Search

• 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]

• Over 80 % of subtropical China exhibited increased photosynthetic activity, particularly in southern and central-western regions. • Soil moisture showed significant lag effects, while solar radiation and vapor pressure deficit had combined lag and cumulative effects on photosynthesis. • Considering lag and cumulative effects, the area with significant correlations between climatic factors and vegetation SIF increased substantially. • The interaction of climatic factors, especially involving vapor pressure deficit, significantly enhanced their impact on photosynthesis. Monitoring vegetation photosynthesis in China's subtropical regions using remote sensing is challenging because of the complex ecosystems and climate variability. Previous studies often pay less attention on the influence of multiple climatic factors on the temporal effects (lag and accumulation) of vegetation photosynthesis, thereby underestimating their impact. This study utilizes a dataset comprising Solar-induced chlorophyll fluorescence (SIF) data (GOSIF product), MODIS Land Cover product (MCD12C1), and various climatic variables. Analytical methods including Theil-Sen Median trend analysis, the Mann-Kendall test, partial correlation analysis, and the optimal parameter-based geographical detector (OPGD) model were employed to explore the temporal dynamics of subtropical vegetation SIF responses to climatic factors and to identify their climate drivers in subtropical China. The study findings indicate that (1) vegetation photosynthesis, as indicated by SIF, exhibited an increasing trend in the majority of Chinese subtropical regions, which constitute over 80 % of the study area, with particularly pronounced enhancements in the southern and central western parts of the Chinese subtropics. (2) Soil moisture primarily exhibits lag effects on SIF, particularly in evergreen needleleaf forests, deciduous broadleaf forests, and mixed forests, whereas temperature does not exhibit significant temporal effects. Solar radiation and vapor pressure deficits impact SIF through both lag and accumulation effects. Under the lag and accumulation effects, the proportion of significant correlations between climatic factors and vegetation SIF increases by 36.71 % ∼ 43.8 %, excluding temperature. (3) Temperature is the dominant factor affecting vegetation SIF, particularly in the evergreen needleleaf forest. Interactions between climatic factors have a significantly stronger influence on SIF than individual factors. Notably, the explanatory power of the vapor pressure deficit increases substantially when it interacts with other factors. Studying the lag and accumulation effects of climatic factors on photosynthesis aids in accurately predicting vegetation responses to climate change, thereby improving the accuracy of global carbon cycle models and guiding the development of carbon sequestration management strategies. [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]

Solar-induced chlorophyll fluorescence (SIF) is a signal directly and functionally related to photosynthetic activity and thus holds great promise for large-scale agricultural monitoring. However, the coarse spatial resolution of existing satellite SIF observations usually consist of mixed SIF signals contributed by different crop types with distinct phenology (modulated by management practices) and varying SIF emission capacities, which impedes effective utilization of existing SIF records for large-scale agricultural applications. This study makes the first effort to overcome this challenge by developing a sub-pixel SIF extraction framework for corn and soybean in the US Corn Belt as a case study. Here we developed a machine learning (ML) based sub-pixel SIF extraction framework using Orbiting Carbon Observatory 2 (OCO-2), whose high-resolution SIF acquired along orbits at nadir enables the identification of relatively pure pixels dominated by single corn or soybean crops, facilitating validation of the developed framework. To achieve this, we first generated artificially mixed SIF pixels from pure pixels randomly weighted by fractional area coverage. We then employed a standard feed forward artificial neural network (ANN) to estimate sub-pixel SIF for corn and soybean respectively, using the following predictors: total mixed SIF, spectral reflectance of corn/soybean (from Moderate Resolution Imaging Spectroradiometer MODIS), and the fractional area coverage of corn/soybean (derived from CropScape-Cropland Data Layer). Our results demonstrated that the estimated sub-pixel SIF could successfully reproduce the original pure SIF values constituting the mixed pixel, with a normalized root mean squared error (NRMSE) of <10% during the peak growing season. We further demonstrated that this ANN-based framework substantially outperforms the parsimonious linear extraction methods. This developed sub-pixel SIF extraction framework was then applied to generate regional-scale SIF maps for corn and soybean at 0.05° in the US Midwest. Although tested for corn and soybean only, the developed framework has the potential to resolve sub-pixel SIF of more endmembers from coarse SIF observations. [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) emitted from photosystem I (PSI) and photosystem II (PSII) is characterized by two peaks centered in the red and far-red spectral regions. SIF provides a unique remotely sensible signal to track plant photosynthetic dynamics. Compared with far-red SIF, red SIF (RSIF) is more strongly linked to PSII and thus with plant photosynthetic activity, but is subject to stronger reabsorption within leaves and canopies. This hinders the understanding and use of canopy RSIF observations (RSIF obs), which is only a small fraction of the total RSIF emitted by the photosystems (RSIF total). Deriving RSIF total from RSIF obs is still challenging due to retrieval uncertainty, limited availability of RSIF obs and spectral overlap with chlorophyll a bsorption. To address the challenges associated with deriving RSIF total , we propose an exploratory method framework that combines canopy far-red SIF observations (FRSIF obs) and leaf chlorophyll content (LCC) to derive RSIF total. We first downscale FRSIF obs from canopy to leaf, and then leverage LCC information to estimate RSIF at the leaf level. Finally, we incorporate LCC information in the subsequent downscaling of RSIF from leaf to photosystem. To evaluate our approach, we use ground-based observation data in three crop types (rice, wheat, and maize) and SCOPE model simulations. Our results demonstrate that the seasonal patterns of RSIF total show a close agreement with the seasonal patterns of gross primary production (GPP) and absorbed photosynthetic active radiation (APAR). More importantly, RSIF total slightly outperforms FRSIF obs in estimating GPP for the three crop types. Our study has also revealed a strong linear relationship between the escape probability of RSIF total (f esc_R) and the RSIF obs /FRSIF obs ratio affected by LCC. The simplicity and robustness of our approach, along with its potential application in satellite remote sensing, will contribute to the improvement of large-scale GPP estimation and photosynthetic phenology detection. Moreover, our investigation of f esc_R will contribute to a better understanding the physiological and non-physiological dynamics of RSIF obs. • Deriving RSIF total by combining FRSIF obs and LCC. • Our approach improves GPP estimation by deriving RSIF total. • Phenological metrics of RSIF total show agreement with those of GPP. • The ratio of RSIF obs and FRSIF obs explains f esc_R. [ABSTRACT FROM AUTHOR]

Highlights • Canopy conductance and SIF show similar diurnal and seasonal patterns. • Tower-based and satellite-based SIF significantly correlate with canopy conductance. • Model integrated SIF generally yields reasonable estimation of transpiration. • Drought decouples the relationship between carbon and water exchange. Abstract Vegetation transpiration (T) is the process of plant water loss through the stomata on the leaf surface and plays a key role in the energy and water balance of the land surface, especially with dense vegetation cover. To date, however, estimation of ecosystem-scale T is still rather uncertain mainly due to errors in modeling canopy resistance or conductance. Considering the intrinsic link between photosynthesis and chlorophyll fluorescence, the recent available remote sensing of solar-induced chlorophyll fluorescence (SIF) provides a valuable opportunity to estimate plants T at large scales. In this study, we demonstrate how remote sensing of SIF relates to canopy stomatal conductance and transpiration at diurnal and seasonal scales with continuous ground measurements of SIF at three flux sites in forest, cropland and grassland ecosystems. The results show that both ground and spaceborne SIF observations are good indicators of canopy conductance at both diurnal and seasonal scales (R2 = 0.57 and 0.74 for forest, R2 = 0.62 and 0.80 for cropland, R2 = 0.52 and 0.63 for grassland, respectively). Then, empirical SIF-based canopy conductance models are employed to estimate hourly and daily transpiration. We evaluate our ecosystem T estimations against latent heat fluxes measured by eddy covariance systems with more satisfactory results for forest (R2 = 0.57 and 0.71), and cropland (R2 = 0.77 and 0.83) than for grassland (R2 = 0.13 and 0.22) at hourly and daily time scales. Our results suggest the potential of remotely-sensed SIF for estimating canopy conductance and plant transpiration, but a more mechanistic understanding is needed for their link. [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]

Solar-Induced chlorophyll Fluorescence (SIF) could be used as an indicator of photosynthetic status due to the close relationship between SIF and the photosynthetic apparatus. Terrestrial SIF is emitted throughout the red and near-infrared spectrum and is characterized by two peaks centered around 685 nm and 740 nm, respectively. In this study, we present a data-driven approach to reconstruct the terrestrial SIF spectrum from measurements by TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 precursor mission. This approach makes use of solar Fraunhofer lines in the combined spectral windows devoid of strong atmospheric absorption features to retrieve SIF signal from the solar radiation reflected by the surface and atmosphere system. Information contents are mainly from the two windows close to the red and far-red SIF peaks, 663–686 nm and 743–758 nm. A linear forward model represented as an addition of the SIF-free radiance spectrum and the SIF component is proposed with a proper selection of its parameter settings. The SIF component was simulated as linear combinations of 2 basis SIF spectra. Through inverting the linear forward model, the SIF spectrum was retrieved from the solar radiation reflected by the surface and atmosphere system. The evaluation of the retrieval results is performed by inter-comparison with other SIF datasets. The comparisons display similar spatial distributions for the weekly global SIF composites for the first two weeks in June and December of 2019 and July and December of 2021. Especially the comparison of the far-red SIF datasets with other dedicated far-red SIF retrievals demonstrates close agreement, indicating consistency among the retrieval approaches. The reconstructed TROPOMI red SIF shows improved and more reasonable spatiotemporal distributions. The retrieval uncertainty for the weekly global composite is about 12% and 2% of the peak red and far-red SIF values, respectively, which can be considered as satisfactory error thresholds for global composites of SIF observations. Different spectral features for several typical biomes from reconstructed SIF spectra suggest that red and far-red SIF may carry complementary information on photosynthetic function and biophysical properties of the plant. For the first time, the reconstruction of the SIF spectrum is achieved for spaceborne measurements with the potential to open new applications for better understanding of the ecosystem function. • First retrieval of the full SIF spectrum from space measurements by TROPOMI. • The retrieval is based on a data-driven method by exploiting solar Fraunhofer lines. • Far-red SIF results are highly consistent with other dedicated retrievals. • Red and far-red SIF may carry complementary information on plant's properties. [ABSTRACT FROM AUTHOR]

Predicting crop yield before harvest and understanding the factors determining yield at a regional scale is vital for global food security, supply chain management in agribusiness, crop and insurance pricing and optimising crop production. Often satellite remote sensing data, environmental data or their combinations are used to model crop yield at a regional scale. However, their contribution, including that of recently developed remote sensing data like solar-induced chlorophyll fluorescence (SIF) and near-infrared reflectance of vegetation (NIRv), are not explored sufficiently. This study aims to assess the contribution of weather, soil and remote sensing data to estimate wheat yield prediction at a regional scale. For this, we employed four types of remote sensing data, thirteen climatic variables, four soil variables, and nationwide yield data of 14 years combined with statistical learning methods to predict winter wheat yield in the Conterminous United States (CONUS) and access the role of predicting variables. Machine-learning algorithms were used to build yield prediction models in different experimental settings, and predictive performance was evaluated. Further, the relative importance of predictor variables for the models was assessed to gain insight into the model's behaviour. NIRv and SIF data are found to be promising for crop yield prediction. The model with only NIRv data explained up to 64% of the variability in yield, and adding SIF data improved it to 69%. We also found that vegetation indices, SIF, climate and soil data all contribute unique and overlapping information to crop yield prediction. The study also identified important variables and the time of the growing period when these variables have higher explanatory power for winter wheat yield prediction. This study enhanced our knowledge of yield-predicting variables, which will contribute to optimising the yield and developing better yield prediction models. [Display omitted] • Used machine learning methods to integrate multiple data for crop yield prediction. • The role of different variables in yield prediction was also assessed. • NIRv and SIF data are found to be promising for crop yield prediction. • RS and environmental data provide unique and overlapping information. • Identified key variables and the growth phase when they have high explanatory power. [ABSTRACT FROM AUTHOR]

Abstract Solar-Induced chlorophyll Fluorescence (SIF) can serve as an early and non-invasive indicator of the functioning and status of vegetation due to its close link to photosynthetic activity. Most existing approaches retrieve SIF at around few discrete absorption lines. However, the full SIF spectrum can provide more information on the functional status of photosynthetic machinery. European Space Agency's FLuorescence EXplorer (FLEX) mission, to be launched in 2022, is dedicated to the accurate reconstruction of the full SIF spectrum over land and incorporates the heights and positions of the two SIF peaks and the total fluorescence emission (spectrally-integrated value) into planned Level-2 products. In this paper, an advanced Fluorescence Spectrum Reconstruction (aFSR) method was proposed to reconstruct the full SIF spectrum by capitalizing on the features of existing methods. The aFSR method used linear combinations of basis spectra to approximate the spectra of SIF and the reflectance factor and exploited all available bands within the spectral range of SIF emission for spectral fitting of SIF and reflected radiance. The number of basis spectra of the reflectance factor used was self-adaptively determined based on the Bayesian information criterion. A comprehensive intercomparison between the aFSR method and three other methods (i.e., the Fluorescence Spectrum Reconstruction method, the Full-spectrum Spectral Fitting Method, and the SpecFit method) was performed using simulated and experimental datasets. For simulated datasets, the impact of spectral resolution (SR), signal-to-noise ratio (SNR), atmospheric correction, canopy structure, leaf biochemical parameters and directional effect on the accuracy of SIF spectrum reconstruction was considered. Results show that while all methods could achieve the accuracy standard set by the FLEX mission (average absolute relative error of spectrally-integrated SIF <10%) when spectral resolving power and SNR were high (e.g., SR ≤ 0.3 nm and SNR ≥ 700), aFSR generally provided the highest reconstruction accuracy. For the first time we investigated the performance of the SIF spectrum reconstruction on 3-D radiative transfer (RT) simulations and compared with that on typical 1-D simulations. The increase of canopy heterogeneity from 1-D to 3-D did not noticeably deteriorate the accuracy of aFSR, implying that aFSR was applicable to different canopy structures. The aFSR method was also more robust than other methods as it was less affected by atmospheric correction and directional effect. For the experimental dataset, the SIF spectra reconstructed by aFSR agreed well with literature in terms of shape, magnitude and diurnal variation and were in agreement with the other methods: the coefficient of determination and the root-mean-square error between the reconstruction results of aFSR and the average of the SIF spectra reconstructed through three other methods were higher than 0.93 and lower than 0.09 W·m−2·sr−1·μm−1, respectively. Highlights • A novel method to reconstruct the full SIF spectrum is proposed. • The method is evaluated by comprehensive intercomparison with existing methods. • Method is tested for simulated structurally homogeneous and heterogeneous canopies. [ABSTRACT FROM AUTHOR]

Solar-Induced Chlorophyll Fluorescence (SIF) is an emission of light in the 650–850 nm spectral range from the excited state of the chlorophyll-a pigment after absorption of photosynthetically active radiation (PAR). As this is directly linked to the electron transport chain in oxygenic photosynthesis, SIF is a powerful proxy for photosynthetic activity. SIF observations are relatively new and, while global scale measurements from satellites using high-resolution spectroscopy of Fraunhofer bands are becoming more available, observations at the intermediate canopy scale using these techniques are sparse. We present a novel ground-based spectrometer system - PhotoSpec - for measuring SIF in the red (670–732 nm) and far-red (729–784 nm) wavelength range as well as canopy reflectance (400–900 nm) to calculate vegetation indices, such as the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), and the photochemical reflectance index (PRI). PhotoSpec includes a 2D scanning telescope unit which can be pointed to any location in a canopy with a narrow field of view (FOV = 0.7°). PhotoSpec has a high signal-to-noise ratio and spectral resolution, which allows high precision solar Fraunhofer line retrievals over the entire fluorescence wavelength range under all atmospheric conditions using a new two-step linearized least-squares retrieval procedure. Initial PhotoSpec observations include the diurnal SIF cycle of single broad leaves, grass, and dark-light transitions. Results from the first tower-based measurements in Costa Rica show that the instrument can continuously monitor SIF of several tropical species throughout the day. The PhotoSpec instrument can be used to explore the relationship between SIF, photosynthetic efficiencies, Gross Primary Productivity (GPP), and the impact of canopy radiative transfer, viewing geometry, and stress conditions at the canopy scale. [ABSTRACT FROM AUTHOR]

Accurate and non-destructive monitoring of wheat nitrogen nutrition is of great significance for field fertilizer management to ensure crop yield and quality, reduce environmental pollution, and improve economic benefits. Compared with spectral vegetation indices (which are sensitive to greenness and structural parameters), or active fluorescence (which is limited to small-scale studies), solar-induced chlorophyll fluorescence (SIF) provides a direct measure of crop response to environmental stress and photosynthetic characteristics. However, there has been few studies comparing agronomic parameters, photosynthetic parameters, vegetation indices and SIF as an indicator of nitrogen status. In this paper, we therefore explore these measures as tools for monitoring nitrogen nutrition. During the 2016–2017 growing season, we conducted a field experiment in Rugao, Jiangsu Province, China, using winter wheat (Triticum aestivum L.) and different nitrogen treatments. The sensitivity of SIF indices, vegetation indices, photosynthetic parameters and agronomic parameters to crop nitrogen status were compared. Our results demonstrated that, compared with vegetation indices and agronomic parameters, the ratio of SIF emission peaks (FY 687 /FY 761) responded to nitrogen status most rapidly at both the leaf and canopy scales, as soon as the fourth day after treatment (DAT4). A wheat nitrogen nutrition index (NNI), based on FY 687 /FY 761 , was used to construct a leaf dry matter (LDM-based NNI) diagnostic model, which will be beneficial for monitoring and diagnosing the nitrogen nutrition status of wheat leaves. Our results also illuminate the physiological mechanism that enables SIF to be used as a tool to monitor nitrogen nutrient status, primarily through changes in the proportion of light energy distribution. These findings provide theoretical and technical support for monitoring and diagnosing wheat nitrogen nutrition status based on SIF technology. • SIF indices can better trace the change of crop nitrogen status than VIs. • Non-destructive diagnosis of nitrogen was achieved using the index FY 687 / FY 761. • The LDM-based NNI diagnostic model has high accuracy. • The light energy distribution explains the mechanism of SIF to trace N status. [ABSTRACT FROM AUTHOR]

Several satellite-based solar-induced chlorophyll fluorescence (SIF) products have progressively emerged and have been developed in recent years. However, till date, no direct validation has been conducted on existing satellite-based SIF products. In this study, validation of two groups of TROPOspheric Monitoring Instrument (TROPOMI) SIF products, namely TROPOSIF Caltech (containing far-red and red TROPOSIF Caltech datasets) and TROPOSIF ESA (containing TROPOSIF 735 and TROPOSIF 743 datasets that are retrieved from two different retrieval windows), was conducted using tower-based SIF measurements over seven sites. Several issues and potential obstacles emerged while matching satellite-based and in situ SIF retrievals, including spatial scale mismatch. To overcome the spatial scale mismatch between the satellite data and ground observations, a near-infrared reflectance of vegetation (NIRv)-scaled approach was employed to mitigate the spatial difference between the locations of specific sites and the matched TROPOSIF samples using Sentinel-2 imagery. Other issues related to retrival methods and instrument differences were examined. Subsequently, the 3FLD retrieval method was chosed for the in situ data. The validation results showed that the three far-red TROPOSIF datasets exhibit slightly different performances in terms of the validation accuracy; the R2 for TROPOSIF Caltech , TROPOSIF 735 , and TROPOSIF 743 was 0.43, 0.33 and 0.40, respectively, which is asociated with root-mean-square error(RMSE) values of 0.59, 0.42 and 0.57 mW m−2 sr−1 nm−1, respectively. However, red TROPOSIF Caltech exhibited no significant correlation with tower-based SIF with R2 of 0.02 and RMSE of 0.34 mW m−2 sr−1 nm−1. Furthermore, the validation results at different sites varied, with R2 ranging from 0.01 to 0.70. Uncertainties still exist in the validation of the four TROPOSIF datasets, which are attributed to some unresolved issues, such as the limited quality of in situ SIF retrievals and the spatial scaling difference. Thus, to fully utilize satellite-based SIF products for wide ranging applications, further improvements in SIF product quality are urgently required at both ground and satellite scales. • TROPOMI SIF products are firstly validated using tower-based SIF measurements. • Far-red TROPOMI SIF is more accurate than red TROPOMI SIF. • Three far-red TROPOSIF datasets perform slightly differently on validation accuracy. • The validation accuracy of TROPOSIF is influenced by site heterogeneity. [ABSTRACT FROM AUTHOR]

[Display omitted] • Tree ring is an ideal proxy for forest productivity. • New sampling method was developed in order to estimate forest productivity using tree rings. • Chronologies can be used to reconstruct forest productivity on long time scales. The lack of long-term high-resolution data makes it difficult to determine historical and future trends in net primary productivity (NPP). This study used tree rings as a proxy to investigate the dynamics of NPP in Tianshan forests where coniferous forests are the major species and the other species are deficient. All trees and some tree cores from five sample plots in different geographic locations in the western Tianshan Mountains were selected to reconstruct forest NPP data from 1950 to 2020. Multiple historical events that resulted in large-scale terrestrial carbon fluxes were identified and the existence of 28a and 17a time-scale cycles of historical forest NPP was observed. We discovered that the reconstructed forest NPP in the western Tianshan Mountains did not significantly correlate with satellite-based products (e.g., MODIS NPP, solar-induced chlorophyll fluorescence data). This result was attributed to the lag of forest growth for climate, the accuracy of the satellite-based products and statistical errors due to the short overlap time. We analysed the uncertainties in reconstructing historical forest NPP using tree ring widths and proposed corresponding solutions. We concluded that the reconstructed data remain the ideal proxy for regions lacking long-term empirical data and exhibit a high degree of confidence for expressing trends in forest productivity change over long time series. [ABSTRACT FROM AUTHOR]

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]

Solar-induced chlorophyll fluorescence (SIF) has been shown to be a novel proxy for terrestrial gross primary production (GPP). A growing number of ground-based automatic SIF observation systems equipped with hemispherical-conical and bi-hemispherical observation configurations have been developed in synergy with EC flux measurements across different ecosystems. However, the difference in the canopy SIF observed by these two types of configurations has not been well studied, which poses challenges in evaluating their performance in tracking GPP. In this study, we investigated SIF from both hemispherical-conical and bi-hemispherical observation configurations for their ability to track GPP in a maize field during the 2020 growth season. We found that bi-hemispherical SIF observations (SIF Hemis) showed higher correlations with GPP at both diurnal and seasonal scales, and the superiority of SIF Hemis for GPP estimation was also supported by Soil-Canopy-Observation of Photosynthesis and the Energy balance (SCOPE) model simulations. In addition, we found that the SIF Hemis -GPP model established at a satellite overpass time (e.g., 09:30) outperformed the corresponding SIF Nadir -GPP model in estimating both the half-hourly and daily GPP. The underlying mechanism for the advantage of this SIF Hemis -GPP relationship was elucidated by a simplified geometrical optical model, which showed that the diurnal patterns of the observed sunlit and shaded leaves for the SIF Hemis were consistent with those of the canopy GPP. Our study recommends a bi-hemispherical configuration setup for its superiority in monitoring GPP dynamics. • Hemispherical-conical observed SIF at nadir (SIF Nadir) shows hotspot at noon. • The diurnal cycle of sunlit leaf observed by SIF Hemis matches with that of GPP. • SIF Hemis is better related to GPP than SIF Nadir at diurnal and seasonal cycles. [ABSTRACT FROM AUTHOR]

Recent advances in remotely sensed solar-induced chlorophyll fluorescence (SIF) have provided an exciting and promising opportunity for estimating gross primary production (GPP). Previous studies mainly focused on the linear correlation between SIF and GPP and the slope of the SIF-GPP relationship, both of which lack rigorous consideration of the seasonal trajectories of SIF and GPP. Here, we investigated the timing of seasonal peaks of far-red SIF and GPP in soybean fields by integrating tower data, satellite data, and process-based Soil Canopy Observation of Photosynthesis and Energy (SCOPE, v2.0) model simulations. We found inconsistency between the seasonal peak timing of far-red SIF and GPP in three of four soybean fields based on tower far-red SIF and eddy-covariance measurements. In particular, far-red SIF reached its seasonal maximum 14–17 days earlier than GPP. This far-red SIF-GPP difference in peak timing degraded the correlation between sunny-day far-red SIF and GPP at daily scale (Pearson r = 0.83–0.87 at the site with 14–17 days difference and Pearson r = 0.96 at the site with no difference), and it can be explained by a divergence in the seasonality between absorbed photosynthetic active radiation (APAR) and canopy chlorophyll content (Chl Canopy). We found that the seasonality of far-red SIF - a byproduct of the light reactions of photosynthesis - was primarily controlled by APAR, whereas GPP seasonality was dominated by Chl Canopy. Further, SCOPE model simulations showed that the seasonal patterns of leaf area index (LAI), leaf chlorophyll content (Chl Leaf) and leaf angle distribution (LAD) could affect the different peak timing of SIF and GPP and consequently the seasonal relationship between far-red SIF and GPP. A further increase in LAI after the fraction of light absorption (FPAR) saturates and a later peak of Chl Leaf compared to LAI results in a later peak of GPP compared to far-red SIF. More horizontal leaf angles can further exacerbate this difference. Our results advance mechanistic understanding of the SIF-GPP relationships and combining chlorophyll content information with SIF could potentially improve remote-sensing-based GPP estimation. • We did a rigorous seasonality investigation of soybean canopy SIF and GPP. • SIF reaches seasonal maximum 14–17 days earlier than GPP at three of four sites. • SIF and APAR reach seasonal peak at similar times. • GPP and canopy chlorophyll content (Chl Canopy) reach peak at similar times. • Later peak of LAI and Chl Leaf than FPAR caused the later peak of GPP than SIF. [ABSTRACT FROM AUTHOR]

• The physiological and non-physiological dynamics of SIF under herbicide stress are quantified. • SIF captures herbicide-induced photosynthetic anomalies in heterogeneous cropland ecosystems. • SIF, NIRv and GPP have different responses to herbicide stress. • The link between SIF and GPP was disturbed after herbicide treatment. • ΦF dominates the variation of SIF in the early stage after herbicide treatment. Solar-induced chlorophyll fluorescence (SIF) has shown great potential for detecting changes in vegetation function under herbicide stress. However, how physiological (ΦF, canopy SIF emission efficiency) and non-physiological (e.g., structure and illumination) dynamics regulate canopy SIF, and the coupling between SIF and gross primary production (GPP) under herbicide stress remains unclear. Here, we conducted continuous eddy covariance flux and far-red SIF measurements during the early stage of maize in an herbicide-resistant maize field, where herbicide exclusively affects weeds. We investigated the performance of SIF, GPP, and vegetation indices (VIs) in capturing herbicide stress and then explored the sensitivity of SIF to the effects of herbicide treatments by disentangling canopy SIF into the physiological (ΦF) and non-physiological components (NIRvP). We found that SIF rapidly increased in response to the herbicide and that GPP decreased, and that both were more responsive than VIs in capturing the early effects of herbicides. Thus, the opposing responses in SIF and GPP disrupted their otherwise linear relationship during herbicide treatment. More importantly, we found that the increased ΦF dominated the variation of SIF during the early stages of herbicide stress, while the influence of NIRvP was prominent in the variability of SIF in the absence of herbicide. By unraveling its physiological and non-physiological contributions, our findings advance our understanding of how SIF responds to herbicide stress in heterogeneous cropland and will improve our interpretation of SIF as a tool for monitoring photosynthesis. [ABSTRACT FROM AUTHOR]

• We compared vegetation phenology from a SIF product (CSIF) and from MODIS NDVI. • We found similar spatial patterns in CSIF and NDVI derived phenology. • NDVI data indicated an earlier start and later end of the growing season than CSIF. • Models based on leaf greenness may overestimate photosynthesis active period. Vegetation phenology is highly sensitive to climate change, although the data and methods used to estimate key phenological states can influence this sensitivity. Because of its direct relation to leaf photosynthetic carbon uptake, remotely sensed solar-induced chlorophyll fluorescence (SIF) can provide new insight assessing changes in vegetation phenology. Here, we investigated the potential of using a SIF time series product named contiguous SIF (CSIF) to estimate spring, summer, and autumn phenology in the extratropical Northern Hemisphere (>30°N) and compared the results with those based on Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) for the period 2001–2018. Overall, we found similar spatial patterns in phenological states. However, specific dates of key phenological events differed when using CSIF vs. MODIS NDVI data. NDVI data indicated that the growing season started earlier (by 10.1 days on average) and ended later (11.5 days on average) relative to CSIF data. This implies that actual periods for photosynthetic activity are shorter (by 21.6 days on average) than those estimated from vegetation indices more directly related to changes in canopy structure. These large differences between results from NDVI and that from CSIF suggest that vegetation indices such as NDVI seem to overestimate the period for active photosynthesis over the extratropical Northern Hemisphere. Furthermore, while phenology of the early growing season is dominated by temperature for both NDVI and CSIF data, phenology of the late growing season is mainly controlled by temperature for NDVI but by precipitation for CSIF. Our findings were further confirmed by other SIF (GOME-2 SIF) and vegetation index (MODIS EVI) datasets. Phenology modes in Earth system modelling are often parameterized using leaf unfolding and senescence from either station or satellite observations. Our results imply that canopy structure-based parameterization schemes may have overestimated photosynthesis active period, and thus productivity responses. We conclude that SIF data offers a novel and unique approach for assessing phenological change - one that is more directly tied to the carbon cycle and how it is being influenced by climate change. [ABSTRACT FROM AUTHOR]

Recent advances in instruments and retrieval methods enable measurements of solar-induced chlorophyll fluorescence (SIF) across a wide range of scales. Radiative transfer (RT) models for simulating scattering and (re-)absorption of SIF provide a powerful tool to study the upscaling of SIF signal from leaf level to terrestrial ecosystems. Based on the Monte Carlo ray-tracing (MCRT) model, WPS (Weighted Photon Spread), we made major extensions with new functionalities and systematic evaluation of the new modules. By modeling the radiative coupling between atmosphere and land surface with the same MCRT method, the non-fluorescent and SIF radiance received by sensors can be simulated at levels from top-of canopy to top-of-atmosphere (TOA) in a coherent manner. New extension to represent the three-dimensional (3-D) canopies with geometrical primitives composed of turbid medium makes the hyperspectral simulation (especially SIF) for a sensor with medium spatial resolution at kilometer-scale feasible and practical. Evaluations through ROMC (Radiation transfer model intercomparison Online Model Checker) show that the accuracy of the new module of 3-D structure representation in WPS is within 1% of the reference solution. The spectra of TOA radiance and SIF and their components simulated at nadir by WPS agree closely with those simulated by the coupled SCOPE and MODTRAN models with the coefficient of determination (R2) higher than 0.99 and the average absolute relative error (AARE) lower than 6.39%; for angular distributions of TOA radiance and SIF at 685 nm and 740 nm, R2 is higher than 0.81 and AARE is lower than 6.94%. Comparisons of the spectra of TOA radiance and SIF and their components simulated at nadir by WPS and the DART model give R2 higher than 0.99 and AARE lower than 3.5%; R2 is higher than 0.92 and AARE is lower than 5.92% for the TOA angular simulations. The WPS model was also evaluated by hyperspectral measurements through unmanned aerial vehicle at different altitudes, which shows that WPS can reproduce the spectral features of a rapeseed crop. WPS can be used as a versatile tool to assess the impacts of various factors on the SIF signal and to evaluate the SIF retrieval methods under different conditions. • Major extensions of the WPS model and systematic evaluation of the new modules. • Radiative coupling between atmosphere and land with the coherent MCRT method. • Representing the 3-D canopies with geometrical primitives composed of turbid medium. • Intercomparisons with the coupled SCOPE and MODTRAN models and the DART model. • Intercomparison exercise for the RT modeling of canopy SIF is proposed. [ABSTRACT FROM AUTHOR]

• Seedling rapeseed Pn correlated with SIF-VIs derived from UAV data significantly. • Composite indices of SIF × VIs further improved the correlation with Pn. • Photosynthesis-related plant status could be monitored by multi-source UAV data. Net photosynthesis rate (Pn) can be used to characterize the health status of plants and their ability to accumulate organic matter. In this study, remotely sensed vegetation indices (VIs) and solar-induced chlorophyll fluorescence (SIF) were retrieved to build regression models to estimate rapeseed canopy Pn. Multi-source unmanned aerial vehicle (UAV) remote sensing data collected from seedling stage rapeseed were used in this study. The results showed that Pn was significantly related to traditional VIs and SIF (R2 = 0.52, p < 0.01). A quadratic polynomial regression model built using the normalized difference vegetation index performed the best on the inversion of Pn (R2 = 0.63, RMSE = 2.56, NRMSE = 0.18). Moreover, this study coupled SIF with traditional VIs by mathematical operations. The composite indices obtained by multiplication resulted in increased correlations. The inversion model established using SIF × VARI (visible atmospherically resistant index) achieved the best overall performance with 0.14 increase in R2 (0.54–0.68) and 0.48 decrease in RMSE (2.87–2.39) compared to SIF, 0.13 increase in R2 (0.55–0.68) and 0.45 decrease in RMSE (2.84–2.39) compared to VARI. Therefore, a novel composite index obtained from the multiplication operation of individual indices improved Pn retrieval of seedling rapeseed from remotely sensed UAV data. The results from this study indicate that the novel composite index has the potential for improving the accuracy of growth status monitoring compared with traditional indices. [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]

Solar-induced chlorophyll fluorescence (SIF) shows great potential to assess plants physiological state and response to environmental changes. Recently the near-infrared reflectance of vegetation (NIRv) provides a promising way to quantify the confounding effect of canopy structure in SIF, while the difference between SIF and NIRv under varying environmental conditions has not been well explored. Here we developed a simple approach to extract the fluorescence yield (Φ F) by the combined use of SIF and the near-infrared radiance of vegetation (NIRvR). The proposed NIRvR approach was evaluated in multiple ways, including with the seasonal leaf-level steady-state fluorescence yield. Results indicate that NIRvR-derived Φ F well captured the seasonal variation of the fluorescence yield changes, and achieved similar results with the existing approach. Both SIF and NIRvR were derived from the airborne imaging fluorescence spectrometer HyPlant for three case studies to evaluate the impacts of light adaptation, heat stress and water limitation on Φ F. For the light adaptation case study, Φ F over the low-light adapted sugar beet field was about 1.3 times larger compared to an unaffected reference area while the difference in NIRvR was minimal, which clearly shows the short-term photosynthetic light induction effect and the ability of SIF to detect plant physiological responses. For the heat stress experiment, Φ F decreased during a natural heatwave in 2015 in the fields of rapeseed from 0.0150 to 0.0130, barley from 0.0152 to 0.0144, and wheat from 0.0146 to 0.0142 which showed signs of senescence, while slightly increased from 0.0125 to 0.0130 in the corn field which was still in growing. At the water-limited sugar beet field, Φ F first increased towards solar noon and then slightly decreased during the afternoon over the water-limited areas from 0.017 to 0.021 and 0.020, with high temperature and high light at noon. The advantages to use SIF/NIRvR as a proxy of Φ F to detect stress-induced limitations in photosynthesis include that the impacts of canopy structure and sun-sensor geometry on the Φ F estimation are explicitly cancelled, and photosynthetically active radiation (PAR) is not required as input. Finally, our approach is directly applicable to satellite-derived estimates of SIF, enabling the study of variations in Φ F to detect the effects of abiotic changes and stresses at large scale. • The slope of SIF and NIRvR indicates the canopy-scale fluorescence yield Φ F. • Φ F can detect several effects of abiotic changes, e.g., the heat stress. • This approach with HyPlant dataset can minimize the canopy structure effects. • This method does not require PAR and FPAR products as inputs to calculate Φ F. [ABSTRACT FROM AUTHOR]

Disentangling the individual contributions from vegetation and soil in measured canopy reflectance is a grand challenge to the remote sensing and ecophysiology communities. Since Solar Induced chlorophyll Fluorescence (SIF) is uniquely emitted from vegetation, it can be used to evaluate how well reflectance-based vegetation indices (VIs) can separate the vegetation and soil components. Due to the residual soil background contributions, Near-infrared (NIR) reflectance of vegetation (NIRv) and Difference Vegetation index (DVI) present offsets when compared to SIF (i.e., the value of NIRv or DVI is non-zero when SIF is zero). In this study, we proposed a simple framework for estimating the true NIR reflectance of vegetation from Hyperspectral measurements (NIRvH) with minimal soil impacts. NIRvH takes advantage of the spectral shape variations in the red-edge region to minimize the soil effects. We evaluated the capability of NIRvH, NIRv and DVI in isolating the true NIR reflectance of vegetation using the data from both the model-based simulations and Hyperspectral Plant imaging spectrometer (HyPlant) measurements. Benchmarked by simultaneously measured SIF, NIRvH has the smallest offset (0–0.037), as compared to an intermediate offset of 0.047–0.062 from NIRv, and the largest offset of 0.089–0.112 from DVI. The magnitude of the offset can vary with different soil reflectance spectra across spatio-temporal scales, which may lead to bias in the downstream NIRv-based photosynthesis estimates. NIRvH and SIF measurements from the same sensor platform avoided complications due to different geometry, footprint and time of observation across sensors when studying the radiative transfer of reflected photons and SIF. In addition, NIRvH was primarily determined by canopy structure rather than chlorophyll content and soil brightness. Our work showcases that NIRvH is promising for retrieving canopy structure parameters such as leaf area index and leaf inclination angle, and for estimating fluorescence yield with current and forthcoming hyperspectral satellite measurements. • Previous VIs have not accounted for the shape of the soil spectrum at the red edge. • We propose the NIRvH approach for estimating the true NIR reflectance of vegetation. • NIRvH reduces the soil contamination using the SVD method and a logistic function. • Compared to DVI and NIRv, the proposed NIRvH has the best agreement with SIF. • NIRvH is promising for the anisotropic correction of directional observed SIF. [ABSTRACT FROM AUTHOR]

[Display omitted] • The Cab and SIF were retrieved based on the SCOPE model for paddy rice. • We assessed the effect of senescent material on retrieved Cab during the whole growing season. • The accuracy of Cab retrieval was improved when the variation in senescent material (Cs) was considered. • The Interpretation of SIF was need to evaluate in different growth stages. Solar-induced chlorophyll fluorescence at 760 nm (SIF) is a promising proxy of photosynthesis and can help improving plant stress monitoring. The Soil-Canopy Observation of Photochemistry and Energy fluxes (SCOPE) model combines radiative transfer and enzyme kinetics of photosynthesis and is widely used to interpret SIF at different temporal and spatial scales. In this study, growing season canopy hyperspectral reflectance between 400 nm and 900 nm was used to retrieve chlorophyll content (Cab) and leaf inclination (LIDFa) using radiative transfer models (RTMs) combined with the shuffled complex evolution-University of Arizona (SCE-UA) method. These parameters were then used to simulate diurnal and seasonal trends of SIF for paddy rice. The results showed that the accuracy of Cab retrieval was improved when the variation in senescent material (Cs) was considered, especially in the later growth stages. The SCOPE model was able to reliably interpret the diurnal cycle and seasonal trend of SIF with a correlation coefficient of 0.92 and RMSE of 0.12 w m−2 sr-1 um−1. Our results revealed that the SCOPE model provides a promising method for interpreting SIF variations but its accuracy should be evaluated in different growth stages. This will serve as a significant reference for detecting plant photosynthetic activity and physiological traits at different growth stages. [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]

• Sun-induced chlorophyll fluorescence (SIF) was a quantifying indicator of gross primary production (GPP). • SIF successfully characterized the temporal dynamics and spatial extent of drought-induced GPP anomalies. • SIF performed better than enhanced vegetation index (EVI) in capturing photosynthesis changes induced by droughts. • SIF had a time lag response to both Palmer drought severity index (PDSI) and soil moisture. In the parallel with the climate change, droughts have become one of the major climate extremes that induce losses in the terrestrial gross primary production (GPP). However, studying of the drought effects on GPP is still challenging, partly due to the lack of direct observations of GPP at large scales. Aiming to explore the potential of spaceborne sun-induced chlorophyll fluorescence (SIF) in monitoring vegetation droughts and quantifying the drought-induced GPP variabilities, we evaluated GOME-2 SIF with two state-of-art GPP products (i.e. FLUXCOM GPP and GLASS GPP) and flux tower observed GPP, using 2009 summer drought in Northeast China and 2010 spring drought in Southwest China as study cases. We found that SIF was a quantifying indicator of GPP and successfully characterized the temporal dynamics and spatial extents of GPP anomalies during the drought events, as well as accurately estimating the drought-induced GPP losses. Moreover, SIF (R2 = 0.87) performed better than enhanced vegetation index (R2 = 0.78) in capturing the photosynthesis changes induced by droughts, suggesting SIF was more sensitive to plant physiological changes. The strong positive correlations of SIF with Palmer drought severity index (PDSI) and root-zone soil moisture, further gave confidences on the capacity of SIF in vegetation drought monitoring. SIF had a time lag response to both PDSI and soil moisture, which might be attributable to the plants adaptation mechanisms for the drought occurrence and physiological recovery after water stress. Our study demonstrates that satellite-based SIF can achieve real-time vegetation drought monitoring and the assessment of drought-induced GPP anomalies at large scales, thus providing a unique opportunity to study the impacts of drought on vegetation carbon uptake. [ABSTRACT FROM AUTHOR]

Terrestrial evapotranspiration (ET) is an important flux that links global cycles of carbon, water and energy and is largely driven by transpiration (T) through leaf stomata in vegetated areas during the growing season. ET , however, remains one of the most uncertain hydrological variables at the global scale. In this study, we proposed a semi-mechanistic model for estimating terrestrial T by deriving an analytical solution between solar-induced chlorophyll fluorescence (SIF) and stomatal conductance (g c) as well as vapor pressure deficit (VPD), combining theories on the photosynthetic pathway and optimal stomatal behavior. The relationships of SIF-ETR and ETR-gc·VPD0.5 was calibrated by the Soil-Canopy Observation of Photosynthesis and Energy (SCOPE) model. This model was validated by hourly canopy SIF and concurrent eddy covariance flux observations at both forest and cropland ecosystems. Results showed that the SIF combined with VPD can better predict g c than using SIF alone with a more consistent seasonal trends found in both SIF and g c ·VPD0.5. The correlation between g c ·VPD0.5 and SIF was stronger than those between g c and SIF and between g c and VIs. Canopy T was accurately predicted from SIF at both hourly (R 2 > 0.65) and daily (R 2 > 0.76) scales and was also successfully estimated using SIF observations from the TROPOspheric Monitoring Instrument (TROPOMI) at cropland ecosystems. In comparison with empirical relationships of directly linking g c with SIF or VIs, the proposed model produced latent heat flux (λ E) estimation in best agreement with measured values at all three sites. Our model could be a step forward in understanding the coupling of carbon and water cycles and may be used in ecosystem models for improving ET estimation over large areas. • A semi-mechanistic model is derived that correlate g c with SIF. • The correlation of SIF with g c is improved by multiplying the term of VPD0.5. • Ecosystem transpiration is accurately estimated from SIF-based model. [ABSTRACT FROM AUTHOR]

• An approach for coupling BEPS and SCOPE was presented. • The BEPS-SCOPE coupling model provided an efficient solution for regional/global SIF and GPP simulations. • Performance of the coupling model was assessed by compared with the GOME-2 SIF and the SCOPE model. • The factors that mostly affect the fluorescence upscaling process from leaf to canopy were assessed. Remotely sensed solar-induced chlorophyll fluorescence (SIF) has been increasingly used to probe photosynthesis and model the gross primary productivity (GPP). Although SIF at the top of canopy (TOC) can be simulated using the coupled photosynthesis-fluorescence model SCOPE (Soil Canopy Observation, Photochemistry and Energy fluxes), simulating spatially distributed TOC SIF usually requires extensive calculations, entailing some challenges when applying the model to the regional and the global scales. This study puts forward a coupling framework that combines SIF and global terrestrial biosphere models (TBMs). The theory for fluorescence emissions and the fluorescence radiative transfer algorithm described in the SCOPE model were integrated with the "two-leaf"-based BEPS (Boreal Ecosystem Productivity Simulator) model. To simplify the fluorescence radiative transfer physics, we put forward a canopy-averaged leaf-level fluorescence to represent the fluorescence emitted from sunlit and shaded leaf groups and performed a sensitivity analysis to assess the determining factors in upscaling fluorescence from leaf scale to canopy scale. We found that the relationship between the leaf and canopy fluorescence at 740 nm was mainly affected by LAI. Although brown pigments and leaf inclination angle demonstrate some impacts on the scaling process, an LAI-based coefficient can well characterize the upscaling from leaf to canopy scale. Since our BEPS-SCOPE coupling model deploys the sunlit-shaded leaf separation strategy, we expect that it can efficiently characterize the nonlinear responses of photosynthesis and the associated fluorescence to environmental factors. The performance of our model was evaluated at both site and global scales, which demonstrated a good performance for most plant functional types (PFTs) except for needleleaf types that have a more clumped nature. Apart from these limitations, the presented model can contribute to efficiently simulating SIF at regional and global scales, and has the potential to reduce uncertainties in GPP estimation. [ABSTRACT FROM AUTHOR]

• A novel unmanned aerial system with bi-hemispherical spectral acquisition. • Simultaneous solar-induced chlorophyll fluorescence and hyperspectral reflectance. • Emulates and correlates well with a tower system, enabling synergistic application. • Characterizes seasonal and diurnal dynamics of crop photosynthetic activity. Unmanned aerial system (UAS)-based remote sensing can provide high spatial- and temporal-resolution crop monitoring for precision management. Existing crop monitoring UAS primarily use conventional techniques such as multi-spectral broadband vegetation indices (VIs) to track canopy structure/biomass. Recently developed lightweight hyperspectral sensors can track crop physiology and performance via complementary signals such as solar-induced chlorophyll fluorescence (SIF) and photochemical reflectance index (PRI) that can be derived from hyperspectral reflectance. However, few existing UAS can acquire high-quality SIF and hyperspectral reflectance. We designed a novel UAS that simultaneously captures far-red SIF and hyperspectral reflectance, using a single bifurcated fiber and motorized arm to measure downwelling and upwelling irradiance. The UAS was tested over (1) a heterogeneous corn field with spatially varying crop yield potential and (2) a set of soybean and corn plots under differing nutrient treatments. Our UAS can maintain stability under low-to-moderate winds (2–3 m/s) with <0.4 m geolocation accuracy, <0.1 m altitude drift, and <1.5° error of the fiber optic from nadir position during scanning. Weekly seasonal campaigns reveal that SIF outperforms conventional VIs such as the normalized difference vegetation index (NDVI) for distinguishing plots with different crop yield potential, especially after canopy closure when NDVI tends to saturate. The UAS can capture the diurnal dynamics of SIF and PRI for both corn and soybean. At the seasonal scale, SIF acquired from the UAS correlates well with that from a fixed SIF tower system (R 2 = 0.81), which measured the same target albeit with different footprints, demonstrating the capability of UAS in characterizing the seasonal progression of crop activity. In conclusion, our newly developed UAS provides high-quality SIF and hyperspectral reflectance, facilitating mechanistic understanding of the physiological control on photosynthesis dynamics. Our findings also imply that this UAS could enable extension of fixed tower-based systems to monitor multiple targets from diurnal to seasonal scales and improve monitoring of heterogeneous fields. [ABSTRACT FROM AUTHOR]

• A simple method to estimate SIF emission at the photosystem level is proposed. • Only canopy BRF and NDVI are needed for SIF downscaling at the red and NIR bands. • Red-band SIF at the photosystem level has great potential for monitoring GPP. Numerous studies have proved that solar-induced chlorophyll fluorescence (SIF) is a good proxy of gross primary production (GPP). The SIF spectrum covers the spectral window from about 640 nm to 850 nm. Due to the strong chlorophyll absorption effect at the red band, the red SIF is much more influenced by the radiative transfer effect inside the canopy than the near infrared (NIR) SIF is. Therefore, at the canopy level, the NIR SIF shows more potential for use in estimating GPP. However, the red-band SIF contains more information about PS II, which is more sensitive to photosynthesis. So, in theory, if the canopy radiative transfer effect could be corrected for, the potential of the red SIF for estimating GPP would be greatly improved. In this paper, we propose a new simple reflectance-based method for estimating the photosystem-level SIF at both the NIR band and the red band. Long-term observations of SIF and GPP at two crop sites in two years (four datasets in total) were used to investigate the SIF−GPP relationship at the canopy or photosystem level. The SIF downscaled by the simple reflectance-based approach is found to be consistent with that found using the random forest regression method proposed formerly. For the NIR band, the performance of the canopy-level SIF and PS-level SIF for GPP estimation is similar while, for the red band, the correlation between the SIF and GPP is greatly improved after downscaling the canopy-level SIF to the PS level. For the four test datasets, at the red band, the values of R 2 for the SIF−GPP relationship increase from 0.213−0.599 to 0.398−0.759 after the downscaling. At the PS level, the performance of the red SIF for GPP estimation is as good as or even better than that of the NIR-band SIF. The results of this study indicate that estimation of the SIF photosystem level to canopy level escape probability is important, and that more importance should be paid to the red-band SIF in the monitoring of GPP. [ABSTRACT FROM AUTHOR]