Mechanistic evidence for tracking the seasonality of photosynthesis with solar-induced fluorescence

Seasonal timing of fluorescence and photosynthetic yields at needle and canopy scales in evergreen needleleaf forests

The seasonal timing and magnitude of photosynthesis in evergreen needleleaf forests (ENFs) has major implications for the carbon cycle and is increasingly sensitive to changing climate. Earlier spring photosynthesis can increase carbon uptake over the growing season or cause early water reserve depletion that leads to premature cessation and increased carbon loss. Determining the start and the end of the growing season in ENFs is challenging due to a lack of field measurements and difficulty in interpreting satellite data, which are impacted by snow and cloud cover, and the pervasive "greenness" of these systems. We combine continuous needle-scale chlorophyll fluorescence measurements with tower-based remote sensing and gross primary productivity (GPP) estimates at three ENF sites across a latitudinal gradient (Colorado, Saskatchewan, Alaska) to link physiological changes with remote sensing signals during transition seasons. We derive a theoretical framework for observations of solar-induced chlorophyll fluorescence (SIF) and solar intensity-normalized SIF (SIFrelative) under snow-covered conditions, and show decreased sensitivity compared with reflectance data (~20% reduction in measured SIF vs. ~60% reduction in near-infrared vegetation index [NIRv] under 50% snow cover). Needle-scale fluorescence and photochemistry strongly correlated (r2 = 0.74 in Colorado, 0.70 in Alaska) and showed good agreement on the timing and magnitude of seasonal transitions. We demonstrate that this can be scaled to the site level with tower-based estimates of LUEP and SIFrelative which were well correlated across all sites (r2 = 0.70 in Colorado, 0.53 in Saskatchewan, 0.49 in Alaska). These independent, temporally continuous datasets confirm an increase in physiological activity prior to snowmelt across all three evergreen forests. This suggests that data-driven and process-based carbon cycle models which assume negligible physiological activity prior to snowmelt are inherently flawed, and underscores the utility of SIF data for tracking phenological events. Our research probes the spectral biology of evergreen forests and highlights spectral methods that can be applied in other ecosystems.

Physiological dynamics dominate the relationship between solar-induced chlorophyll fluorescence and gross primary productivity along the nitrogen gradient in cropland

Solar-induced chlorophyll fluorescence (SIF) has been found to be robustly correlated with gross primary productivity (GPP) based on satellite datasets. However, it is unclear whether nitrogen affects the relationship between SIF and GPP at the canopy scale. Here, seasonal dynamics of SIF, GPP, vegetation physiology and canopy structure were measured synchronously throughout growing season along the nitrogen gradient in a rice paddy of China's subtropical region. Our results found that the slope of SIF against GPP was not constant, showing an increasing trend from low to high nitrogen levels. The sensitivity of SIF to nitrogen was larger than that of GPP. Nitrogen enrichment versus deficiency had asymmetrical effects on the SIF-GPP relationship. The steeper slope of SIF against GPP under high nitrogen level was mainly attributed to the promotion of canopy fluorescence efficiency (ΦF) rather than the variation of canopy fluorescence escape probability (Fesc). These results emphasize the vital role of nitrogen in exploring mechanisms underlying SIF dynamics and decoding GPP from SIF.

Methodological advancement in deriving primary productivity and ecosystem respiration fluxes across different biomes

In this paper, we introduce a methodology that can improve the estimations of Gross Primary Productivity (GPP) and ecosystem Respiration (Reco) processes at a regional scale. This method is based on a satellite data-driven approach which is suitable for regions like India where there exists a serious shortage of ground-based observations of biospheric carbon fluxes (e.g., Eddy Covariance (EC) flux measurements). We relied on the Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance for capturing vegetation dynamics in the Light-Use Efficiency (LUE)-based vegetation model. Further, we utilised recently available satellite-based Solar-Induced Fluorescence (SIF) and other variables such as Soil Moisture (SM) and Soil Temperature (ST) to refine the predictions of GPP and Reco. The methodology involves establishing a relationship between SIF and GPP for different vegetation classes over India. The SIF-GPP relationship established across the biomes was then used to correct the GPP fluxes simulated by the LUE-based model. Similarly, the ecosystem respiration estimations by the model have undergone refinement by incorporating ST and SM information. This innovative method shows remarkable potential to improve biospheric CO2 uptake and release, especially for in situ data-constrained regions like India. • SIF-based information is introduced to a light-use efficiency-based vegetation model. • SIF-GPP relationship is established for major biomes across India. • SM and ST information is incorporated into the Reco simulations in the model.

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.

Canopeo app as image-based phenotyping tool in controlled environment utilizing Arabidopsis mutants

Canopeo app was developed as a simple, accurate, rapid, and free tool to analyze ground cover fraction (GCF) from red-green-blue (RGB) images and videos captured in the field. With increasing interest in tools for plant phenotyping in controlled environments, the usefulness of Canopeo to identify differences in growth among Arabidopsis thaliana mutants in a controlled environment were explored. A simple imaging system was used to compare Arabidopsis mutants based on the FLAVIN-BINDING, KELCH REPEAT, F-BOX-1 (FKF1) mutation, which has been identified with increased biomass accumulation. Two FKF1 lines such as null expression (fkf1-t) and overexpression (FKF1-OE) lines were used along with wild type (Col-0). Canopeo was used to phenotype plants, based on biomass estimations. Under long-day photoperiod, fkf1-t had increased cellulose biosynthesis, and therefore biomass. Resource partitioning favored seedling vigor and delayed onset of senescence. In contrast, FKF1-OE illustrated a determinative growth habit where plant resources are primarily allocated for seed production. This study demonstrates the use of Canopeo for model plants and highlights its potential for phenotyping broadleaved crops in controlled environments. The value of adapting Canopeo for lab use is those with limited experience and resources have access to phenotyping methodology that is simple, accessible, accurate, and cost-efficient in a controlled environment setting.

Detecting drought stress occurrence using synergies between Sun induced fluorescence and vegetation surface temperature spatial records

Drought stress occurrence and recovery from drought can be detected using a single spatial set of simultaneous observations of SIF and canopy temperature records. Temporal and spatial responses to drought and heat stresses by plant stands of a drought-adapted diverse grassland ecosystem were studied using sun induced fluorescence (SIF,O2A and O2B bands) and further ecophysiological (canopy temperature (Tsurf), spatially modeled evapotranspiration, vegetation reflectance spectra) variables collected along spatial sampling grids while also utilizing eddy covariance measured carbon dioxide (net ecosystem exchange: NEE, gross primary production: GPP) and water flux (evapotranspiration: ET) data. The grids were of 0.5 and 5 ha spatial extents and contained 78 sampling points. Data were collected in four spatial sampling campaigns, two under drought (early summer) and another two during and after recovery (midsummer) at both spatial resolutions. Small values of spatial SIF_A averages (around 0.5 mW m-2 nm-1 sr-1) under strong early summer drought increased (to around 2 mW m-2 nm-1 sr-1) due recovery upon rain arrivals, showing high (R2: 0.8-0.88) positive temporal correlations to eddy covariance measured carbon (GPP, NEE) and water (ET) fluxes. Spatial averages of LAI, vegetation indices (NDVI, NIRv) and modeled ET followed similar temporal patterns. While SIF was depressed by drought, it showed higher values in high canopy temperature vegetation patches than in vegetation patches with lower Tsurf. The spatial pattern of higher SIF in higher Tsurf patches was persistent (2 weeks) under drought. The positive SIF_A-Tsurf spatial correlation turned into negative/not significant after recovery of the grassland from the drought, while hot summer weather persisted. It is proposed that, by using a single set of simultaneously measured spatial SIF and Tsurf data it is possible to infer whether the studied vegetation is under drought (and heat) stress while it could not be decided on the base of SIF data alone. Evaluation of the slope of the above relationship seems therefore beneficial before e.g. starting the (stress) classification procedure based on SIF.

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.

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.

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.

Quantifying Chilling Injury on the Photosynthesis System of Strawberries: Insights from Photosynthetic Fluorescence Characteristics and Hyperspectral Inversion

Chilling injury can adversely affect strawberry bud differentiation, pollen vitality, fruit yield, and quality. Photosynthesis is a fundamental process that sustains plant life. However, different strawberry varieties exhibit varying levels of cold adaptability. Quantitatively evaluating the physiological activity of the photosynthetic system under low-temperature chilling injury remains a challenge. In this study, we investigated the effects of different levels of chilling stress on twenty photosynthetic fluorescence parameters in strawberry plants, using short-day strawberry variety "Toyonoka" and day-neutral variety "Selva" as representatives. Three dynamic chilling treatment levels (20/10 °C, 15/5 °C, and 10/0 °C) and three durations (3 days, 6 days, and 9 days) were applied to each variety. WUE, LCP, Y(II), qN, SIFO2-B and rSIFO2-B were selected as crucial indicators of strawberry photosynthetic physiological activity. Subsequently, we constructed a comprehensive score to assess the strawberry photosynthetic system under chilling injury and established a hyperspectral inversion model for stress quantification. The results indicate that the short-day strawberry "Toyonoka" exhibited a recovery effect under continuous 20/10 °C treatment, while the day-neutral variety "Selva" experienced progressively worsening stress levels across all temperature groups, with stress severity higher than that in "Toyonoka". The BPNN model for the comprehensive assessment of the strawberry photosynthetic system under chilling injury showed optimal performance. It achieved a stress level prediction accuracy of 71.25% in 80 validation samples, with an R2 of 0.682 when fitted to actual results. This study provides scientific insights for the application of canopy remote sensing diagnostics of strawberry photosynthetic physiological chilling injury in practical agricultural production.

Widespread and complex drought effects on vegetation physiology inferred from space

The response of vegetation physiology to drought at large spatial scales is poorly understood due to a lack of direct observations. Here, we study vegetation drought responses related to photosynthesis, evaporation, and vegetation water content using remotely sensed data, and we isolate physiological responses using a machine learning technique. We find that vegetation functional decreases are largely driven by the downregulation of vegetation physiology such as stomatal conductance and light use efficiency, with the strongest downregulation in water-limited regions. Vegetation physiological decreases in wet regions also result in a discrepancy between functional and structural changes under severe drought. We find similar patterns of physiological drought response using simulations from a soil-plant-atmosphere continuum model coupled with a radiative transfer model. Observation-derived vegetation physiological responses to drought across space are mainly controlled by aridity and additionally modulated by abnormal hydro-meteorological conditions and vegetation types. Hence, isolating and quantifying vegetation physiological responses to drought enables a better understanding of ecosystem biogeochemical and biophysical feedback in modulating climate change.

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.

Synchronous Changes of GPP and Solar-Induced Chlorophyll Fluorescence in a Subtropical Evergreen Coniferous Forest

Using in situ near-surface observations of solar-induced chlorophyll fluorescence (SIF) and gross primary productivity (GPP) of a subtropical evergreen coniferous forest in southern China, this study analyzed the dynamics of SIF, GPP and their environmental responses, and explored the potential of SIF in characterizing the variation of GPP. The results showed that SIF and GPP have similar diurnal and seasonal variation and both reach the highest value in summer, indicating that the SIF can be applied to indicate the seasonal variation of GPP for the subtropical evergreen co-niferous. With the increase in temporal scale, the correlation between SIF and GPP becomes more linear. The diurnal variations of both SIF and GPP were characterized by photosynthetically active radiation (PAR), the seasonal variations of SIF and GPP were influenced by air temperature (Ta) and PAR. Probably due to the absent of drought stress during the study period, no significant correlation was detected between soil water content (SWC) and either SIF or GPP. With the in-crease in Ta, PAR or SWC, the linear correlation between the SIF and GPP gradually decreased, and when Ta or PAR was relatively higher, the correlation between SIF and GPP become weakly. Further research is still needed to illustrate the relationship between SIF and GPP under drought condition which occurred frequently in this region based on longer observation.

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.

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.

It's only natural: Plant respiration in unmanaged systems

Respiration plays a key role in the terrestrial carbon cycle and is a fundamental metabolic process in all plant tissues and cells. We review respiration from the perspective of plants that grow in their natural habitat and how it is influenced by wide-ranging elements at different scales, from metabolic substrate availability to shifts in climate. Decades of field-based measurements have honed our understanding of the biological and environmental controls on leaf, root, stem, and whole-organism respiration. Despite this effort, there remain gaps in our knowledge within and across species and ecosystems, especially in more challenging-to-measure tissues like roots. Recent databases of respiration rates and associated leaf traits from species representing diverse biomes, plant functional types, and regional climates have allowed for a wider-lens view at modeling this important CO2 flux. We also re-analyze published data sets to show that maximum leaf respiration rates (Rmax) in species from around the globe are related both to leaf economic traits and environmental variables (precipitation and air temperature), but that root respiration does not follow the same latitudinal trends previously published for leaf data. We encourage the ecophysiological community to continue to expand their study of plant respiration in tissues that are difficult to measure and at the whole plant and ecosystem levels to address outstanding questions in the field.

Warming and drought weaken the carbon sink capacity of an endangered paleoendemic temperate rainforest in South America

Measurements of ecosystem carbon (C) fluxes in temperate forests are concentrated in the Northern Hemisphere, leaving the functionally diverse temperate forests in the Southern Hemisphere underrepresented. Here, we report three years (February 2018-January 2021) of C fluxes, studied with eddy-covariance and closed chamber techniques, in an endangered temperate evergreen rainforest of the long-lived paleoendemic South American conifer Fitzroya cupressoides. Using classification and regression trees we analyzed the most relevant drivers and thresholds of daily net ecosystem exchange (NEE) and soil respiration. The annual NEE showed that the forest was a moderate C sink during the period analyzed (-287±38 g C m^-2 year ^-1). We found that the capacity to capture C of the Fitzroya rainforests in the Coastal Range of southern Chile is optimal under cool and rainy conditions in the early austral spring (October-November) and decreases rapidly towards the summer dry season (January-February) and autumn. Although the studied forest type has a narrow geographical coverage, the gross primary productivity measured at the tower was highly representative of Fitzroya and other rainforests in the region. Our results suggest that C fluxes in paleoendemic cool F. cupressoides forests may be negatively affected by the warming and drying predicted by climate change models, reinforcing the importance of maintaining this and other long-term ecological research sites in the Southern Hemisphere.

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.

Construction and application of Optimized Comprehensive Drought Index based on lag time: A case study in the middle reaches of Yellow River Basin, China

Drought is a complex and dynamic natural phenomenon. A single drought index can hardly reflect the multi-type characteristics of drought, and comprehensive drought indices that incorporate data from multiple sources have been proposed recently. In this study, an Optimized Comprehensive Drought Index (OCDI) was constructed by taking into account the lag time of meteorological drought, agricultural drought and hydrological drought. The Standardized Precipitation Evapotranspiration Index (SPEI), Vegetation Condition Index (VCI), and Water Storage Deficit Index (WSDI) represented the three types of droughts, respectively. Specifically, we used the Solar-induced Chlorophyll Fluorescence (SIF) to characterize the vegetation condition instead of the Normalized Difference Vegetation Index (NDVI). The application results of the proposed drought index in the middle reaches of Yellow River basin (MRYRB) showed that the lag time of different types of drought indices had seasonal differences, with a shorter lag time in summer (0-4 months) and a longer lag time in winter and spring (> 4 months). For typical drought events, the drought intensity and duration identified by OCDI were compatible with the drought evolution characteristics and consistent with the historical records, therefore, OCDI is more suitable for drought monitoring in the study area. Based on the monitoring results of the OCDI, the average number of droughts in the MRYRB was 16 times, with a duration of 2.8 months and an average drought intensity of 0.28 (at moderate drought grade). Drought times and intensity were higher in the northwestern part of the study area, and spring was a high-frequency period for drought occurrences.

Vertical gradients in photosynthetic physiology diverge at the latitudinal range extremes of white spruce

Light availability drives vertical canopy gradients in photosynthetic functioning and carbon (C) balance, yet patterns of variability in these gradients remain unclear. We measured light availability, photosynthetic CO2  and light response curves, foliar C, nitrogen (N) and pigment concentrations, and the photochemical reflectance index (PRI) on upper and lower canopy needles of white spruce trees (Picea glauca) at the species' northern and southern range extremes. We combined our photosynthetic data with previously published respiratory data to compare and contrast canopy C balance between latitudinal extremes. We found steep canopy gradients in irradiance, photosynthesis and leaf traits at the southern range limit, but a lack of variation across canopy positions at the northern range limit. Thus, unlike many tree species from tropical to mid-latitude forests, high latitude trees may not require vertical gradients of metabolic activity to optimize photosynthetic C gain. Consequently, accounting for self-shading is less critical for predicting gross primary productivity at northern relative to southern latitudes. Northern trees also had a significantly smaller net positive leaf C balance than southern trees suggesting that, regardless of canopy position, low photosynthetic rates coupled with high respiratory costs may ultimately constrain the northern range limit of this widely distributed boreal 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.

Responses of Forest Net Primary Productivity to Climatic Factors in China during 1982-2015

Forest ecosystems play an important role in the global carbon cycle. Clarifying the large-scale dynamics of net primary productivity (NPP) and its correlation with climatic factors is essential for national forest ecology and management. Hence, this study aimed to explore the effects of major climatic factors on the Carnegie−Ames−Stanford Approach (CASA) model-estimated NPP of the entire forest and all its corresponding vegetation types in China from 1982 to 2015. The spatiotemporal patterns of interannual variability of forest NPP were illustrated using linear regression and geographic information system (GIS) spatial analysis. The correlations between forest NPP and climatic factors were evaluated using partial correlation analysis and sliding correlation analysis. We found that over thirty years, the average annual NPP of the forests was 887 × 1012 g C/a, and the average annual NPP per unit area was 650.73 g C/m2/a. The interannual NPP of the entire forest and all its corresponding vegetation types significantly increased (p < 0.01). The increase in the NPP of evergreen broad-leaved forests was markedly substantial among forest types. From the spatial perspective, the NPP of the entire forest vegetation gradually increased from northwest to southeast. Over the years, the proportions of the entire forest and all its corresponding vegetation types with a considerable increase in NPP were higher than those with a significant decrease, indicating, generally, improvements in forest NPP. We also found climatic factors variably affected the NPP of forests over time considering that the rise in temperature and solar radiation improved the interannual forest NPP, and the decline in precipitation diminished the forest NPP. Such varying strength of the relationship between the interannual forest NPP and climatic factors also varied across many forest types. Understanding the spatiotemporal pattern of forest NPP and its varying responses to climatic change will improve our knowledge to manage forest ecosystems and maintain their sustainability under a changing environment.

Flash drought early warning based on the trajectory of solar-induced chlorophyll fluorescence

Flash drought often leads to devastating effects in multiple sectors and presents a unique challenge for drought early warning due to its sudden onset and rapid intensification. Existing drought monitoring and early warning systems are based on various hydrometeorological variables reaching thresholds of unusually low water content. Here, we propose a flash drought early warning approach based on spaceborne measurements of solar-induced chlorophyll fluorescence (SIF), a proxy of photosynthesis that captures plant response to multiple environmental stressors. Instead of negative SIF anomalies, we focus on the subseasonal trajectory of SIF and consider slower-than-usual increase or faster-than-usual decrease of SIF as an early warning for flash drought onset. To quantify the deviation of SIF trajectory from the climatological norm, we adopt existing formulas for a rapid change index (RCI) and apply the RCI analysis to spatially downscaled 8-d SIF data from GOME-2 during 2007-2018. Using two well-known flash drought events identified by the operational US Drought Monitor (in 2012 and 2017), we show that SIF RCI can produce strong predictive signals of flash drought onset with a lead time of 2 wk to 2 mo and can also predict drought recovery with several weeks of lead time. While SIF RCI shows great early warning potential, its magnitude diminishes after drought onset and therefore cannot reflect the current drought intensity. With its long lead time and direct relevance for agriculture, SIF RCI can support a global early warning system for flash drought and is especially useful over regions with sparse hydrometeorological data.

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.

A long-term reconstructed TROPOMI solar-induced fluorescence dataset using machine learning algorithms

Photosynthesis is a key process linking carbon and water cycles, and satellite-retrieved solar-induced chlorophyll fluorescence (SIF) can be a valuable proxy for photosynthesis. The TROPOspheric Monitoring Instrument (TROPOMI) on the Copernicus Sentinel-5P mission enables significant improvements in providing high spatial and temporal resolution SIF observations, but the short temporal coverage of the data records has limited its applications in long-term studies. This study uses machine learning to reconstruct TROPOMI SIF (RTSIF) over the 2001-2020 period in clear-sky conditions with high spatio-temporal resolutions (0.05° 8-day). Our machine learning model achieves high accuracies on the training and testing datasets (R2 = 0.907, regression slope = 1.001). The RTSIF dataset is validated against TROPOMI SIF and tower-based SIF, and compared with other satellite-derived SIF (GOME-2 SIF and OCO-2 SIF). Comparing RTSIF with Gross Primary Production (GPP) illustrates the potential of RTSIF for estimating gross carbon fluxes. We anticipate that this new dataset will be valuable in assessing long-term terrestrial photosynthesis and constraining the global carbon budget and associated water fluxes.

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.

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.

Globally ubiquitous negative effects of nitrogen dioxide on crop growth

Nitrogen oxides (NOx) are among the most widely emitted pollutants in the world, yet their impacts on agriculture remain poorly known. NOx can directly damage crop cells and indirectly affect growth by promoting ozone (O3) and aerosol formation. We use satellite measures of both crop greenness and NOx during 2018-2020 to evaluate crop impacts for five major agricultural regions. We find consistent negative associations between NO2 and greenness across regions and seasons. These effects are strongest in conditions where O3 formation is NOx limited but remain significant even in locations where this pathway is muted, suggesting a role for direct NOx damage. Using simple counterfactuals and leveraging published relationships between greenness and growth, we estimate that reducing NOx levels to the current fifth percentile in each region would raise yields by ~25% for winter crops in China, ~15% for summer crops in China, and up to 10% in other regions.

Bridge to the future: Important lessons from 20 years of ecosystem observations made by the OzFlux network

In 2020, the Australian and New Zealand flux research and monitoring network, OzFlux, celebrated its 20th anniversary by reflecting on the lessons learned through two decades of ecosystem studies on global change biology. OzFlux is a network not only for ecosystem researchers, but also for those 'next users' of the knowledge, information and data that such networks provide. Here, we focus on eight lessons across topics of climate change and variability, disturbance and resilience, drought and heat stress and synergies with remote sensing and modelling. In distilling the key lessons learned, we also identify where further research is needed to fill knowledge gaps and improve the utility and relevance of the outputs from OzFlux. Extreme climate variability across Australia and New Zealand (droughts and flooding rains) provides a natural laboratory for a global understanding of ecosystems in this time of accelerating climate change. As evidence of worsening global fire risk emerges, the natural ability of these ecosystems to recover from disturbances, such as fire and cyclones, provides lessons on adaptation and resilience to disturbance. Drought and heatwaves are common occurrences across large parts of the region and can tip an ecosystem's carbon budget from a net CO2 sink to a net CO2 source. Despite such responses to stress, ecosystems at OzFlux sites show their resilience to climate variability by rapidly pivoting back to a strong carbon sink upon the return of favourable conditions. Located in under-represented areas, OzFlux data have the potential for reducing uncertainties in global remote sensing products, and these data provide several opportunities to develop new theories and improve our ecosystem models. The accumulated impacts of these lessons over the last 20 years highlights the value of long-term flux observations for natural and managed systems. A future vision for OzFlux includes ongoing and newly developed synergies with ecophysiologists, ecologists, geologists, remote sensors and modellers.

Heatwave breaks down the linearity between sun-induced fluorescence and gross primary production

Sun-induced fluorescence in the far-red region (SIF) is increasingly used as a remote and proximal-sensing tool capable of tracking vegetation gross primary production (GPP). However, the use of SIF to probe changes in GPP is challenged during extreme climatic events, such as heatwaves. Here, we examined how the 2018 European heatwave (HW) affected the GPP-SIF relationship in evergreen broadleaved trees with a relatively invariant canopy structure. To do so, we combined canopy-scale SIF measurements, GPP estimated from an eddy covariance tower, and active pulse amplitude modulation fluorescence. The HW caused an inversion of the photosynthesis-fluorescence relationship at both the canopy and leaf scales. The highly nonlinear relationship was strongly shaped by nonphotochemical quenching (NPQ), that is, a dissipation mechanism to protect from the adverse effects of high light intensity. During the extreme heat stress, plants experienced a saturation of NPQ, causing a change in the allocation of energy dissipation pathways towards SIF. Our results show the complex modulation of the NPQ-SIF-GPP relationship at an extreme level of heat stress, which is not completely represented in state-of-the-art coupled radiative transfer and photosynthesis models.

Representation of Leaf-to-Canopy Radiative Transfer Processes Improves Simulation of Far-Red Solar-Induced Chlorophyll Fluorescence in the Community Land Model Version 5

Recent advances in satellite observations of solar-induced chlorophyll fluorescence (SIF) provide a new opportunity to constrain the simulation of terrestrial gross primary productivity (GPP). Accurate representation of the processes driving SIF emission and its radiative transfer to remote sensing sensors is an essential prerequisite for data assimilation. Recently, SIF simulations have been incorporated into several land surface models, but the scaling of SIF from leaf-level to canopy-level is usually not well-represented. Here, we incorporate the simulation of far-red SIF observed at nadir into the Community Land Model version 5 (CLM5). Leaf-level fluorescence yield was simulated by a parametric simplification of the Soil Canopy-Observation of Photosynthesis and Energy fluxes model (SCOPE). And an efficient and accurate method based on escape probability is developed to scale SIF from leaf-level to top-of-canopy while taking clumping and the radiative transfer processes into account. SIF simulated by CLM5 and SCOPE agreed well at sites except one in needleleaf forest (R ² > 0.91, root-mean-square error <0.19 W⋅m^-2⋅sr^-1⋅μm^-1), and captured the day-to-day variation of tower-measured SIF at temperate forest sites (R ² > 0.68). At the global scale, simulated SIF generally captured the spatial and seasonal patterns of satellite-observed SIF. Factors including the fluorescence emission model, clumping, bidirectional effect, and leaf optical properties had considerable impacts on SIF simulation, and the discrepancies between simulate d and observed SIF varied with plant functional type. By improving the representation of radiative transfer for SIF simulation, our model allows better comparisons between simulated and observed SIF toward constraining GPP simulations.

Reducing model uncertainty of climate change impacts on high latitude carbon assimilation

The Arctic-Boreal Region (ABR) has a large impact on global vegetation-atmosphere interactions and is experiencing markedly greater warming than the rest of the planet, a trend that is projected to continue with anticipated future emissions of CO₂ . The ABR is a significant source of uncertainty in estimates of carbon uptake in terrestrial biosphere models such that reducing this uncertainty is critical for more accurately estimating global carbon cycling and understanding the response of the region to global change. Process representation and parameterization associated with gross primary productivity (GPP) drives a large amount of this model uncertainty, particularly within the next 50 years, where the response of existing vegetation to climate change will dominate estimates of GPP for the region. Here we review our current understanding and model representation of GPP in northern latitudes, focusing on vegetation composition, phenology, and physiology, and consider how climate change alters these three components. We highlight challenges in the ABR for predicting GPP, but also focus on the unique opportunities for advancing knowledge and model representation, particularly through the combination of remote sensing and traditional boots-on-the-ground science.

Canopy Solar-Induced Chlorophyll Fluorescence and Its Link to Transpiration in a Temperate Evergreen Needleleaf Forest during the Fall Transition

Northern hemisphere evergreen needleleaf forest (ENF) contributes a significant fraction of global water exchange but regional transpiration (T) observation in ENF ecosystems is still challenging. Traditional remote sensing techniques and terrestrial biosphere models reproduce the transpiration seasonality with difficulty, and with large uncertainties. Solar-induced chlorophyll fluorescence (SIF) emission from vegetation correlates to photosynthesis at multiple spatial and temporal scales. However, how SIF links to transpiration of evergreen forest during seasonal transition is unclear. Here, we explored the relationship between canopy SIF and T retrieved from ground observation towers in ENF. We also examined the role of meteorological and soil factors on the relationship between SIF and T. A slow decrease of SIF and T with a fast reduction in photosynthetically active radiation (PAR), air temperature, vapor pressure deficit (VPD), soil temperature and soil water content (SWC) were found in the ENF during the fall transition. The correlation between SIF and T at hourly and daily scales varied significantly among different months (Pearson correlation coefficient = 0.29–0.68, p < 0.01). SIF and T were significantly linearly correlated at hourly (R2 = 0.53, p < 0.001) and daily (R2 = 0.67, p < 0.001) timescales in the October. Air temperature and PAR were the major moderating factors for the relationship between SIF and T in the fall transition. Soil water content (SWC) influenced the SIF-T relationship at an hourly scale. Soil temperature and VPD’s effect on the SIF-T relationship was evident at a daily scale. This study can help extend the possibility of constraining ecosystem T by SIF at an unprecedented spatiotemporal resolution during season transitions.

Seasonal changes in GPP/SIF ratios and their climatic determinants across the Northern Hemisphere

Satellite-derived sun-induced chlorophyll fluorescence (SIF) has been increasingly used for estimating gross primary production (GPP). However, the relationship between SIF and GPP has not been well defined, impeding the translation of satellite observed SIF to GPP. Previous studies have generally assumed a linear relationship between SIF and GPP at daily and longer time scales, but support for this assumption is lacking. Here, we used the GPP/SIF ratio to investigate seasonal variations in the relationship between SIF and GPP over the Northern Hemisphere (NH). Based on multiple SIF products and MODIS and FLUXCOM GPP data, we found strong seasonal hump-shaped patterns for the GPP/SIF ratio over northern latitudes, with higher values in the summer than in the spring or autumn. This hump-shaped GPP/SIF seasonal variation was confirmed by examining different SIF products and was evident for most vegetation types except evergreen broadleaf forests. The seasonal amplitude of the GPP/SIF ratio decreased from the boreal/arctic region to drylands and the tropics. For most of the NH, the lowest GPP/SIF values occurred in October or September, while the maximum GPP/SIF values were evident in June and July. The most pronounced seasonal amplitude of GPP/SIF occurred in intermediate temperature and precipitation ranges. GPP/SIF was positively related to temperature in the early and late parts of the growing season, but not during the peak growing months. These shifting relationships between temperature and GPP/SIF across different months appeared to play a key role in the seasonal dynamics of GPP/SIF. Several mechanisms may explain the patterns we observed, and future research encompassing a broad range of climate and vegetation settings is needed to improve our understanding of the spatial and temporal relationships between SIF and GPP. Nonetheless, the strong seasonal variation in GPP/SIF we identified highlights the importance of incorporating this behavior into SIF-based GPP estimations.

Wide variation of winter-induced sustained thermal energy dissipation in conifers: a common-garden study

Low temperature in winter depresses rates of photosynthesis, which, in evergreen plants, can exacerbate imbalances between light absorption and photochemical light use. Damage that could result from increased excess light absorption is minimized by the conversion of excitation energy to heat in a process known as energy dissipation, which involves the de-epoxidized carotenoids of the xanthophyll cycle. Overwintering evergreens employ sustained forms of energy dissipation observable even after lengthy periods of dark acclimation. Whereas most studies of photoprotective energy dissipation examine one or a small number of species; here, we measured the levels of sustained thermal energy dissipation of seventy conifer taxa growing outdoors under common-garden conditions at the Red Butte Garden in Salt Lake City, Utah, U.S.A. (forty nine taxa were also sampled for needle pigment content). We observed an extremely wide range of wintertime engagement of sustained energy dissipation; the percentage decrease in dark-acclimated photosystem II quantum efficiency from summer to winter ranged from 6 to 95%. Of the many pigment-based parameters measured, the magnitude of the seasonal decrease in quantum efficiency was most closely associated with the seasonal increase in zeaxanthin content expressed on a total chlorophyll basis, which explained only slightly more than one-third of the variation. We did not find evidence for a consistent wintertime decrease in needle chlorophyll content. Thus, the prevailing mechanism for winter decreases in solar-induced fluorescence emitted by evergreen forests may be decreases in fluorescence quantum yield, and wintertime deployment of sustained energy dissipation likely underlies this effect.

Response to Comments on "Recent global decline of CO2 fertilization effects on vegetation photosynthesis"

[Figure: see text].

Chlorophyll a fluorescence illuminates a path connecting plant molecular biology to Earth-system science

For decades, the dynamic nature of chlorophyll a fluorescence (ChlaF) has provided insight into the biophysics and ecophysiology of the light reactions of photosynthesis from the subcellular to leaf scales. Recent advances in remote sensing methods enable detection of ChlaF induced by sunlight across a range of larger scales, from using instruments mounted on towers above plant canopies to Earth-orbiting satellites. This signal is referred to as solar-induced fluorescence (SIF) and its application promises to overcome spatial constraints on studies of photosynthesis, opening new research directions and opportunities in ecology, ecophysiology, biogeochemistry, agriculture and forestry. However, to unleash the full potential of SIF, intensive cross-disciplinary work is required to harmonize these new advances with the rich history of biophysical and ecophysiological studies of ChlaF, fostering the development of next-generation plant physiological and Earth-system models. Here, we introduce the scale-dependent link between SIF and photosynthesis, with an emphasis on seven remaining scientific challenges, and present a roadmap to facilitate future collaborative research towards new applications of SIF.

Cold Wave-Induced Reductions in NDII and ChlRE for North-Western Pacific Mangroves Varies with Latitude and Climate History

Mangrove forests growing at the poleward edges of their geographic distribution are occasionally subject to freezing (<0 °C) and cold wave (>0 °C) events. Cold wave effects on mangrove trees are well documented and adaptation to cold stress has been reported for local mangrove populations in the North Atlantic. However, there is less understanding of effects of cold waves on mangroves in the northern Pacific, especially at the regional scale. Moreover, it is unclear if cold tolerant mangrove species of North Asia display variation in resistance to cold temperatures across their geographic distribution. Using a cold wave event that occurred in January 2021, we evaluated the effects of low temperatures on vegetation index (VI) change (relative to a recent five-year baseline) for mangrove forests dominated by Kandelia obovata (Rhizophoraceae) and Avicennia marina (Acanthaceaee) at the northern edge of their geographical range. We used two VIs derived from Sentinel-2 imagery as indicators for canopy health: the normalized difference infrared index (NDII) and the chlorophyll red-edge index (ChlRE), which reflect forest canopy water content and chlorophyll concentration, respectively. We isolated the cold wave effects on the forest canopy from phenology (i.e., cold wave induced deviation from a five-year baseline) and used multiple linear regression to identify significant climatic predictors for the response of mangrove forest canopy VI change to low temperatures. For areas where the cold wave resulted in temperatures <10 °C, immediate decreases in both VIs were observed, and the VI difference relative to the baseline was generally greater at 30-days after the cold wave than when temperatures initially recovered to baseline values, showing a slight delay in VI response to cold wave-induced canopy damage. Furthermore, the two VIs did not respond consistently suggesting that cold-temperature induced changes in mangrove canopy chlorophyll and water content are affected independently or subject to differing physiological controls. Our results confirm that local baseline (i.e., recent past) climate predicts canopy resistance to cold wave damage across K. obovata stands in the northern Pacific, and in congruence with findings from New World mangroves, they imply geographic variation in mangrove leaf physiological resistance to cold for Northern Pacific mangroves.

Linking Remotely Sensed Carbon and Water Use Efficiencies with In Situ Soil Properties

The capacity of terrestrial ecosystems to sequester carbon dioxide (CO2) from the atmosphere is expected to be altered by climate change and CO2 fertilization, but this projection is limited by our understanding of how the soil system interacts with plants. Understanding the soil–vegetation interactions is essential to assess the magnitude and response of terrestrial ecosystems to the changing climate. Here, we used soil profile and satellite data to explore the role that soil properties play in regulating water and carbon use by plants. Data obtained for 19 terrestrial ecosystem sites in a warm temperate and humid climate were used to investigate the relationship between remotely sensed data and soil physical and chemical properties. Classification and regression tree results showed that in situ soil carbon isotope (δ13C), and soil order were significant predictors (r2 = 0.39, mean absolute error (MAE) = 0 of 0.175 gC/KgH2O) of remotely sensed water use efficiency (WUE) based on the Moderate Resolution Imaging Spectroradiometer (MODIS). Soil extractable calcium (Ca), and land cover type were significant predictors of remotely sensed carbon use efficiency (CUE) based on MODIS and Landsat data-(r2 = 0.64–0.78, MAE = 0.04–0.06). We used gross primary productivity (GPP) derived from solar-induced fluorescence (SIF) data, based on the Orbiting Carbon Observatory-2 (OCO-2), to calculate WUE and CUE (referred to as WUESIF and CUESIF, respectively) for our study sites. The regression tree analysis revealed that soil organic matter and soil extractable magnesium (Mg), δ13C, and soil silt content were the important predictors of both WUESIF (r2 = 0.19, MAE = 0.64 gC/KgH2O) and CUESIF (r2 = 0.45, MAE = 0.1), respectively. Our results revealed the importance of soil extractable Ca, soil carbon (S13C is a facet of soil carbon content), and soil organic matter predicting CUE and WUE. Insights gained from this study highlighted the importance of biotic and abiotic factors regulating plant and soil interactions. These types of data are timely and critical for accurate predictions of how terrestrial ecosystems respond to climate change.

Linking remote sensing parameters to CO2 assimilation rates at a leaf scale

Solar-induced chlorophyll fluorescence (SIF) and photochemical reflectance index (PRI) are expected to be useful for remote sensing of photosynthetic activity at various spatial scales. This review discusses how chlorophyll fluorescence and PRI are related to the CO₂ assimilation rate at a leaf scale. Light energy absorbed by photosystem II chlorophylls is allocated to photochemistry, fluorescence, and heat dissipation evaluated as non-photochemical quenching (NPQ). PRI is correlated with NPQ because it reflects the composition of xanthophylls, which are involved in heat dissipation. Assuming that NPQ is uniquely related to the photochemical efficiency (quantum yield of photochemistry), photochemical efficiencies can be assessed from either chlorophyll fluorescence or PRI. However, this assumption may not be held under some conditions such as low temperatures and photoinhibitory environments. Even in such cases, photosynthesis may be estimated more accurately if both chlorophyll fluorescence and PRI are determined simultaneously. To convert from photochemical efficiency to CO₂ assimilation, environmental responses in stomatal conductance also need to be considered. Models linking chlorophyll fluorescence and PRI with CO₂ assimilation rates will contribute to understanding and future prediction of the global carbon cycle.

Emerging satellite observations for diurnal cycling of ecosystem processes

Diurnal cycling of plant carbon uptake and water use, and their responses to water and heat stresses, provide direct insight into assessing ecosystem productivity, agricultural production and management practices, carbon and water cycles, and feedbacks to the climate. Temperature, light, atmospheric water demand, soil moisture and leaf water potential vary over the course of the day, leading to diurnal variations in stomatal conductance, photosynthesis and transpiration. Earth observations from polar-orbiting satellites are incapable of studying these diurnal variations. Here, we review the emerging satellite observations that have the potential for studying how plant functioning and ecosystem processes vary over the course of the diurnal cycle. The recently launched ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) and Orbiting Carbon Observatory-3 (OCO-3) provide land surface temperature, evapotranspiration (ET), gross primary production (GPP) and solar-induced chlorophyll fluorescence data at different times of day. New generation operational geostationary satellites such as Himawari-8 and the GOES-R series can provide continuous, high-frequency data of land surface temperature, solar radiation, GPP and ET. Future satellite missions such as GeoCarb, TEMPO and Sentinel-4 are also planned to have diurnal sampling capability of solar-induced chlorophyll fluorescence. We explore the unprecedented opportunities for characterizing and understanding how GPP, ET and water use efficiency vary over the course of the day in response to temperature and water stresses, and management practices. We also envision that these emerging observations will revolutionize studies of plant functioning and ecosystem processes in the context of climate change and that these observations and findings can inform agricultural and forest management and lead to improvements in Earth system models and climate projections.

The Links between Canopy Solar-Induced Chlorophyll Fluorescence and Gross Primary Production Responses to Meteorological Factors in the Growing Season in Deciduous Broadleaf Forest

Solar-induced chlorophyll fluorescence (SIF) is a hopeful indicator, which along with remote sensing, is used to measure the photosynthetic efficiency and gross primary production (GPP) of vegetation in regional terrestrial ecosystems. Studies have found a significant linear correlation between SIF and GPP in a variety of ecosystems. However, this relationship has mainly been established using SIF and GPP data derived from satellite remote sensing and continuous ground-based observations, respectively, which are difficult to accurately match. To overcome this, some studies have begun to use tower-based automatic observation instruments to study the changes of near-surface SIF and GPP. This study conducts continuous simultaneous observation of SIF, carbon flux, and meteorological factors on the forest canopy of a cork oak plantation during the growing season to explore how meteorological factors impact on canopy SIF and its relationship with GPP. This research found that the canopy SIF has obvious diurnal and day-to-day variations during the growing season but overall is relatively stable. Furthermore, SIF is greatly affected by incident radiation in different weather conditions and can change daily. Meteorological factors have a major role in the relationship between SIF and GPP; overall, the relationship shows a significant linear regression on the 30 min scale, but weakens when aggregating to the diurnal scale. Photosynthetically active radiation (PAR) drives SIF on a daily basis and changes the relationship between SIF and GPP on a seasonal timescale. As PAR increases, the daily slopes of the linear regressions between SIF and GPP decrease. On the 30 min timescale, both SIF and GPP increase with PAR until it reaches 1250 μmol·m−2·s−1; subsequently, SIF continues to increase while GPP decreases and they show opposite trends. Soil moisture and vapor pressure deficit influence SIF and GPP, respectively. Our findings demonstrate that meteorological factors affect the relationship between SIF and GPP, thereby enhancing the understanding of the mechanistic link between chlorophyll fluorescence and photosynthesis.

Potential of hotspot solar-induced chlorophyll fluorescence for better tracking terrestrial photosynthesis

Remote sensing of solar-induced fluorescence (SIF) opens a new window for quantifying a key ecological variable, the terrestrial ecosystem gross primary production (GPP), because of the revealed strong SIF-GPP correlation. However, similar to many other remotely sensed metrics, SIF observations suffer from the sun-sensor geometry effects, which may have important impacts on the SIF-GPP relationship but remain poorly understood. Here we used remotely sensed SIF, globally distributed tower GPP data, and a mechanistic model to provide a systematic analysis. Our results reveal that leaf physiology, canopy structure, and sun-sensor geometries all affect the SIF-GPP relationship. In particular, we found that SIF observations in the sun-tracking hotspot direction can be a better proxy of GPP due to the similar responses of light use efficiency and SIF escaping probability in the hotspot direction to the increasing incoming solar radiation. Such conclusions are supported by a variety of modeling simulations and satellite observations over various plant function types, at different time scales and with satellite observational modes. This study demonstrates the potential and advantage of normalizing SIF observations to the hotspot direction for better global GPP estimations. This study also demonstrates the great potentials of current and future spaceborne sun-tracking satellite missions for a significant improvement in measuring and monitoring, at a wide range of spatial and temporal scales, the changes in terrestrial ecosystem GPP in response to anticipated changes in the Earth's environmental conditions.

Differential responses to two heatwave intensities in a Mediterranean citrus orchard are identified by combining measurements of fluorescence and carbonyl sulfide (COS) and CO2 uptake

The impact of extreme climate episodes such as heatwaves on plants physiological functioning and survival may depend on the event intensity, which requires quantification. We unraveled the distinct impacts of intense (HW) and intermediate (INT) heatwave days on carbon uptake, and the underlying changes in the photosynthetic system, in a Mediterranean citrus orchard using leaf active (pulse amplitude modulation; PAM) and canopy level passive (sun-induced; SIF) fluorescence measurements, together with CO₂ , water vapor, and carbonyl sulfide (COS) exchange measurements. Compared to normal (N) days, gross CO₂ uptake fluxes (gross primary production, GPP) were significantly reduced during HW days, but only slightly decreased during INT days. By contrast, COS uptake flux and SIF_A (at 760 nm) decreased during both HW and INT days, which was reflected in leaf internal CO₂ concentrations and in nonphotochemical quenching, respectively. Intense (HW) heatwave conditions also resulted in a substantial decrease in electron transport rates, measured using leaf-scale fluorescence, and an increase in the fractional energy consumption in photorespiration. Using the combined proxy approach, we demonstrate a differential ecosystem response to different heatwave intensities, which allows the trees to preserve carbon assimilation during INT days but not during HW days.

Soil microbial CO2 fixation plays a significant role in terrestrial carbon sink in a dryland ecosystem: A four-year small-scale field-plot observation on the Tibetan Plateau

Assessment of the global terrestrial carbon (C) sink remains uncertain, and the uncertainty is largely derived from dryland ecosystems. Here we investigated the uncertainty and dynamics of gross primary productivity (GPP) by distinguishing the contributions of soil microbial primary producers and plants to CO₂ fixation during four sequential growing seasons in a fragile dry grassland on the Tibetan Plateau. The results demonstrated that soil microbial GPP consistently accounted for a high proportion of plant GPP (18.2%), and both exhibited similar seasonal patterns during the four-year observation. Soil microbial GPP demonstrated a much greater interannual variation (76.1%) than plant GPP (15.1%), indicating that the interannual GPP uncertainty could be largely from microbial primary producers. Regression analysis indicated that plant GPP had higher sensitivity (demonstrated by slope) than soil microbial GPP to both soil water content and temperature. The GPP ratio of soil microbes to plants also demonstrated a clear seasonal change, and peaked in July in the four-year observation, with a minimum interannual variation (6.8%). The GPP ratio enhanced with increasing soil water content (P < 0.001), but did not correlate with soil temperature. Our findings suggest the great potential of soil microbial GPP, and challenge the plant-oriented models of terrestrial C estimation, which account for plant GPP but ignore soil microbial GPP. Thus, a more robust framework needs to incorporate both soil microbial and plant GPPs for accurately assessing C balance.

Seasonal variation in the canopy color of temperate evergreen conifer forests

Evergreen conifer forests are the most prevalent land cover type in North America. Seasonal changes in the color of evergreen forest canopies have been documented with near-surface remote sensing, but the physiological mechanisms underlying these changes, and the implications for photosynthetic uptake, have not been fully elucidated. Here, we integrate on-the-ground phenological observations, leaf-level physiological measurements, near surface hyperspectral remote sensing and digital camera imagery, tower-based CO₂ flux measurements, and a predictive model to simulate seasonal canopy color dynamics. We show that seasonal changes in canopy color occur independently of new leaf production, but track changes in chlorophyll fluorescence, the photochemical reflectance index, and leaf pigmentation. We demonstrate that at winter-dormant sites, seasonal changes in canopy color can be used to predict the onset of canopy-level photosynthesis in spring, and its cessation in autumn. Finally, we parameterize a simple temperature-based model to predict the seasonal cycle of canopy greenness, and we show that the model successfully simulates interannual variation in the timing of changes in canopy color. These results provide mechanistic insight into the factors driving seasonal changes in evergreen canopy color and provide opportunities to monitor and model seasonal variation in photosynthetic activity using color-based vegetation indices.

The inverse relationship between solar-induced fluorescence yield and photosynthetic capacity: benefits for field phenotyping

Improving photosynthesis is considered a promising way to increase crop yield to feed a growing population. Realizing this goal requires non-destructive techniques to quantify photosynthetic variation among crop cultivars. Despite existing remote sensing-based approaches, it remains a question whether solar-induced fluorescence (SIF) can facilitate screening crop cultivars of improved photosynthetic capacity in plant breeding trials. Here we tested a hypothesis that SIF yield rather than SIF had a better relationship with the maximum electron transport rate (Jmax). Time-synchronized hyperspectral images and irradiance spectra of sunlight under clear-sky conditions were combined to estimate SIF and SIF yield, which were then correlated with ground-truth Vcmax and Jmax. With observations binned over time (i.e. group 1: 6, 7, and 12 July 2017; group 2: 31 July and 18 August 2017; and group 3: 24 and 25 July 2018), SIF yield showed a stronger negative relationship, compared with SIF, with photosynthetic variables. Using SIF yield for Jmax (Vcmax) predictions, the regression analysis exhibited an R2 of 0.62 (0.71) and root mean square error (RMSE) of 11.88 (46.86) μmol m-2 s-1 for group 1, an R2 of 0.85 (0.72) and RMSE of 13.51 (49.32) μmol m-2 s-1 for group 2, and an R2 of 0.92 (0.87) and RMSE of 15.23 (30.29) μmol m-2 s-1 for group 3. The combined use of hyperspectral images and irradiance measurements provides an alternative yet promising approach to characterization of photosynthetic parameters at plot level.