest experienced one of the most intense hot droughts on record, with record-low precipitation and record-high air temperature and VPD across the region. Here, we use this natural experiment to evaluate the effects of hot drought on GPP and further decompose those negative GPP anomalies into their constituent meteorological and hydrological drivers. We found a 122 Tg C (>25%) reduction in GPP below the 2015-2019 mean, by far the lowest regional GPP over the Soil Moisture Active Passive satellite record. Roughly half of the estimated GPP loss was attributable to low SM (likely a combination of record-low precipitation and warming-enhanced evaporative depletion), but record-breaking VPD amplified the reduction of GPP, contributing roughly 40% of the GPP anomaly. Both air temperature and VPD are very likely to continue increasing over the next century, likely leading to more frequent and intense hot droughts and substantially enhancing drought-induced GPP reductions.

Integrate the Canopy SIF and Its Derived Structural and Physiological Components for Wheat Stripe Rust Stress Monitoring

Solar-induced chlorophyll fluorescence (SIF) has great advantages in the remote sensing detection of crop stress. However, under stripe rust stress, the effects of canopy structure and leaf physiology on the variations in canopy SIF are unclear, and these influencing factors are entangled during the development of disease, resulting in an unclear coupling relationship between SIFcanopy and the severity level (SL) of disease, which affects the remote sensing detection accuracy of wheat stripe rust. In this study, the observed canopy SIF was decomposed into NIRVP, which can characterize the canopy structure, and SIFtot, which can sensitively reflect the physiological status of crops. Additionally, the main factors driving the variations in canopy SIF under different disease severities were analyzed, and the response characteristics of SIFcanopy, NIRVP, and SIFtot to SL under stripe rust stress were studied. The results showed that when the severity level (SL) of disease was lower than 20%, NIRVP was more sensitive to variation in SIFcanopy than SIFtot, and the correlation between SIFtot and SL was 6.6% higher than that of SIFcanopy. Using the decomposed SIFtot component allows one to detect the stress state of plants before variations in vegetation canopy structure and leaf area index and can realize the early diagnosis of crop diseases. When the severity level (SL) of disease was in the state of moderate incidence (20% < SL ≤ 45%), the variation in SIFcanopy was affected by both NIRVP and SIFtot, and the detection accuracy of SIFcanopy for wheat stripe rust was better than that of the NIRVP and SIFtot components. When the severity level (SL) of disease reached a severe level (SL > 45%), SIFtot was more sensitive to the variation in SIFcanopy, and NIRVP reached a highly significant level with SL, which could better realize the remote sensing detection of wheat stripe rust disease severity. The research results showed that analyzing variations in SIFcanopy by using the decomposed canopy structure and physiological response signals can effectively capture additional information about plant physiology, detect crop pathological variations caused by disease stress earlier and more accurately, and promote crop disease monitoring and research progress.

Response of vegetation ecosystems to flash drought with solar-induced chlorophyll fluorescence over the Hai River Basin, China during 2001-2019

With global climate change, frequent flash droughts have critically impacted vegetation productivity. Based on the new definition on flash drought onset and duration, the temporal and spatial evolution patterns of the flash drought over the Hai River Basin (HRB) was analysed. Among the events, the flash drought in 2019 lasted for 40 days, from the day of the year (DOY) 120 to DOY160, which was the strongest and mainly concentrated in the south-eastern part of the basin. Solar-induced chlorophyll fluorescence (SIF) and vegetation indices were used to explore the responses of different vegetation types to this flash drought. Compared to forest and grassland, the SIF and SIFyield (SIF normalized by the absorbed photosynthetically active radiation (APAR)) values of cropland were more sensitive to water losses and replenishment. By analysing different radiation conditions which would affect SIF and photosynthesis, low radiation was found altering the linear relationship between fluorescence and photosynthesis. The flash drought event caused gross primary productivity (GPP) losses in 40% of the basin and the maximum loss reached 0.16 kg C m^-2, indicating that the impact of this flash drought on vegetation productivity was quite serious. The results obtained in this work can be used to understand the mechanisms with which the vegetation photosynthetic capacity responds to flash droughts and to evaluate the impact of flash droughts on terrestrial ecosystems.

Different Responses of Solar-Induced Chlorophyll Fluorescence at the Red and Far-Red Bands and Gross Primary Productivity to Air Temperature for Winter Wheat

Solar-induced chlorophyll fluorescence (SIF) is closely related to the light-reaction process and has been recognized as a good indicator for tracking gross primary productivity (GPP). Nevertheless, it has not been widely examined how SIF and GPP respond to temperature. Here, we explored the linkage mechanisms between SIF and GPP in winter wheat based on continuous measurements of canopy SIF (cSIF), GPP, and meteorological data. To separately explore the structural and physiological mechanisms underlying the SIF–GPP relationship, we studied the temperature responses of the estimated light use efficiency (LUEp), canopy-level chlorophyll fluorescence yield (cSIFyield) and photosystem-level chlorophyll fluorescence yield (ΦF) estimated using canopy-scale remote sensing measurements. We found that GPP, red canopy SIF (cSIF688) and far-red canopy SIF (cSIF760) all exhibited a decreasing trend during overwintering periods. However, GPP and cSIF688 showed relatively more obvious changes in response to air temperature (Ta) than cSIF760 did. In addition, the LUEp responded sensitively to Ta (the correlation coefficient, r = 0.83, p-value < 0.01). The cSIFyield_688 and ΦF_688 (ΦF at 688 nm) also exhibited significantly positive correlations with Ta (r > 0.7, p-value < 0.05), while cSIFyield_760 and ΦF_760 (ΦF at 760 nm) were weakly correlated with Ta (r < 0.3, p-value > 0.05) during overwintering periods. The results also show that LUEp was more sensitive to Ta than ΦF, which caused changes in the LUEp/ΦF ratio in response to Ta. By considering the influence of Ta, the GPP estimation based on the total SIF emitted at the photosystem level (tSIF) was improved (with R2 increased by more than 0.12 for tSIF760 and more than 0.05 for tSIF688). Therefore, our results indicate that the LUEp/ΦF ratio is affected by temperature conditions and highlights that the SIF–GPP model should consider the influence of temperature.

Comparison of Vegetation Phenology Derived from Solar-Induced Chlorophyll Fluorescence and Enhanced Vegetation Index, and Their Relationship with Climatic Limitations

Satellite-based vegetation datasets enable vegetation phenology detection at large scales, among which Solar-Induced Chlorophyll Fluorescence (SIF) and Enhanced Vegetation Index (EVI) are widely used proxies for detecting phenology from photosynthesis and greenness perspectives, respectively. Recent studies have revealed the divergent performances of SIF and EVI for estimating different phenology metrics, i.e., the start of season (SOS) and the end of season (EOS); however, the underlying mechanisms are unclear. In this study, we compared the SOS and EOS of natural ecosystems derived from SIF and EVI in China and explored the underlying mechanisms by investigating the relationships between the differences of phenology derived from SIF and EVI and climatic limiting factors (i.e., temperature, water and radiation). The results showed that the differences between phenology generated using SIF and EVI were diverse in space, which had a close relationship with climatic limitations. The increasing climatic limitation index could result in larger differences in phenology from SIF and EVI for each dominant climate-limited area. The phenology extracted using SIF was more correlated with climatic limiting factors than that using EVI, especially in water-limited areas, making it the main cause of the difference in phenology from SIF and EVI. These findings highlight the impact of climatic limitation on the differences of phenology from SIF and EVI and improve our understanding of land surface phenology from greenness and photosynthesis perspectives.

Nanosized zinc oxide (n-ZnO) particles pretreatment to alfalfa seedlings alleviate heat-induced morpho-physiological and ultrastructural damages

Global efforts are in rapid progress to tackle the emerging conundrum of climate change-induced heat stress in grassland ecosystems. Zinc oxide nanoparticles (n-ZnO) are known to play a crucial role in plants' abiotic stress regulation, but its response in alfalfa against heat stress has not been explored. This study aimed at assessing the effects of n-ZnO on alfalfa under heat stress by various morpho-physiological and cellular approaches. Five-week-old alfalfa seedlings were subjected to foliar application of n-ZnO as a pretreatment before the onset of heat stress (BHS) to evaluate its effect on heat tolerance, and as a post-treatment after heat stress (AHS) to evaluate recovery efficiency. In vitro studies on Zn release from n-ZnO by Inductively coupled plasma mass spectroscopy (ICPMS) disclosed that the particle uptake and Zn release were concentration dependent. The uptake and translocation of n-ZnO examined by transmission electron microscope (TEM) reveling showed that n-ZnO was primarily localized in the vacuoles and chloroplasts. TEM images showed that ultrastructural modifications to chloroplast, mitochondria, and cell wall were reversible by highest dose of n-ZnO applied before heat stress, and damages to these organelles were not recoverable when n-ZnO was applied after heat stress. The results further enlightened that 90 mg L^-1 n-ZnO better prevented the heat stress-mediated membrane damage, lipid peroxidation and oxidative stress by stimulating antioxidant systems and enhancing osmolyte contents in both BHS and AHS. Although, application of 90 mg L^-1 n-ZnO in BHS was more effective in averting heat-induced damages and maintaining better plant growth and morpho-physiological attributes compared to AHS. Conclusively, foliar application of n-ZnO can be encouraged as an effective strategy to protect alfalfa from heat stress damages while minimizing the risk of nanoparticle transmission to environmental compartments, which could happen with soil application.

Investigating the Performance of Red and Far-Red SIF for Monitoring GPP of Alpine Meadow Ecosystems

Alpine meadow ecosystems are extremely vulnerable to climate change and serve an essential function in terrestrial carbon sinks. Accurately estimating their gross primary productivity (GPP) is essential for understanding the global carbon cycle. Solar-induced chlorophyll fluorescence (SIF), as a companion product directly related to plant photosynthesis process, has become an attractive pathway for estimating GPP accurately. To date, the quantitative SIF-GPP relationship in terrestrial ecosystems is not yet clear. Especially, red SIF and far-red SIF present differences in their ability to track GPP under different environmental conditions. In this study, we investigated the performance of SIF at both red and far-red band in monitoring the GPP of an alpine meadow ecosystem based on continuous tower-based observations in 2019 and 2020. The results show that the canopy red SIF (SIFRed) and far-red SIF (SIFFar-red) were both strongly correlated with GPP. SIFRed was comparable to SIFFar-red for monitoring GPP based on comparisons of both half-hourly averaged and daily averaged datasets. Moreover, the relationship between SIFRed and GPP was linearly correlated, while the relationship between SIFFar-red and GPP tended to be nonlinear. At a diurnal scale, dramatic changes in photosynthetically active radiation (PAR), air temperature (Ta), and vapor pressure deficit (VPD) all had effects on the slope of the linear fitted line with zero intercept for SIFRed-GPP and SIFFar-red-GPP, and the effect on the slope of the linear fitted line with zero intercept for SIFFar-red-GPP was obviously stronger than that for SIFRed-GPP. PAR was the dominant factor among the three environmental factors in determining the diurnal variation of the slope of SIF-GPP. At a seasonal scale, the SIFFar-red/GPP was susceptible to PAR, Ta, and VPD, while the SIFRed/GPP remained relatively stable at different levels of Ta and VPD, and it was only weakly affected by PAR, suggesting that SIFRed was more consistent than SIFFar-red with GPP in response to seasonal variations in environmental factors. These results indicate that SIFRed has more potential than SIFFar-red for monitoring the GPP of alpine meadow ecosystems and can also assist researchers in gaining a more comprehensive understanding of the diversity of SIF-GPP relationships in different ecosystems.

Remote Sensing of Instantaneous Drought Stress at Canopy Level Using Sun-Induced Chlorophyll Fluorescence and Canopy Reflectance

Climate change amplifies the intensity and occurrence of dry periods leading to drought stress in vegetation. For monitoring vegetation stresses, sun-induced chlorophyll fluorescence (SIF) observations are a potential game-changer, as the SIF emission is mechanistically coupled to photosynthetic activity. Yet, the benefit of SIF for drought stress monitoring is not yet understood. This paper analyses the impact of drought stress on canopy-scale SIF emission and surface reflectance over a lettuce and mustard stand with continuous field spectrometer measurements. Here, the SIF measurements are linked to the plant’s photosynthetic efficiency, whereas the surface reflectance can be used to monitor the canopy structure. The mustard canopy showed a reduction in the biochemical component of its SIF emission (the fluorescence emission efficiency at 760 nm—ϵ760) as a reaction to drought stress, whereas its structural component (the Fluorescence Correction Vegetation Index—FCVI) barely showed a reaction. The lettuce canopy showed both an increase in the variability of its surface reflectance at a sub-daily scale and a decrease in ϵ760 during a drought stress event. These reactions occurred simultaneously, suggesting that sun-induced chlorophyll fluorescence and reflectance-based indices sensitive to the canopy structure provide complementary information. The intensity of these reactions depend on both the soil water availability and the atmospheric water demand. This paper highlights the potential for SIF from the upcoming FLuorescence EXplorer (FLEX) satellite to provide a unique insight on the plant’s water status. At the same time, data on the canopy reflectance with a sub-daily temporal resolution are a promising additional stress indicator for certain species.

Advances in field-based high-throughput photosynthetic phenotyping

Gas exchange techniques revolutionized plant research and advanced understanding, including associated fluxes and efficiencies, of photosynthesis, photorespiration, and respiration of plants from cellular to ecosystem scales. These techniques remain the gold standard for inferring photosynthetic rates and underlying physiology/biochemistry, although their utility for high-throughput phenotyping (HTP) of photosynthesis is limited both by the number of gas exchange systems available and the number of personnel available to operate the equipment. Remote sensing techniques have long been used to assess ecosystem productivity at coarse spatial and temporal resolutions, and advances in sensor technology coupled with advanced statistical techniques are expanding remote sensing tools to finer spatial scales and increasing the number and complexity of phenotypes that can be extracted. In this review, we outline the photosynthetic phenotypes of interest to the plant science community and describe the advances in high-throughput techniques to characterize photosynthesis at spatial scales useful to infer treatment or genotypic variation in field-based experiments or breeding trials. We will accomplish this objective by presenting six lessons learned thus far through the development and application of proximal/remote sensing-based measurements and the accompanying statistical analyses. We will conclude by outlining what we perceive as the current limitations, bottlenecks, and opportunities facing HTP of photosynthesis.

Formulation of a Structural Equation Relating Remotely Sensed Electron Transport Rate Index to Photosynthesis Activity

Chlorophyll fluorescence can be remotely sensed in open fields via the Fraunhofer atmospheric absorption lines of oxygen and is termed Solar-Induced Fluorescence (SIF). SIF has been extensively related to carbon assimilation at global ecology scale and was interpreted as electron transport rate. However, SIF was shown to be unrelated directly to carbon assimilation at finer-scale resolution and may be related to other photosynthetic processes, such as non-photochemical quenching. This raises the question how exactly the SIF relates to actual photosynthetic activity. Based on a recently introduced spectral index that relates the photochemical fraction of SIF to the actual electron transport rate, this study presents the formulation of a structural equation, relating the remotely sensed electron transport rate index to fluorescence yield which considers the various fates of energetic quanta and electron excitation. The proposed structural equations are used to examine and interpret the relation between the novel spectral index and seasonal growth of corn (Z. mays Sh2, ‘super sweet’) on a platform of fertilization concentration gradient. Potential uses, practical and theoretical, for the proposed structural equations are discussed.

A global 0.05° dataset for gross primary production of sunlit and shaded vegetation canopies from 1992 to 2020

Distinguishing gross primary production of sunlit and shaded leaves (GPP_sun and GPP_shade) is crucial for improving our understanding of the underlying mechanisms regulating long-term GPP variations. Here we produce a global 0.05°, 8-day dataset for GPP, GPP_shade and GPP_sun over 1992–2020 using an updated two-leaf light use efficiency model (TL-LUE), which is driven by the GLOBMAP leaf area index, CRUJRA meteorology, and ESA-CCI land cover. Our products estimate the mean annual totals of global GPP, GPP_sun, and GPP_shade over 1992–2020 at 125.0 ± 3.8 (mean ± std) Pg C a⁻¹, 50.5 ± 1.2 Pg C a⁻¹, and 74.5 ± 2.6 Pg C a⁻¹, respectively, in which EBF (evergreen broadleaf forest) and CRO (crops) contribute more than half of the totals. They show clear increasing trends over time, in which the trend of GPP (also GPP_sun and GPP_shade) for CRO is distinctively greatest, and that for DBF (deciduous broadleaf forest) is relatively large and GPP_shade overwhelmingly outweighs GPP_sun. This new dataset advances our in-depth understanding of large-scale carbon cycle processes and dynamics.

Measurement(s)	gross primary production of sunlit and shaded vegetation canopies
Technology Type(s)	the revised TL-LUE model
Sample Characteristic - Environment	carbon cycling
Sample Characteristic - Location	terrestrial ecosystem