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Solar-induced chlorophyll fluorescence (SIF) offers significant potential as a novel approach for quantifying carbon and water cycling in coastal wetland ecosystems across multiple spatial scales. However, the mechanisms governing these biogeochemical processes remain insufficiently understood, largely due to the periodic influence of tidal inundation. In this study, we investigated the effects and underlying mechanisms of meteorological and tidal factors on the relationships between canopy-level solar-induced chlorophyll fluorescence at 760 nm (SIF760) and key ecosystem processes, including gross primary productivity (GPP) and evapotranspiration (ET), in coastal wetlands. These processes are critical components of the ecosystem carbon and water cycles. Our approach involved a comparative analysis of simulations from the Soil Canopy Observation, Photochemistry and Energy Fluxes (SCOPE) model with field measurements. The results showed that: (1) simulations of SIF760 improved following observation-based calibration of the fluorescence photosynthesis module in the SCOPE model; (2) under optimal moisture and temperature conditions (VPD 1.2–1.4 kPa and temperatures of 20–23 °C for air, soil, and water), the simulations of GPP, ET, and SIF760 were most accurate, although salinity stress reduced performance. GPP simulations tended to overestimate under drought stress but improved at higher air temperatures (30–32 °C); (3) during tidal inundation, the SIF760-GPP relationship weakened while the SIF760-ET strengthened. The range of significant correlations between SIF760, water levels, and temperature narrowed, with both relationships becoming more complex due to salinity stress. These findings suggest that tidal inundation can alleviate temperature stress on photosynthesis and transpiration; however, it also decreases photosynthetic efficiency and alters radiative transfer processes due to elevated salinity and water levels. These factors are critical considerations when using SIF to monitor GPP and ET dynamics in coastal wetlands. This study demonstrated that the tidal dynamics significantly affected the SIF760-GPP and SIF760-ET relationships, underscoring the necessity of incorporating tidal influences in the application of SIF remote sensing for monitoring GPP and ET dynamics. The results of this study not only contribute to a deeper understanding of the mechanisms linking SIF760 with GPP and ET but also provide new insights into the development and refinement of SIF-based remote sensing for carbon quantification in coastal blue-carbon ecosystems on a large-scale domain. [ABSTRACT FROM AUTHOR]

Coupling solar-induced chlorophyll fluorescence (SIF) with gross primary productivity (GPP) for ecological function integration research presents numerous uncertainties, especially in ecologically fragile and climate-sensitive arid regions. Therefore, evaluating the suitability of SIF data for estimating GPP and the feasibility of improving its accuracy in the northern region of Xinjiang is of profound significance for revealing the spatial distribution patterns of GPP and the strong coupling relationship between GPP and SIF in arid regions, achieving the goal of "carbon neutrality" in arid regions. This study is based on multisource SIF satellite data and GPP observation data from sites in three typical ecosystems (cultivated and farmlands, pasture grasslands, and desert vegetation). Two precision improvement methods (canopy and linear) are used to couple multiple indicators to determine the suitability of multisource SIF data for GPP estimation and the operability of accuracy improvement methods in arid regions reveal the spatial characteristics of SIF (GPP). The results indicate the following. (1) The interannual variation of GPP shows an inverted "U" shape, with peaks values in June and July. The cultivated and farmland areas have the highest peak value among the sites (0.35 gC/m2/month). (2) The overall suitability ranking of multisource SIF satellite products for GPP estimation in arid regions is RTSIF > CSIF > SIF_OCO2_005 > GOSIF. RTSIF shows better suitability in the pasture grassland and cultivated and farmland areas (R2 values of 0.85 and 0.84, respectively). (3) The canopy method is suitable for areas with a high leaf area proportion (R2 improvement range: 0.05–0.06), while the linear method is applicable across different surface types (R2 improvement range: 0.01–0.13). However, the improvement effect of the linear method is relatively weaker in areas with high vegetation cover. (4) Combining land use data, the overall improvement of SIF (GPP) is approximately 0.11%, and the peak values of its are mainly distributed in the northern and southern slopes of the Tianshan Mountains, while the low values are primarily found in the Gurbantunggut Desert. The annual mean value of SIF (GPP) is about 0.13 mW/m2/nm/sr. This paper elucidates the applicability of SIF for GPP estimation and the feasibility of improving its accuracy, laying the theoretical foundation for the spatiotemporal coupling study of GPP and SIF in an arid region, and providing practical evidence for achieving carbon neutrality goals. [ABSTRACT FROM AUTHOR]

Coupling solar-induced chlorophyll fluorescence (SIF) with gross primary productivity (GPP) for ecological function integration research presents numerous uncertainties, especially in ecologically fragile and climate-sensitive arid regions. Therefore, evaluating the suitability of SIF data for estimating GPP and the feasibility of improving its accuracy in the northern region of Xinjiang is of profound significance for revealing the spatial distribution patterns of GPP and the strong coupling relationship between GPP and SIF in arid regions, achieving the goal of “carbon neutrality” in arid regions. This study is based on multisource SIF satellite data and GPP observation data from sites in three typical ecosystems (cultivated and farmlands, pasture grasslands, and desert vegetation). Two precision improvement methods (canopy and linear) are used to couple multiple indicators to determine the suitability of multisource SIF data for GPP estimation and the operability of accuracy improvement methods in arid regions reveal the spatial characteristics of SIF (GPP). The results indicate the following. (1) The interannual variation of GPP shows an inverted “U” shape, with peaks values in June and July. The cultivated and farmland areas have the highest peak value among the sites (0.35 gC/m2/month). (2) The overall suitability ranking of multisource SIF satellite products for GPP estimation in arid regions is RTSIF > CSIF > SIF_OCO2_005 > GOSIF. RTSIF shows better suitability in the pasture grassland and cultivated and farmland areas (R2 values of 0.85 and 0.84, respectively). (3) The canopy method is suitable for areas with a high leaf area proportion (R2 improvement range: 0.05–0.06), while the linear method is applicable across different surface types (R2 improvement range: 0.01–0.13). However, the improvement effect of the linear method is relatively weaker in areas with high vegetation cover. (4) Combining land use data, the overall improvement of SIF (GPP) is approximately 0.11%, and the peak values of its are mainly distributed in the northern and southern slopes of the Tianshan Mountains, while the low values are primarily found in the Gurbantunggut Desert. The annual mean value of SIF (GPP) is about 0.13 mW/m2/nm/sr. This paper elucidates the applicability of SIF for GPP estimation and the feasibility of improving its accuracy, laying the theoretical foundation for the spatiotemporal coupling study of GPP and SIF in an arid region, and providing practical evidence for achieving carbon neutrality goals.

Accurate phenological extraction is important for estimating carbon uptake in terrestrial ecosystems under climate change. The emergence of remotely sensed vegetation indices (VIs) and solar‐induced chlorophyll fluorescence (SIF) provides multiple approaches for extracting land surface phenology. However, there is lacking studies to track phenological metrics via multiple VIs and SIF. Therefore, the advantage of combining VIs and SIF to estimate more accurate phenology requires exploration. In this study, we combined the advantages of the normalized difference, enhanced, green‐red, near‐infrared reflectance vegetation indices from MCD43A4 data set, and SIF from CSIF data set to estimate hybrid phenology at 20 eddy flux sites in North America. Results showed that the hybrid phenology derived from the best‐performing start (SOS) and end (EOS) of the growing season among multiple VIs and SIF for each plant functional type and site were both more consistent with those derived from gross primary production (GPP). Specifically, the R2 of hybrid phenology increased by 0.11–0.4 (0.04–0.4) for SOS, 0.01–0.24 (0.09–0.22) for EOS, 0.01–0.7 (0.05–0.34) for the length of the growing season (LOS) based on Gaussian (logistic) method. Moreover, hybrid phenology can improve the explanation of the seasonal and annual variations in GPP. The explanatory power of hybrid phenology for GPP variations increased by 0.05–0.15 (0.02–0.23) for SOS, 0–0.36 (0.11–0.27) for EOS, 0.01–0.51 (0.03–0.4) for LOS, 0.04–0.18 (0.04–0.16) for LOS × ${\times} $ seasonal GPP maximum based on Gaussian (logistic) method. These findings highlight the potential of combining high‐spatiotemporal structural and coarse‐spatiotemporal physiological vegetation indicators in tracking phenology and GPP. Plain Language Summary: Vegetation phenology illustrates the timing of plant growth phases and can serve as a valuable indicator for understanding plant responses and feedback mechanisms to climate change. Consequently, it is important to accurately estimate vegetation phenology in terrestrial ecosystems. The remotely sensed vegetation indices (VIs) and solar‐induced chlorophyll fluorescence (SIF) are widely employed to estimate land surface phenology such as the start (SOS), end (EOS), and thus LOS. However, there is lacking studies to track phenological metrics via multiple VIs and SIF. This study proposes two hybrid phenological metrics estimation approach that combines the best‐performing vegetation indicators estimated SOS and EOS among multiple VIs and SIF for each plant functional type and site. Results reveal that two hybrid phenological metrics significantly enhance the accuracy of phenology estimation, and provide a more robust interpretation of gross primary productivity. Key Points: Hybrid phenological metrics are derived by identifying the most effective vegetation indicators for capturing the start of growing season and the end of growing seasonHybrid phenological metrics can yield superior accuracy compared to single vegetation index or solar‐induced chlorophyll fluorescence derived phenologyHybrid phenological metrics can better track the seasonal and annual gross primary production dynamics [ABSTRACT FROM AUTHOR]

• SIF performs well in tracking GPP during dry conditions in a semi-arid grassland. • SIF and GPP exhibit parallel responses to environmental variables under dry conditions. • Compared to wet conditions, the role of physiological information regulation in SIF and GPP increases under dry conditions. Solar-induced chlorophyll fluorescence (SIF) has recently emerged as a promising tool for estimating gross primary production (GPP). To date, there is ongoing debate regarding whether the strong correlations between SIF and GPP persist under dry conditions. Here, we conducted continuous far-red SIF measurements in a semi-arid grassland from 2017 to 2019 to investigate its association with GPP. Our findings revealed strong correlations in the seasonal patterns of SIF and GPP during dry conditions (R2=0.79). After disentangling the effects of photosynthetically active radiation and soil water content, we observed consistent responses to environmental variables in both SIF and GPP under dry conditions. Furthermore, we conducted a dominance analysis to assess the contributions of physiological and non-physiological components to the variations in SIF and GPP. Our results demonstrated a substantial increase in the contributions of physiological components to both SIF (wet: 19.29%; dry: 60.23%) and GPP (wet: 20.89%; dry: 28.38%) during dry conditions, highlighting a shift towards enhanced physiological regulation of SIF and GPP in response to dry conditions. In conclusion, our findings provide valuable insights into the GPP-SIF relationships in semi-arid grasslands under dry conditions. These insights hold the potential to refine and constrain model predictions under climate change. [ABSTRACT FROM AUTHOR]

• A canopy-scale model is proposed to estimate the fraction of APAR allocated to PSII. • The fraction of APAR allocated to PSII for sunlit and shaded leaves is simulated. • The fraction of APAR allocated to PSII varies from about 0.5 to 0.8. • The model enhances the potential of SIF observations for accurately estimating GPP. • The contribution of PSII to SIF TOC_760 remains nearly constant. Solar-induced chlorophyll fluorescence (SIF) contains contributions from both photosystem I (PSI) and photosystem II (PSII). In theory, SIF emitted from PSII (SIF PSII) should be extracted from at-sensor SIF to quantify photosynthetic CO 2 assimilation, as PSI fluorescence yield is nearly insensitive to changes in photochemical yield. In many SIF-related studies, the fraction of chlorophyll-absorbed energy allocated to PSII (β 2), a key factor controlling the flux of excitation energy for SIF PSII , is simply assigned a fixed value. However, β 2 is regulated in response to variations in environmental conditions to avoid an energy imbalance between PSI and PSII. By quantifying the regulating effect of the cytochrome b 6 f complex (Cyt b 6 f) on the electron transport from PSII to PSI, and its interaction with energy dissipation in both photosystems, we develop a framework to estimate β 2 and PSII fluorescence yield (Φ F2), two key determinants for SIF PSII , from SIF emission, PAR, air temperature, the maximum carboxylase activity of Rubisco (V cmax), and the maximum activity of Cyt b 6 f (J CB6F_max). The framework is equipped with a two-leaf scheme, enabling us to estimate SIF PSII for sunlit and shaded leaves from top-of-canopy SIF observations (SIF TOC). Our simulation results showed that β 2 and Φ F2 tend to change in opposite directions with varying PAR, making the contribution of PSII to SIF TOC relatively constant. We compare gross primary productivity (GPP) mechanistically estimated from SIF PSII obtained with fixed (GPP SIF_Fβ) and dynamic β 2 (GPP SIF_Dβ) against the eddy-tower-derived GPP (GPP EC) at a winter-wheat experiment site. At a half-hourly time scale, GPP SIF_Dβ is better than GPP SIF_Fβ , showing higher correlations with GPP EC (R2 = 0.74 versus R2 = 0.64 and RMSE = 6.61 μmol m−2 s−1 versus RMSE = 7.67 μmol m−2 s−1). The study provides a practical way to estimate the contribution of SIF PSII to SIF TOC , giving a better theoretical basis to SIF-based GPP estimation models. [ABSTRACT FROM AUTHOR]

Advances in retrieval of solar-induced chlorophyll fluorescence (SIF) provide a promising and independent approach for quantifying gross primary production (GPP) across spatial scales. Recent studies have highlighted the prominent role of q L, the fraction of open Photosystem II (PSII) reaction centers, in mechanistically modeling GPP from remote sensing SIF. However, due to the limited availability of simulated and experimental data, a comprehensive understanding of q L responses to environmental and physiological variations has yet to emerge, and as a consequence, prediction of q L across leaf and canopy scales is still in an early stage. Based on a global sensitivity analysis of a recently developed mechanical model of photosynthesis, we find that the broadband total SIF emitted from PSII (SIF TOT_FULL_PSII) and leaf temperature (T Leaf) are the two major predictors of q L. A leaf-level instrument is designed to obtain concurrent measurements of q L , SIF TOT_FULL_PSII , and T Leaf over a wide range of environmental conditions. From these measurements, we show that q L can be modelled as a hyperbolic function of SIF TOT_FULL_PSII with only one temperature-related parameter m which increases with temperature, but decreases rapidly as temperatures exceed the optimum temperature. It is suggested that m can be mathematically modelled by a peaked function. The results of the leaf-level experiments on winter wheat demonstrate that the proposed model predicts q L with high accuracy (R 2 ≥ 0.91, rRMSE ≤ 8.46%) under diverse light and temperature conditions. The essential steps necessary to apply it at canopy scale, including estimating the escape fraction, removing fluorescence emitted from Photosystem I, and reconstructing SIF TOT_FULL_PSII from top-of-canopy (TOC) narrowband SIF, are also presented. Our results confirm that estimated GPP using SIF-informed q L agrees well with measured GPP at a winter wheat site (R 2 = 0.81, rRMSE = 12.03%). The key benefit of SIF-informed approach is that SIF TOT_FULL_PSII provides critical information on the collective influence of the sub-canopy light environment on q L , avoiding the requirement to explicitly estimate q L at different canopy depths, potentially promoting the ability of SIF to mechanistically quantify photosynthetic CO 2 assimilation at large scales. • The mechanical model shows SIF and air temperature are the main predictors of q L. • A predictive model of q L as a function of SIF and air temperature is proposed. • Leaf-level measurements demonstrate the proposed model accurately predicts q L. • The q L predictive model helps to estimate directly canopy photosynthesis via SIF. • This practical approach allows us to quantify GPP at large scales. [ABSTRACT FROM AUTHOR]

Soil moisture and salinization are key environmental factors affecting crop growth and yield in arid farming areas with intense irrigation. Quantifying their effects on crops is beneficial to understanding the principle of soil water-salt interactions. Due to the strong coupling relationship between them, the independent effect on field crops is hard to distinguish clearly. In this study, taking Yongji Sub-irrigation District (YJSID) in Inner Mongolia of China as case study, the sorting bins method and GeoDetector were employed to try to disentangle relative effects of soil moisture and salinization on crop development and yield during growing seasons in 2021–2022. The regional soil water content (SWC) and soil salt content (SSC) were firstly mapped and validated through the inverse density weighted method and random forest model. Separated effects of SWC and SSC on solar-induced chlorophyll fluorescence (SIF), gross primary productivity (GPP), and crop yield were then calculated and presented. Results showed that low SWC inhibited SIF and the ratio of GPP to SIF (GPP/SIF) in SSC bins, as their values declined among 52.989% and 71.801% of YJSID. In SWC bins, the increase of SIF and GPP/SIF covered 91.356% and 50.087% of the area, indicating that low SSC could bring out higher SIF and GPP/SIF for field crops. Relative importance assessment suggested that SSC posed greater impacts than SWC on SIF and GPP/SIF in YJSID with the area of 77.188% and 59.628%, respectively. To the SWC effects, it performed greater for the C3 crops (sunflower, wheat, interplant, others) than the C4 plant (maize) in SIF, while reversely in GPP/SIF. The q value calculated from GeoDetector also indicated that SSC had greater effects than SWC on crop yield. Increased SSC tended to reduce grain yield with linear relationships, and the severity was bigger for maize than sunflower. These results would contribute to further understanding of the key processes involved in soil water-salt interactions and dealing with them better in agricultural practice. [Display omitted] • Soil moisture and salinization were decoupled on crop growth by sorting bins method. • Soil salinization posed greater effects than soil moisture on crop development. • Soil moisture influenced SIF on C3 more than C4 crops, but GPP/SIF reversely. • The GeoDetector explained the impacts of soil moisture and salinization on yield. • Yield was affected by soil salinization more than moisture for maize and sunflower. [ABSTRACT FROM AUTHOR]

Solar-induced chlorophyll fluorescence (SIF) has long been regarded as a proxy for photosynthesis and has shown superiority in estimating gross primary production (GPP) compared to traditional vegetation indices, especially in evergreen ecosystems. However, current SIF-based GPP estimations regard the canopy as a large leaf and seldom consider the impact of interactions among light, canopy structure, and leaf physiology. In this study, we proposed GPP estimation models with different descriptions of light–structure–physiology interactions (including the layered model, the two-leaf model, and the layered two-leaf model) and compared their performances with the big-leaf model using half-hourly (or hourly) observations at evergreen needleleaf forest sites. First, we found that the big-leaf model underestimated GPP, especially at noon. All models showed higher accuracy than that of the big-leaf model. Second, we investigated the diurnal dynamics of GPP estimations in each canopy layer and found that models with a two-leaf assumption captured the diurnal variations in GPP better than that with the layered assumption. We also deduced that the poor performance of the big-leaf model was related to its overestimation of the overall light stress on the redox state of PSII reaction centers (qL). Finally, we noticed that the qL at the canopy scale had lower sensitivity to light change than the single-leaf qL and that the light response of canopy-scale qL was influenced by the leaf area index during seasonal cycles. Overall, this study describes methods to accurately estimate sub-daily GPP from SIF in evergreen needleleaf forests and demonstrates that the interactions among light, canopy structure, and leaf physiology regulate the SIF-GPP relationship at the canopy scale. Further, it indicates the need to consider the description of light distribution within the canopy in next-generation terrestrial biosphere models, even if they incorporate SIF to constrain their parameterization. Thus, upscaling the established leaf-scale mechanistic SIF-GPP relationship or findings to canopy-scale applications still requires much work, especially when there are significant changes in environmental conditions and their within-canopy distributions. • Considering vertical light gradient improves sub-daily GPP estimation via SIF. • Two-leaf assumption helps in capturing the diurnal variations in GPP. • Canopy-scale qL is less sensitive to PAR than the single-leaf qL. [ABSTRACT FROM AUTHOR]

The uncertainty of carbon fluxes of the terrestrial ecosystem is the highest among all flux components, calling for more accurate and efficient means to monitor land sinks. Gross primary productivity (GPP) is a key index to estimate the terrestrial ecosystem carbon flux, which describes the total amount of organic carbon fixed by green plants through photosynthesis. In recent years, the solar-induced chlorophyll fluorescence (SIF), which is a probe for vegetation photosynthesis and can quickly reflect the state of vegetation growth, emerges as a novel and promising proxy to estimate GPP. The launch of Orbiting Carbon Observatory 2 (OCO-2) further makes it possible to estimate GPP at a finer spatial resolution compared with Greenhouse Gases Observing Satellite (GOSAT), Global Ozone Monitoring Experiment-2 (GOME-2) and SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY). However, whether the relationship between GPP and SIF is linear or non-linear has always been controversial. In this research, we proposed a new model to estimate GPP using SIF and the atmospheric CO2 concentration from OCO-2 as critical driven factors simultaneously (SIF-CO2-GPP model). Evidences from all sites show that the introduction of the atmospheric CO2 concentration improves accuracies of estimated GPP. Compared with the SIF-CO2-GPP linear model, we found the SIF-GPP model overestimated GPP in summer and autumn but underestimated it in spring and winter. A series of simulation experiments based on SCOPE (Soil-Canopy Observation of Photosynthesis and Energy) was carried out to figure out the possible mechanism of improved estimates of GPP due to the introduction of atmospheric CO2 concentrations. These experiments also demonstrate that there could be a non-linear relationship between SIF and GPP at half an hour timescale. Moreover, such relationships vary with CO2 concentration. As OCO-2 is capable of providing SIF and XCO2 products with identical spatial and temporal scales, the SIF-CO2-GPP linear model would be implemented conveniently to monitor GPP using remotely sensed data. With the help of OCO-3 and its successors, the proposed SIF-CO2-GPP linear model would play a significant role in monitoring GPP accurately in large geographical extents.

Solar induced chlorophyll fluorescence has been shown to be increasingly an useful proxy for the estimation of gross primary productivity (GPP), at a range of spatial scales. Here, we explore the seasonality in a continuous time series of canopy solar induced fluorescence (hereafter SiF) and its relation to canopy gross primary production (GPP), canopy light use efficiency (LUE), and direct estimates of leaf level photochemical efficiency in an evergreen canopy. SiF was calculated using infilling in two bands from the incoming and reflected radiance using a pair of Ocean Optics USB2000+ spectrometers operated in a dual field of view mode, sampling at a 30 min time step using custom written automated software, from early spring through until autumn in 2011. The optical system was mounted on a tower of 18 m height adjacent to an eddy covariance system, to observe a boreal forest ecosystem dominated by Scots pine. (Pinus sylvestris) A Walz MONITORING-PAM, multi fluorimeter system, was simultaneously mounted within the canopy adjacent to the footprint sampled by the optical system. Following correction of the SiF data for O2 and structural effects, SiF, SiF yield, LUE, the photochemicsl reflectance index (PRI), and the normalized difference vegetation index (NDVI) exhibited a seasonal pattern that followed GPP sampled by the eddy covariance system. Due to the complexities of solar azimuth and zenith angle (SZA) over the season on the SiF signal, correlations between SiF, SiF yield, GPP, and LUE were assessed on SZA 2 = 0.3. The diurnal responses of SiF, SiF yield, PAM estimates of effective quantum yield (ΔF/Fm′), and meteorological parameters demonstrated some agreement over the diurnal cycle. The challenges inherent in SiF retrievals in boreal evergreen ecosystems are discussed.

Satellite-based data of solar-induced chlorophyll fluorescence (SIF) and the near-infrared radiation reflected by vegetation (NIRvP) are being increasingly used for the estimation of vegetation gross primary product (GPP) at the global scale. Although SIF contains more physiological information than NIRvP, NIRvP can have higher data quality and spatio-temporal resolution. Therefore, the two variables can be considered complementary for GPP monitoring. Here, we propose a simple framework to combine SIF and NIRvP data from different data sources to generate an enhanced SIF product (eSIF). The original SIF data comes from the TROPOMI instrument onboard the Sentinel-5P mission, whereas NIRvP data are derived from MODIS spectral reflectance and ERA5 reanalysis data. The resulting eSIF product has a spatial resolution of 0.05° and a temporal resolution of 8 days, as well as a higher signal-to-noise ratio and a lower angular dependency than the original TROPOMI SIF data. Our results demonstrate that eSIF has similar spatial patterns to the original SIF but is more spatially continuous and less noisy. Comparisons with the FLUXCOM global GPP product show that eSIF has a more universal relationship with GPP than NIRvP for different grass/crop plant functional types (the coefficients of variation are 18.9% for slopes of GPP to eSIF and 27.3% for slopes of GPP to NIRvP), but NIRvP outperforms eSIF for tracking GPP for forest PFTs exclude BoENF. Moreover, eSIF is able to better track the seasonal variations in GPP related to environmental stresses. This study highlights that our methodology based on the combination of SIF and NIRvP is a promising approach for better monitoring of GPP. • An enhanced SIF product (eSIF) is introduced by combining SIF and NIRvP • eSIF shows increased data quality compared to original SIF • eSIF has a more universal relationship with GPP than NIRvP for grass/crop PFTs • NIRvP better tracks GPP for forest PFTs (except BoENF) than eSIF • The approach is simple and applicable to both global and site scales [ABSTRACT FROM AUTHOR]

Accurately estimating the dynamics of ecosystem photosynthesis in coastal wetlands is of paramount importance to the quantification of blue carbon and climate change. In this study, we investigated the relationship between the solar-induced chlorophyll fluorescence at 760 nm (SIF 760) and the gross primary productivity (GPP), as well as the underlying mechanisms in coastal salt marshes that are regularly flooded by tides, based on continuous observations of the SIF 760 and GPP throughout the growing season of Phragmites australis during 2019. The results show that the SIF 760 was significantly correlated with the GPP on the half-hourly, daily, and weekly scales in different phenological stages, and the linearity of the SIF 760 –GPP relationship generally improved as the time scale increased. Moreover, we found that the canopy structure (i.e., leaf area index and spatial distribution of leaf angles) plays a major role in explaining the relationship between the SIF 760 and photosynthesis on half-hourly and daily scales at the study site. Tidal flooding significantly suppressed the strength of the correlation between SIF 760 and GPP in the early and rapid growth vegetation stage. Furthermore, the tides generally lowered the degree of the correlation between the light use efficiency for photosynthesis (LUE p) and the SIF yield, and between the LUE p and the canopy SIF 760 escape probability. This indicates that tidal inundation diminished the roles of the plant physiology and canopy structure in explaining the relationship between the SIF 760 and photosynthesis, and thus, it had a negative effect on the SIF 760 -based GPP estimation. The results of this study demonstrate that tidal inundation exerts a significant influence on the relationships between the SIF 760 and GPP and their responses to the absorbed photosynthetically active radiation, which leads to a better understanding of the mechanism linking the SIF 760 and photosynthesis in tidal wetlands and provides new insights into reliable blue carbon quantification in a large scale domain. • The linearity of the SIF 760 –GPP relationship improved as time scale increased. • Canopy structure explains the relationship between SIF 760 and photosynthesis. • Tides significantly suppressed the SIF 760 –GPP relationship during green-up. • Tidal flooding diminished the role of plant physiology and canopy structure. • f esc is helpful for GPP estimation at half-hourly and daily scales. [ABSTRACT FROM AUTHOR]

• The influence of PAR on LUE and SIFyield for maize was investigated. • A hyperbolic function response model for LUE to PAR was built for maize. • SIFyield was proven less sensitive to PAR than LUE is for unstressed maize. Solar-induced chlorophyll fluorescence (SIF) has been shown to be an ideal indicator of vegetation gross primary productivity (GPP), but the variation in the ratio of the photosynthetic light use efficiency (LUE = GPP/APAR) to the total SIF quantum yield (SIF yield = SIF total /APAR) is an important source of uncertainty in SIF‒GPP models. Incident radiation is one of the key factors influencing LUE and SIF yield. In this study, to investigate the influence of PAR on LUE and SIF yield , pulse-amplitude-modulated (PAM) fluorometry was carried out at the leaf level along with tower-based continuous SIF‒GPP measurements at the canopy level for maize. LUE was found to decrease as PAR increased, following a hyperbolic function, at both the leaf level (R2 = 0.978) and canopy level (R2 = 0.460 for half-hourly averaged dataset; R2 = 0.341 for daily averaged dataset). However, the variation of SIF yield with PAR was found to be very small. By integrating the influence of PAR on LUE, the GPP estimation model based on the red band and near-infrared (NIR) band SIF for maize became more linear. For both the half-hourly and daily datasets, the values of R2 for the SIF-GPP model increased (e.g. from 0.573 to 0.718 for the half-hourly NIR band SIF), and the RMSE for the estimated GPP reduced (e.g. from 8.30 to 6.75 μmol CO 2 m−2 s−1 for the half-hourly NIR band SIF). These results highlight that the ratio of LUE to SIF yield is an important source of uncertainty in SIF‒GPP models and should be carefully corrected. The results also show that PAR is a key factor influencing this ratio. This PAR-based LUE model can be integrated not only in SIF‒GPP models but also in other LUE-related GPP estimation models for unstressed maize. [ABSTRACT FROM AUTHOR]

The uncertainty of carbon fluxes of the terrestrial ecosystem is the highest among all flux components, calling for more accurate and efficient means to monitor land sinks. Gross primary productivity (GPP) is a key index to estimate the terrestrial ecosystem carbon flux, which describes the total amount of organic carbon fixed by green plants through photosynthesis. In recent years, the solar-induced chlorophyll fluorescence (SIF), which is a probe for vegetation photosynthesis and can quickly reflect the state of vegetation growth, emerges as a novel and promising proxy to estimate GPP. The launch of Orbiting Carbon Observatory 2 (OCO-2) further makes it possible to estimate GPP at a finer spatial resolution compared with Greenhouse Gases Observing Satellite (GOSAT), Global Ozone Monitoring Experiment-2 (GOME-2) and SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY). However, whether the relationship between GPP and SIF is linear or non-linear has always been controversial. In this research, we proposed a new model to estimate GPP using SIF and the atmospheric CO2 concentration from OCO-2 as critical driven factors simultaneously (SIF-CO2-GPP model). Evidences from all sites show that the introduction of the atmospheric CO2 concentration improves accuracies of estimated GPP. Compared with the SIF-CO2-GPP linear model, we found the SIF-GPP model overestimated GPP in summer and autumn but underestimated it in spring and winter. A series of simulation experiments based on SCOPE (Soil-Canopy Observation of Photosynthesis and Energy) was carried out to figure out the possible mechanism of improved estimates of GPP due to the introduction of atmospheric CO2 concentrations. These experiments also demonstrate that there could be a non-linear relationship between SIF and GPP at half an hour timescale. Moreover, such relationships vary with CO2 concentration. As OCO-2 is capable of providing SIF and XCO2 products with identical spatial and temporal scales, the SIF-CO2-GPP linear model would be implemented conveniently to monitor GPP using remotely sensed data. With the help of OCO-3 and its successors, the proposed SIF-CO2-GPP linear model would play a significant role in monitoring GPP accurately in large geographical extents. [ABSTRACT FROM AUTHOR]

Solar induced chlorophyll fluorescence has been shown to be increasingly an useful proxy for the estimation of gross primary productivity (GPP), at a range of spatial scales. Here, we explore the seasonality in a continuous time series of canopy solar induced fluorescence (hereafter SiF) and its relation to canopy gross primary production (GPP), canopy light use efficiency (LUE), and direct estimates of leaf level photochemical efficiency in an evergreen canopy. SiF was calculated using infilling in two bands from the incoming and reflected radiance using a pair of Ocean Optics USB2000+ spectrometers operated in a dual field of view mode, sampling at a 30 min time step using custom written automated software, from early spring through until autumn in 2011. The optical system was mounted on a tower of 18 m height adjacent to an eddy covariance system, to observe a boreal forest ecosystem dominated by Scots pine. (Pinus sylvestris) A Walz MONITORING-PAM, multi fluorimeter system, was simultaneously mounted within the canopy adjacent to the footprint sampled by the optical system. Following correction of the SiF data for O2 and structural effects, SiF, SiF yield, LUE, the photochemicsl reflectance index (PRI), and the normalized difference vegetation index (NDVI) exhibited a seasonal pattern that followed GPP sampled by the eddy covariance system. Due to the complexities of solar azimuth and zenith angle (SZA) over the season on the SiF signal, correlations between SiF, SiF yield, GPP, and LUE were assessed on SZA <50° and under strictly clear sky conditions. Correlations found, even under these screened scenarios, resulted around ~r2 = 0.3. The diurnal responses of SiF, SiF yield, PAM estimates of effective quantum yield (ΔF/Fm′), and meteorological parameters demonstrated some agreement over the diurnal cycle. The challenges inherent in SiF retrievals in boreal evergreen ecosystems are discussed. [ABSTRACT FROM AUTHOR]