Search

Spring vegetation growth can benefit summer growth by increasing foliage area and carbon sequestration potential, or impair it by consuming additional resources needed for sustaining subsequent growth. However, the prevalent driving mechanism and its temporal changes remain unknown. Using satellite observations and long-term atmospheric CO 2 records, here we show a weakening trend of the linkage between spring and summer vegetation growth/productivity in the Northern Hemisphere during 1982-2021. This weakening is driven by warmer and more extreme hot weather that becomes unfavourable for peak-season growth, shifting peak plant functioning away from earlier periods. This is further exacerbated by seasonally growing ecosystem water stress due to reduced water supply and enhanced water demand. Our finding suggests that beneficial carryover effects of spring growth on summer growth are diminishing or even reversing, acting as an early warning sign of the ongoing shift of climatic effects from stimulating to suppressing plant photosynthesis during the early to peak seasons., (© 2024. The Author(s), under exclusive licence to Springer Nature Limited.)

The maximum rate of carboxylation (V cmax ) is an essential leaf trait determining the photosynthetic capacity of plants. Existing approaches for estimating V cmax at large scale mainly rely on empirical relationships with proxies such as leaf nitrogen/chlorophyll content or hyperspectral reflectance, or on complicated inverse models from gross primary production or solar-induced fluorescence. A novel mechanistic approach based on the assumption that plants optimize resource investment coordinating with environment and growth has been shown to accurately predict C3 plant V cmax based on mean growing season environmental conditions. However, the ability of optimality theory to explain seasonal variation in V cmax has not been fully investigated. Here, we adapt an optimality-based model to simulate daily V cmax,25C (V cmax at a standardized temperature of 25°C) by incorporating the effects of antecedent environment, which affects current plant functioning, and dynamic light absorption, which coordinates with plant functioning. We then use seasonal V cmax,25C field measurements from 10 sites across diverse ecosystems to evaluate model performance. Overall, the model explains about 83% of the seasonal variation in C3 plant V cmax,25C across the 10 sites, with a medium root mean square error of 12.3 μmol m -2  s -1 , which suggests that seasonal changes in V cmax,25C are consistent with optimal plant function. We show that failing to account for acclimation to antecedent environment or coordination with dynamic light absorption dramatically decreases estimation accuracy. Our results show that optimality-based approach can accurately reproduce seasonal variation in canopy photosynthetic potential, and suggest that incorporating such theory into next-generation trait-based terrestrial biosphere models would improve predictions of global photosynthesis., (© 2020 John Wiley & Sons Ltd.)

The El Niño-Southern Oscillation exerts a large influence on global climate regimes and on the global carbon cycle. Although El Niño is known to be associated with a reduction of the global total land carbon sink, results based on prognostic models or measurements disagree over the relative contribution of photosynthesis to the reduced sink. Here, we provide an independent remote sensing-based analysis on the impact of the 2015-2016 El Niño on global photosynthesis using six global satellite-based photosynthesis products and a global solar-induced fluorescence (SIF) dataset. An ensemble of satellite-based photosynthesis products showed a negative anomaly of -0.7 ± 1.2 PgC in 2015, but a slight positive anomaly of 0.05 ± 0.89 PgC in 2016, which when combined with observations of the growth rate of atmospheric carbon dioxide concentrations suggests that the reduction of the land residual sink was likely dominated by photosynthesis in 2015 but by respiration in 2016. The six satellite-based products unanimously identified a major photosynthesis reduction of -1.1 ± 0.52 PgC from savannahs in 2015 and 2016, followed by a highly uncertain reduction of -0.22 ± 0.98 PgC from rainforests. Vegetation in the Northern Hemisphere enhanced photosynthesis before and after the peak El Niño, especially in grasslands (0.33 ± 0.13 PgC). The patterns of satellite-based photosynthesis ensemble mean were corroborated by SIF, except in rainforests and South America, where the anomalies of satellite-based photosynthesis products also diverged the most. We found the inter-model variation of photosynthesis estimates was strongly related to the discrepancy between moisture forcings for models. These results highlight the importance of considering multiple photosynthesis proxies when assessing responses to climatic anomalies.This article is part of a discussion meeting issue 'The impact of the 2015/2016 El Niño on the terrestrial tropical carbon cycle: patterns, mechanisms and implications'., (© 2018 The Author(s).)

Leaf-level gas exchange data support the mechanistic understanding of plant fluxes of carbon and water. These fluxes inform our understanding of ecosystem function, are an important constraint on parameterization of terrestrial biosphere models, are necessary to understand the response of plants to global environmental change, and are integral to efforts to improve crop production. Collection of these data using gas analyzers can be both technically challenging and time consuming, and individual studies generally focus on a small range of species, restricted time periods, or limited geographic regions. The high value of these data is exemplified by the many publications that reuse and synthesize gas exchange data, however the lack of metadata and data reporting conventions make full and efficient use of these data difficult. Here we propose a reporting format for leaf-level gas exchange data and metadata to provide guidance to data contributors on how to store data in repositories to maximize their discoverability, facilitate their efficient reuse, and add value to individual datasets. For data users, the reporting format will better allow data repositories to optimize data search and extraction, and more readily integrate similar data into harmonized synthesis products. The reporting format specifies data table variable naming and unit conventions, as well as metadata characterizing experimental conditions and protocols. For common data types that were the focus of this initial version of the reporting format, i.e., survey measurements, dark respiration, carbon dioxide and light response curves, and parameters derived from those measurements, we took a further step of defining required additional data and metadata that would maximize the potential reuse of those data types. To aid data contributors and the development of data ingest tools by data repositories we provided a translation table comparing the outputs of common gas exchange instruments. Extensive consultation with data collectors, data users, instrument manufacturers, and data scientists was undertaken in order to ensure that the reporting format met community needs. The reporting format presented here is intended to form a foundation for future development that will incorporate additional data types and variables as gas exchange systems and measurement approaches advance in the future. The reporting format is published in the U.S. Department of Energy's ESS-DIVE data repository, with documentation and future development efforts being maintained in a version control system.

Accurate estimation of terrestrial photosynthesis has broad scientific and societal impacts. Measurements of photosynthesis can be used to assess plant health, quantify crop yield, and determine the largest CO₂ flux in the carbon cycle. Long-term and continuous monitoring of vegetation optical properties can provide valuable information about plant physiology. Recent developments of the remote sensing of solar-induced chlorophyll fluorescence (SIF) and vegetation spectroscopy have shown promising results in using this information to quantify plant photosynthetic activities and stresses at the ecosystem scale. However, there are few automated systems that allow for unattended observations over months to years. Here we present FluoSpec 2, an automated system for collecting irradiance and canopy radiance that has been deployed in various ecosystems in the past years. The instrument design, calibration, and tests are recorded in detail. We discuss the future directions of this field spectroscopy system. A network of SIF sensors, FluoNet, is established to measure the diurnal and seasonal variations of SIF in several ecosystems. Automated systems such as FluoSpec 2 can provide unique information on ecosystem functioning and provide important support to the satellite remote sensing of canopy photosynthesis.

The ecological mechanism underlying nocturnal stomatal conductance (gsn) in C3 and C4 plants remains elusive. In this study, we proposed a 'coordinated leaf trait' hypothesis to explain gsn in rice plants. We conducted an open‐field experiment by applying drought, nutrient stress and the combined drought–nutrient stress. We found that gsn was neither strongly reduced by drought nor consistently increased by nutrient stress. With the aforementioned multiple abiotic stressors considered as random effects, gsn exhibited a strong positive correlation with dark respiration (Rn). Notably, gsn primed early morning (5:00–7:00) photosynthesis through faster stomatal response time. This photosynthesis priming effect diminished after mid‐morning (9:00). Leaves were cooled by gsn‐derived transpiration. However, our results clearly suggest that evaporative cooling did not reduce dark respiration cost. Our results indicate that gsn is more closely related to carbon respiration and assimilation than water and nutrient availability, and that dark respiration can explain considerable variation of gsn. Summary Statement: The nocturnal stomatal conductance in rice plants was not strongly affected by drought or macronutrient stress. Instead, it exhibited a strong positive correlation with dark respiration and played a role in priming early morning photosynthesis. [ABSTRACT FROM AUTHOR]

We test the relationship between canopy photosynthesis and reflected near‐infrared radiation from vegetation across a range of functional (photosynthetic pathway and capacity) and structural conditions (leaf area index, fraction of green and dead leaves, canopy height, reproductive stage, and leaf angle inclination), weather conditions, and years using a network of field sites from across central California. We based our analysis on direct measurements of canopy photosynthesis, with eddy covariance, and measurements of reflected near‐infrared and red radiation from vegetation, with light‐emitting diode sensors. And we interpreted the observed relationships between photosynthesis and reflected near‐infrared radiation using simulations based on the multilayer, biophysical model, CanVeg. Measurements of reflected near‐infrared radiation were highly correlated with measurements of canopy photosynthesis on half‐hourly, daily, seasonal, annual, and decadal time scales across the wide range of function and structure and weather conditions. Slopes of the regression between canopy photosynthesis and reflected near‐infrared radiation were greatest for the fertilized and irrigated C4 corn crop, intermediate for the C3 tules on nutrient‐rich organic soil and nitrogen fixing alfalfa, and least for the native annual grasslands and oak savanna on nutrient‐poor, mineral soils. Reflected near‐infrared radiation from vegetation has several advantages over other remotely sensed vegetation indices that are used to infer canopy photosynthesis; it does not saturate at high leaf area indices, it is insensitive to the presence of dead legacy vegetation, the sensors are inexpensive, and the reflectance signal is strong. Hence, information on reflected near‐infrared radiation from vegetation may have utility in monitoring carbon assimilation in carbon sequestration projects or on microsatellites orbiting Earth for precision agriculture applications. Key Points: Reflected near‐infrared radiation is a strong proxy for canopy photosynthesisThis proxy works for a wide range of ecosystem structure and functionThe method has promise to be used for a variety of applications relating to carbon and water use [ABSTRACT FROM AUTHOR]

Abstract Quantifying global terrestrial photosynthesis is essential to understanding the global carbon cycle and the climate system. Remote sensing has played a pivotal role in advancing our understanding of photosynthesis from leaf to global scale; however, substantial uncertainties still exist. In this review, we provide a historical overview of theory, modeling, and observations of photosynthesis across space and time for decadal intervals beginning in the 1950s. Then we identify the key uncertainties in global photosynthesis estimates, including evaluating light intercepted by canopies, biophysical forcings, the structure of light use efficiency models and their parameters, like photosynthetic capacity, and relationships between sun-induced chlorophyll fluorescence and canopy photosynthesis. Finally, we review new opportunities with big data and recently launched or planned satellite missions. Graphical abstract Unlabelled Image Highlights • Reviewed history of global photosynthesis since 1950s • Reviewed uncertainties in remote sensing of global photosynthesis • Reviewed emerging opportunities with recent and new satellite missions [ABSTRACT FROM AUTHOR]

The El Niño-Southern Oscillation exerts a large influence on global climate regimes and on the global carbon cycle. Although El Niño is known to be associated with a reduction of the global total land carbon sink, results based on prognostic models or measurements disagree over the relative contribution of photosynthesis to the reduced sink. Here, we provide an independent remote sensing-based analysis on the impact of the 2015-2016 El Niño on global photosynthesis using six global satellite-based photosynthesis products and a global solar-induced fluorescence (SIF) dataset. An ensemble of satellite-based photosynthesis products showed a negative anomaly of -0.7±1.2 PgC in 2015, but a slight positive anomaly of 0.05±0.89 PgC in 2016, which when combined with observations of the growth rate of atmospheric carbon dioxide concentrations suggests that the reduction of the land residual sink was likely dominated by photosynthesis in 2015 but by respiration in 2016. The six satellite-based products unanimously identified a major photosynthesis reduction of -1.1±0.52 PgC from savannahs in 2015 and 2016, followed by a highly uncertain reduction of -0.22±0.98 PgC from rainforests. Vegetation in the Northern Hemisphere enhanced photosynthesis before and after the peak El Niño, especially in grasslands (0.33±0.13 PgC). The patterns of satellite-based photosynthesis ensemble mean were corroborated by SIF, except in rainforests and South America, where the anomalies of satellite-based photosynthesis products also diverged the most.We found the inter-model variation of photosynthesis estimates was strongly related to the discrepancy between moisture forcings for models. These results highlight the importance of considering multiple photosynthesis proxies when assessing responses to climatic anomalies. This article is part of a discussion meeting issue 'The impact of the 2015/ 2016 El Niño on the terrestrial tropical carbon cycle: patterns, mechanisms and implications'. [ABSTRACT FROM AUTHOR]

Sun-induced chlorophyll fluorescence (SiF) is increasingly used as a proxy for vegetation canopy photosynthesis. While ground-based, airborne, and satellite observations have demonstrated a strong linear relationship between SiF and gross primary production (GPP) at seasonal scales, their relationships at high temporal resolution across diurnal to seasonal scales remain unclear. In this study, far-red canopy SiF, GPP, and absorbed photosynthetically active radiation (APAR) were continuously monitored using automated spectral systems and an eddy flux tower over an entire growing season in a rice paddy. At half-hourly resolution, strong linear relationships between SiF and GPP (R 2  = 0.76) and APAR and GPP (R 2  = 0.76) for the whole growing season were observed. We found that relative humidity, diffuse PAR fraction, and growth stage influenced the relationships between SiF and GPP, and APAR and GPP, and incorporating those factors into multiple regression analysis led to improvements up to R 2  = 0.83 and R 2  = 0.88, respectively. Relationships between LUE p (=GPP/APAR) and LUE f (=SiF/APAR) were inconsistent at half-hourly and weak at daily resolutions (R 2  = 0.24). Both at diurnal and seasonal time scales with half-hourly resolution, we found considerably stronger linear relationships between SiF and APAR than between either SiF and GPP or APAR and GPP. Overall, our results indicate that for subdiurnal temporal resolution, canopy SiF in the rice paddy is above all a very good proxy for APAR at diurnal and seasonal time scales and that therefore SiF-based GPP estimation needs to take into account relevant environmental information to model LUE p . These findings can help develop mechanistic links between canopy SiF and GPP across multiple temporal scales. [ABSTRACT FROM AUTHOR]

The diurnal sampling capability of geostationary satellites provides unprecedented opportunities for monitoring canopy photosynthesis at multiple temporal scales. At the diurnal scale, only geostationary satellites can currently provide sub-daily data at regular intervals, also it can help to minimize data gaps due to clouds at the seasonal scale. However, the potential of geostationary satellites for monitoring photosynthesis has not been explored in depth. In this study, we tracked diurnal to seasonal variations in gross primary production (GPP) using the product of near-infrared reflectance of vegetation and photosynthetically active radiation (PAR) (NIRvP) over deciduous forests, mixed forests and a rice paddy during the growing season. For this purpose, we generated three levels of reflectance and PAR from Geostationary Korea Multi-Purpose Satellite-2A (GK-2A). We examined how NIRvP derived from GK-2A tracked in-situ GPP data collected from five flux tower sites in South Korea. Bi-directional Reflectance Distribution Function (BRDF) normalized NIRvP agreed well with in-situ GPP over the course of the growing season at hourly (R2 = 0.68–0.77) and daily timesteps (R2 = 0.71–0.83). Atmospheric correction and BRDF normalization improved the performance of NIRvP in tracking GPP at both the diurnal and seasonal time scales. Also, GK-2A showed a much higher percentage of available high-quality BRDF data over the whole growing season for all study sites than the Moderate Resolution Imaging Spectroradiometer (MODIS) (GK-2A: 85%; MODIS: 39%), especially during the cloudy monsoon period. Our findings demonstrated that the unique observation characteristics of geostationary satellites can contribute to large-scale monitoring of diurnal to seasonal GPP dynamics. [Display omitted] • Demonstrated that GK-2A NIRvP well captured diurnal to seasonal variations in GPP. • Quantified greater high-quality BRDF data availability of GK-2A (85%) than MODIS (39%). • Showed importance of atmospheric correction and BRDF normalization for tracking GPP. [ABSTRACT FROM AUTHOR]

Parks account for a large proportion of green spaces in urban regions, but most previous studies have focused on the values of recreational services in urban parks-carbon uptake by plants in urban parks has been studied less extensively. Urban parks typically form complex landscapes in space and time by integrating multiple species with open canopies. Thus, to better understand canopy photosynthesis in urban park, measuring spatial and temporal variations in photosynthetic parameters and canopy structural variables is essential. Here, we report seasonal and spatial variations in two key photosynthetic parameters ( V cmax and J max which are the maximum rates of carboxylation and electron transport, respectively) and leaf area index (LAI) in Seoul Forest Park. During the peak growing season, we found an eightfold difference (20 to 149 μmol m −2 s −1 ) and fourfold difference (38 to 141 μmol m −2 s −1 ) in V cmax and J max , respectively, across 10 species. Over the seasons, two woody species ( Zelkova serrata (Thunb.) Makino and Prunus yedoensis Matsum ), respectively showed three- to fivefold differences in V cmax and two- to fivefold differences in J max . We evaluated whether leaf nitrogen contents could predict V cmax and J max , and found significant correlations among the three variables during the peak growing season across 10 species, but no significant correlations among them over the seasons in the two woody species. LAI computed using in- situ observations and satellite remote-sensing imagery showed a high spatial heterogeneity during the growing season. These results highlight the considerable spatial and temporal variability in photosynthetic parameters and LAI, which implies that individual tree-based three-dimensional canopy modeling will be essential for accurate estimation of canopy photosynthesis in urban parks. [ABSTRACT FROM AUTHOR]

• Generated CubeSats derived NIRvP maps at daily, 3 m resolution. • Matching NIRvP maps to flux tower footprint improved the NIRvP-GPP relationship. • The effects of matching the footprints were largest for wetland sites. Mapping canopy photosynthesis in both high spatial and temporal resolution is essential for carbon cycle monitoring in heterogeneous areas. However, well established satellites in sun-synchronous orbits such as Sentinel-2, Landsat and MODIS can only provide either high spatial or high temporal resolution but not both. Recently established CubeSat satellite constellations have created an opportunity to overcome this resolution trade-off. In particular, Planet Fusion allows full utilization of the CubeSat data resolution and coverage while maintaining high radiometric quality. In this study, we used the Planet Fusion surface reflectance product to calculate daily, 3-m resolution, gap-free maps of the near-infrared radiation reflected from vegetation (NIRvP). We then evaluated the performance of these NIRvP maps for estimating canopy photosynthesis by comparing with data from a flux tower network in Sacramento-San Joaquin Delta, California, USA. Overall, NIRvP maps captured temporal variations in canopy photosynthesis of individual sites, despite changes in water extent in the wetlands and frequent mowing in the crop fields. When combining data from all sites, however, we found that robust agreement between NIRvP maps and canopy photosynthesis could only be achieved when matching NIRvP maps to the flux tower footprints. In this case of matched footprints, NIRvP maps showed considerably better performance than in situ NIRvP in estimating canopy photosynthesis both for daily sum and data around the time of satellite overpass (R2 = 0.78 vs. 0.60, for maps vs. in situ for the satellite overpass time case). This difference in performance was mostly due to the higher degree of consistency in slopes of NIRvP-canopy photosynthesis relationships across the study sites for flux tower footprint-matched maps. Our results show the importance of matching satellite observations to the flux tower footprint and demonstrate the potential of CubeSat constellation imagery to monitor canopy photosynthesis remotely at high spatio-temporal resolution. [Display omitted] [ABSTRACT FROM AUTHOR]

Abstract: Clumping index, the measure of foliage grouping relative to a random distribution of leaves in space, is a key structural parameter of plant canopies that influences canopy radiation regimes and controls canopy photosynthesis and other land–atmosphere interactions. In this study, we retrieve the clumping index using the original 275m resolution data of the Multi-angle Imaging SpectroRadiometer (MISR) instrument over a set of sites representing diverse biomes and different canopy structures. Also for the first time, the MISR derived clumping index values are directly validated with both in-situ vertical profiles and seasonal trajectories of clumping index. Our results illustrate that MISR data with 275m allow clumping index estimates at much more pertinent scales (both spatial and temporal) than previous maps from Polarization and Directionality of Earth Reflectances (POLDER) and Moderate Resolution Imaging Spectroradiometer (MODIS) for modeling local carbon and energy fluxes. [Copyright &y& Elsevier]

Sun-induced chlorophyll fluorescence (SIF) is a promising new tool for remotely estimating photosynthesis. However, the degree to which incoming solar radiation and the structure of the canopy rather than leaf physiology contribute to SIF variations is still not well characterized. Therefore, we investigated relationships between SIF and variables that at least partly capture the canopy structure component of SIF. For this, we relied on high-quality SIF observations from ground-based instruments, high-resolution airborne SIF imagery and the most recent satellite SIF products to cover large ranges in spatial and temporal resolution and diverse ecosystems. We found that the canopy structure-related near-infrared reflectance of vegetation multiplied by incoming sunlight (NIR V P) is a robust proxy for far-red SIF across a wide range of spatial and temporal scales. Our findings indicate that contributions from leaf physiology to SIF variability are small compared to the structure and radiation components. Also, NIR V P captured spatio-temporal patterns of canopy photosynthesis better than SIF, which seems to be mostly due to the greater retrieval noise of SIF. Compared to other relevant structural SIF proxies, NIR V P showed more robust relationships to SIF, especially at the global scale. Our results highlight the promise of using widely available NIR V P data for vegetation monitoring and also indicate the potential of using SIF and NIR V P in combination to extract physiological information from SIF. [Display omitted] • Comprehensive evaluation of SIF - NIRvP relationships across space and time. • NIRvP shows a strong and linear correlation to SIF across spatio-temporal scales. • NIRvP shows stronger relationships to GPP than SIF across scales. • NIRvP shows a stronger relationship to SIF than other structural SIF proxies. [ABSTRACT FROM AUTHOR]

We propose the Breathing Earth System Simulator (BESS), an upscaling approach to quantify global gross primary productivity and evapotranspiration using MODIS with a spatial resolution of 1-5 km and a temporal resolution of 8 days. This effort is novel because it is the first system that harmonizes and utilizes MODIS Atmosphere and Land products on the same projection and spatial resolution over the global land. This enabled us to use the MODIS Atmosphere products to calculate atmospheric radiative transfer for visual and near infrared radiation wave bands. Then we coupled atmospheric and canopy radiative transfer processes, with models that computed leaf photosynthesis, stomatal conductance and transpiration on the sunlit and shaded portions of the vegetation and soil. At the annual time step, the mass and energy fluxes derived from BESS showed strong linear relations with measurements of solar irradiance (r2 = 0.95, relative bias: 8%), gross primary productivity (r2 = 0.86, relative bias: 5%) and evapotranspiration (r2 = 0.86, relative bias: 15%) in data from 33 flux towers that cover seven plant functional types across arctic to tropical climatic zones. A sensitivity analysis revealed that the gross primary productivity and evapotranspiration computed in BESS were most sensitive to leaf area index and solar irradiance, respectively. We quantified the mean global terrestrial estimates of gross primary productivity and evapotranpiration between 2001 and 2003 as 118 ± 26 PgC yr-1 and 500 ± 104 mm yr-1 (equivalent to 63,000 ± 13,100 km3 yr-1), respectively. BESS-derived gross primary productivity and evapotranspiration estimates were consistent with the estimates from independent machine-learning, data-driven products, but the process-oriented structure has the advantage of diagnosing sensitivity of mechanisms. The process-based BESS is able to offer gridded biophysical variables everywhere from local to the total global land scales with an 8-day interval over multiple years. [ABSTRACT FROM AUTHOR]

Solar-induced chlorophyll fluorescence (SIF) provides us with new opportunities to understand the physiological and structural dynamics of vegetation from leaf to global scales. However, the relationships between SIF and gross primary productivity (GPP) are not fully understood, which is mainly due to the challenges of decoupling structural and physiological factors that control the relationships. Here, we report the results of continuous observations of canopy-level SIF, GPP, absorbed photosynthetically active radiation (APAR), and chlorophyll: carotenoid index (CCI) in a temperate evergreen needleleaf forest. To understand the mechanisms underlying the relationship between GPP and SIF, we investigated the relationships of light use efficiency (LUE p), chlorophyll fluorescence yield (Φ F), and the fraction of emitted SIF photons escaping from the canopy (f esc) separately. We found a strongly non-linear relationship between GPP and SIF at diurnal and seasonal time scales (R2 = 0.91 with a hyperbolic regression function, daily). GPP saturated with APAR, while SIF did not. Also, there were differential responses of LUE p and Φ F to air temperature. While LUE p reached saturation at high air temperatures, Φ F did not saturate. We found that the canopy-level chlorophyll: carotenoid index was strongly correlated to canopy-level Φ F (R2 = 0.84) implying that Φ F could be more closely related to pigment pool changes rather than LUE p. In addition, we found that the f esc contributed to a stronger SIF-GPP relationship by partially capturing the response of LUE p to diffuse light. These findings can help refine physiological and structural links between canopy-level SIF and GPP in evergreen needleleaf forest. [Display omitted] • Assessed relationships between GPP and SIF via LUE P , Φ F , f esc in an ENF site. • Found a strongly non-linear relationship between GPP and SIF. • While LUE p reached saturation at high air temperatures, Φ F did not saturate. • Φ F -CCI relationship was strongly linear. • f esc enhanced SIF-GPP correlation. [ABSTRACT FROM AUTHOR]

Remote sensing of far-red sun-induced chlorophyll fluorescence (SIF) has emerged as an important tool for studying gross primary productivity (GPP) at the global scale. However, the relationship between SIF and GPP at the canopy scale lacks a clear mechanistic explanation. This is largely due to the poorly characterized role of the relative contributions from canopy structure and leaf physiology to the variability of the top-of-canopy, observed SIF signal. In particular, the effect of the canopy structure beyond light absorption is that only a fraction (f esc) of the SIF emitted from all leaves in the canopy can escape from the canopy due to the strong scattering of near-infrared radiation. We combined rice, wheat and corn canopy-level in-situ datasets to study how the physiological and structural components of SIF individually relate to measures of photosynthesis. At seasonal time scales, we found a considerably strong positive correlation (R2 = 0.4–0.6) of f esc to the seasonal dynamics of the photosynthetic light use efficiency (LUE P), while the estimated physiological SIF yield was almost entirely uncorrelated to LUE P both at seasonal and diurnal time scales, with the partial exception of wheat. Consistent with these findings, the canopy structure and radiation component of SIF, defined as the product of APAR and f esc , explained the relationship of observed SIF to GPP and even outperformed GPP estimation based on observed SIF at two of the three sites investigated. These results held for both half-hourly and daily mean values. In contrast, the total emitted SIF, obtained by normalizing observed SIF for f esc , improved only the relationship to APAR but considerably decreased the correlation to GPP for all three crops. Our findings demonstrate the dominant role of canopy structure in the SIF-GPP relationship and establish a strong, mechanistic link between the near-infrared reflectance of vegetation (NIR V) and the relevant canopy structure information contained in the SIF signal. These insights are expected to be useful in improving remote sensing based GPP estimates. • A mechanistic decomposition of canopy SIF for three in situ crop datasets. • The canopy structure and radiation factor outperforms SIF for GPP estimation. • Canopy escape fraction of SIF correlates with photosynthetic light use efficiency. • Correcting SIF for canopy scattering improves the correlation to APAR but not GPP. • Estimates of physiological SIF yield show no clear seasonal patterns. [ABSTRACT FROM AUTHOR]

Statistical gross primary productivity (GPP) estimation from remote sensing observations has mostly been attempted on the basis of multispectral observations. To make full use of the information contained in vegetation spectra, however, hyperspectral observations should be used in combination with appropriate multivariate methods. Nevertheless, only very few previous studies attempted to estimate GPP directly from hyperspectral observations and did so on the basis of reflectance, with only a limited number of temporally discontinuous observations. In this study, we used long-term, continuous, half-hourly hyperspectral observations covering the visible and near-infrared spectral range to estimate GPP directly from upwelling irradiance using partial least square (PLS) regression in a rice paddy. To gain a better understanding of processes underlying the PLS estimation, we used extensive complementary field observations to run process-based simulations using the SCOPE model. We then applied PLS regression to the simulated hyperspectral data in the same way as for the observations and disentangled contributions related to relevant physiological processes, namely sun-induced chlorophyll fluorescence (SIF) and xanthophyll cycle-related spectral changes (XC). We found that upwelling hyperspectral irradiance in the visible and near-infrared spectral range predicted GPP better than reflectance. Furthermore, PLS-based GPP estimates outperformed both far-red SIF and widely used vegetation index-based methods. However, the most relevant information for the observation-based PLS-models was not clearly related to XC or SIF as the near-infrared spectral range showed comparable performance. Also, the simple average of upwelling irradiance over the 850–900 nm range outperformed the other non-multivariate approaches, including far-red SIF. These results held for the evaluation in terms of the seasonal variation of GPP, while there was apparently a small contribution of SIF and XC for the diurnal variation. The simulation-based analysis showed that SIF and XC contributed useful information to both GPP and photosynthetic light use efficiency (LUE) estimates at both seasonal and diurnal time scales. The strongest unique contribution from either SIF or XC, however, was to the diurnal variation of GPP and XC showed considerably better performance than SIF. We did not find improvements when combining the spectral regions of XC (500–570 nm) and SIF (650–800 nm) to estimate GPP. While SIF showed improvements when combined with the remaining spectral information excluding XC, this was not the case for XC. Our approach combines the strengths of process-based modeling with multivariate statistical analysis to improve our understanding of the usable information content in vegetation spectra and is highly relevant for further developing suitable methods for GPP estimation at large scales. • Hyperspectral GPP estimation outperforms SIF and other multispectral methods. • Upwelling radiation outperforms reflectance for multivariate GPP estimation. • Simulated xanthophyll cycle-related spectral changes very promising for GPP. • No improvements when combining SIF and xanthophyll cycle-related spectral changes. • Clear discrepancies between main mechanisms for observed and simulated results. [ABSTRACT FROM AUTHOR]

Sun-induced chlorophyll fluorescence (SIF) has recently become an important topic in large scale photosynthesis research. This is mainly related to the selectivity of SIF for vegetation on the one hand and the close mechanistic relationships to photosynthesis on the other hand. The latter, however, is already nonlinear at the leaf level and further complicated by the canopy scattering of far-red SIF. This scattering process, which is strongly affected by the structure of the canopy, results in only a fraction of SIF photons emitted at the leaf-level to escape the canopy. The challenge for canopy-level SIF research is therefore to disentangle the contributions related to canopy structure and leaf physiology. While it is widely assumed that the latter has the stronger relationship to photosynthesis, this has not been tested experimentally at the canopy scale so far. We used a recently developed approach to estimate the canopy escape probability of far-red SIF to analyze the contributions of canopy structure and leaf physiology separately. For this, we conducted a combined analysis for three in-situ datasets from a rice paddy, a wheat and a corn field that all had fPAR, PAR, far-red SIF, GPP and canopy reflectance observations covering most of the growing season. Our analysis focused on half-hourly observations both at the level of fluxes involving the absorbed radiation as well as at the level of light use efficiencies, which excludes the radiation component.We found that canopy structure effects dominated the seasonal component of the SIF escape probability and that this was the cause of a positive correlation of the latter to photosynthetic light use efficiency. The estimated total SIF yield of all leaves in the canopy, in contrast, was dominated by diurnal variations and was almost entirely uncorrelated to photosynthetic light use efficiency. Furthermore, our findings confirm the strong relationships between NIRv to both SIF and GPP at short time scales and the site level that were previously only reported at large spatial and long temporal scales. Thus, our findings bridge gaps between spatio-temoral scales and challenge the widely held assumption that the leaf physiology component in SIF provides most of the additional information for photosynthesis estimation that goes beyond APAR. We think that our approach has the potential to unify previous findings into a coherent picture that could enable the community to more easily identify the advantages and limitations of different methods of estimating GPP from remotely sensed observations. [ABSTRACT FROM AUTHOR]

The emerging technique of remotely sensed Sun-Induced chlorophyll Fluorescence (SIF) hasadvanced our ability to estimate plant photosynthetic activity. However, continuousobservations of SIF and Gross Primary Productivity (GPP) at the canopy scale inevergreen needle leaf forests have been lacking so far. Here, we report continuouslyobserved far-red canopy SIF, GPP and Absorbed Photosynthetically Active Radiation(APAR) during the senescence period in an evergreen needle leaf forest. We installed ahigh-spectral-resolution spectrometer combined with a rotating prism system, whichmeasures up- and down welling irradiance with a single fiber, 20 m above the canopy. Weinvestigated the relationships between SIF, APAR and GPP separately for the followingthree periods; 1) before and 2) after understory vegetation leaf fall, 3) after thetemperature drops below zero. In addition, we examined the effects of diffuse light on therelationships. We found that the slope of the half-hourly SIF-APAR relationshipwas distinctly different in each period but this was not the case in the APAR-GPPand SIF-GPP relationships: while the APAR-GPP relationship showed a similarslope difference as APAR-SIF in the third period, the other two periods had a moresimilar slope value; the SIF-GPP relationship, in contrast had similar slope values inthe last two periods but a very different value in the first period. In addition, thehalf-hourly SIF-APAR relationship showed the highest correlation over the threeperiods (R2 = 0.83). On the other hand, regardless of sky condition, we confirmedthat for daily mean values, SIF-GPP relationship (cloudy: R2 = 0.83, clear: R2 =0.76) was stronger than the SIF-APAR relationship (cloudy: R2 = 0.66, clear: R2 =0.69). Overall, our results indicate that relationships between canopy SIF, GPP andAPAR can vary depending on the presence of understory vegetation, temperatureand the sky condition in an evergreen needle leaf forest. These findings will helpimprove canopy-level SIF-based GPP estimation across multiple temporal scales. [ABSTRACT FROM AUTHOR]

• Including angular variability in canopy structure improves 1D radiative transfer schemes. • The vertical distribution of light is impacted by angular variation in canopy structure. • Sites with dense vegetation are most impacted. Addressing the impact of vegetation architecture on shortwave radiation transfer in land surface models is important for accurate weather forecasting, carbon budget estimates, and climate predictions. This paper investigates to what extent it is possible to retrieve structural parameters of two different parameterization schemes from direct transmittance derived from digital hemispherical photography and 3D radiative transfer modeling for two study sites with different vegetation canopy architectures. Neglecting the representation of 3D canopy structure in radiative transfer schemes leads to significant errors in shortwave radiation partitioning (up to 3.5 times more direct transmittance in the 3D model). Structural parameters, referred to as whole canopy 'clumping indices', were obtained in order to evaluate the impact of angular variation in clumping on shortwave radiation transfer. Impacts on photosynthesis were evaluated at site level with the UKESM land surface model, JULES. A comparison between flux tower derived and modeled photosynthesis indicates that considering zenith angular variations of structural parameters in the radiative transfer scheme of the UKESM land surface model significantly improves photosynthesis prediction in light limited ecosystems (from RMSE = 2.91 μmol CO 2. m −2. s −1 to RMSE = 1.51 μmol CO 2. m −2. s −1, 48% smaller), typically with enhanced photosynthesis from bottom layers. Image, graphical abstract [ABSTRACT FROM AUTHOR]