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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 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]

Conventional process-based crop simulation models and agro-land surface models require numerous forcing variables and input parameters. The regional application of these crop simulation models is complicated by factors concerning input data requirements and parameter uncertainty. In addition, the empirical remotely sensed regional scale crop yield estimation method does not enable growth process modeling. In this study, we developed a process-based rice yield estimation model by integrating an assimilate allocation module into the satellite remote sensing-derived and biophysical process-based Breathing Earth System Simulator (BESS). Normalized accumulated gross primary productivity ( G P P n o r m - a c c u ) was used as a scaler for growth development, and the relationships between G P P n o r m - a c c u and dry matter partitioning coefficients were determined from the eddy covariance and biometric measurements at the Cheorwon Rice paddy KoFlux site. Over 95% of the variation in the dry matter allocation coefficients of rice grain could be explained by G P P n o r m - a c c u . The dynamics of dry matter distribution among different rice components were simulated, and the annual grain yields were estimated. BESS-Rice simulated GPP and dry matter partitioning dynamics, and rice yields were evaluated against in-situ measurements at three paddy rice sites registered in KoFlux. The results showed that BESS-Rice performed well in terms of rice productivity estimation, with average root mean square error (RMSE) value of 2.2 g C m −2  d −1 (29.5%) and bias of –0.5 g C m −2  d −1 (–7.1%) for daily GPP, and an average RMSE value of 534.8 kg ha −1 (7.7%) and bias of 242.1 kg ha −1 (3.5%) for the annual yield, respectively. BESS-Rice is much simpler than conventional crop models and this helps to reduce the uncertainty related to the forcing variables and input parameters and can result in improved regional yield estimation. The process-based mechanism of BESS-Rice also enables an agronomic diagnosis to be made and the potential impacts of climate change on rice productivity to be investigated. [ABSTRACT FROM AUTHOR]

Several global gross primary production (GPP) and evapotranspiration (ET) remote sensing products exist, mainly provided by machine-learning (e.g. MPI-BGC) and semi-empirical (e.g. MODIS) approaches. Process-based approaches have the advantage of representing the atmosphere-vegetation-soil system and associated fluxes as an organic integration, but their sophistication results in a lack of high spatiotemporal resolution continuous products. Targeting this gap, we reported a new set of global 8-day composite 1-km resolution GPP and ET products from 2000 to 2015, using a simplified process-based model, the Breathing Earth System Simulator (BESS). BESS couples atmosphere and canopy radiative transfer, photosynthesis and evapotranspiration, and uses MODIS atmosphere and land data and other satellite data sources as inputs. We evaluated BESS products against FLUXNET observations at site scale (total of 113 sites, 742 site years), and against MPI-BGC products at global scale. At site scale, BESS 8-day products agreed with FLUXNET observations with R 2 = 0.67 and RMSE = 2.58 gC m − 2 d − 1 for GPP, and R 2 = 0.62 and RMSE = 0.78 mm d − 1 for ET, respectively, and they captured a majority of seasonal variability, about half of spatial variability, and a minority of interannual variability in FLUXNET observations. At global scale, BESS mean annual sum GPP and ET maps agreed with MPI-BGC products with R 2 = 0.93 and RMSE = 229 gC m − 2 y − 1 for GPP, and R 2 = 0.90 and RMSE = 118 mm y − 1 for ET, respectively. Over the period of 2001–2011, BESS quantified the mean global GPP and ET as 122 ± 25 PgC y − 1 and 65 × 10 3 ± 11 × 10 3 km 3 y − 1 , respectively, with a significant ascending GPP trend by 0.27 PgC y − 2 ( p < 0.05), similar to MPI-BGC products as well. Overall, BESS GPP and ET estimates were comparable with FLUXNET observations and MPI-BGC products. The process-based BESS can serve as a set of independent GPP and ET products from official MODIS GPP and ET products. [ABSTRACT FROM AUTHOR]

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]

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]

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]

• A machine learning method (NN SIF) is developed to partition NEE using SIF. • NN SIF effectively reduces structural errors of standard partitioning methods. • NN SIF better partitions NEE under high temperature and water stress. • NN SIF unravels physiological controls on ecosystem–scale GPP–SIF relationships. Accurately partitioning net ecosystem exchange (NEE) into ecosystem respiration (ER) and gross primary productivity (GPP) is critical for understanding the terrestrial carbon cycle. The standard partitioning methods rely on simplified empirical models, which have inherent structural errors. These structural errors lead to biased GPP and ER estimation, especially during extreme events (e.g., drought) and human disturbances (e.g., crop harvest). Recently, solar-induced chlorophyll fluorescence (SIF) has been shown to be well correlated to GPP, thus offering a path to improve the NEE partitioning by constraining GPP. However, the ecosystem-scale relationship between GPP and SIF remains limited. Here, we show that neural networks informed by SIF observations (NN SIF) can be successfully used to partition NEE, while simultaneously learning the ecosystem-scale GPP-SIF relationship. NN SIF was compared against standard partitioning methods and NN without SIF constraint (NN noSIF), using field data from different ecosystems and synthetic data generated by a coupled fluorescence-photosynthesis model (SCOPE). NN SIF showed superior performance as: (1) it effectively improves the ER estimation, especially at high temperature, (2) it better captures the moisture limitation on ER, (3) it more accurately estimates LUE variations to stress, and (4) it uniquely captures the rapid GPP drop after land management (harvest). Furthermore, NN SIF can retrieve the GPP-SIF relationship at the ecosystem scale, and elucidate how this relationship responds to environmental conditions. Overall, our algorithm provides the first direct and non-empirical estimate of the ecosystem-scale GPP-SIF relationship, without relying on any prior empirical assumptions on the relationships between CO 2 fluxes, climatic drivers, and SIF. The new knowledge learned by NN SIF can help better estimate global-scale GPP using satellite SIF, especially during extreme events and in the presence of land management. [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]