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Solar-induced chlorophyll fluorescence (SIF) has been found to be a useful indicator of vegetation's gross primary productivity (GPP). However, the directional SIF observations obtained from a canopy only represent a portion of the total fluorescence emitted by all the leaf photosystems because of scattering and reabsorption effects inside the leaves and canopy. Hence, it is crucial to downscale the SIF from canopy level to leaf level by modeling fluorescence escape probability (fesc) for improved comprehension of the relationship between SIF and GPP. Most methods for estimating fesc rely on the assumption of a "black soil background," ignoring soil reflectance and the effect of scattering between soils and leaves, which creates significant uncertainties for sparse canopies. In this study, we added a correction factor considering soil reflectance, which was modeled using the Gaussian process regression algorithm, to the semi-empirical NIRv/FAPAR model and obtained the improved fesc model accounting for soil reflectance (called the fesc_GPR-SR model), which is suitable for near-infrared SIF downscaling. The evaluation results using two simulation datasets from the Soil–Canopy–Observation of Photosynthesis and the Energy Balance (SCOPE) model and the Discrete Anisotropic Radiative Transfer (DART) model showed that the fesc_GPR-SR model outperformed the NIRv/FAPAR model, especially for sparse vegetation, with higher accuracy for estimating fesc (R2 = 0.954 and RMSE = 0.012 for SCOPE simulations; R2 = 0.982 and RMSE = 0.026 for DART simulations) compared with the NIRv/FAPAR model (R2 = 0.866 and RMSE = 0.100 for SCOPE simulations; R2 = 0.984 and RMSE = 0.070 for DART simulations). The evaluation results using in situ observation data from multi-species canopies also suggested that the leaf-level SIF calculated by the fesc_GPR-SR model tracked better with photosynthetic active radiation absorbed by green components (APARgreen) for sparse vegetation (R2 = 0.937, RMSE = 0.656 mW/m2/nm) compared with the NIRv/FAPAR model (R2 = 0.921, RMSE = 0.904 mW/m2/nm). The leaf-level SIF calculated by the fesc_GPR-SR model was less sensitive to observation angles and differences in canopy structure among multiple species. These results emphasize the significance of accounting for soil reflectance in the estimation of fesc and demonstrate that the fesc_GPR-SR model can contribute to further exploring the physiological mechanism between SIF and GPP. [ABSTRACT FROM AUTHOR]

Spaceborne solar-induced chlorophyll fluorescence (SIF) products have emerged over the past decade. However, limited by the spatial-temporal mismatch between satellite-based and ground measurements or the absence of sufficient in situ SIF observations, no direct validation results of satellite-derived SIF products have been provided to date. The Orbiting Carbon Observatoty-2/3 (OCO-2/3) platforms allow us to validate SIF products using in situ SIF observations benefiting from comparable spatial resolution (1.3 km × 2.25 km and 1.6 km × 2.2 km, respectively) to ground measurements. In addition, the automated in situ SIF measurement technique facilitates accuracy validation of spaceborne SIF dataset. In this communication, we describe a direct validation of OCO-2/3 SIF products using tower-based SIF measurements acquired at six sites across China. There were 1–7 and 1–18 successful observations overpassing six in situ sites for OCO-2 and OCO-3, respectively. An overall consistent temporal variation pattern was observed between the OCO-2/3 SIF products and tower-based SIF measurements. The validation accuracies of OCO-2 and OCO-3 SIF products were 0.26 and 0.33 mW m−2 sr−1 nm−1, respectively. To best use satellite-based SIF products in a wide range of applications, we suggest that further accuracy validation using more in situ SIF observations is required, which in turn requires increased availability of public in situ SIF observations. [ABSTRACT FROM AUTHOR]

Alpine meadow ecosystems are extremely vulnerable to climate change and serve an essential function in terrestrial carbon sinks. Accurately estimating their gross primary productivity (GPP) is essential for understanding the global carbon cycle. Solar-induced chlorophyll fluorescence (SIF), as a companion product directly related to plant photosynthesis process, has become an attractive pathway for estimating GPP accurately. To date, the quantitative SIF-GPP relationship in terrestrial ecosystems is not yet clear. Especially, red SIF and far-red SIF present differences in their ability to track GPP under different environmental conditions. In this study, we investigated the performance of SIF at both red and far-red band in monitoring the GPP of an alpine meadow ecosystem based on continuous tower-based observations in 2019 and 2020. The results show that the canopy red SIF ( SIF Red ) and far-red SIF ( SIF Far-red ) were both strongly correlated with GPP. SIF Red was comparable to SIF Far-red for monitoring GPP based on comparisons of both half-hourly averaged and daily averaged datasets. Moreover, the relationship between SIF Red and GPP was linearly correlated, while the relationship between SIF Far-red and GPP tended to be nonlinear. At a diurnal scale, dramatic changes in photosynthetically active radiation (PAR), air temperature (Ta), and vapor pressure deficit (VPD) all had effects on the slope of the linear fitted line with zero intercept for SIF Red -GPP and SIF Far-red -GPP, and the effect on the slope of the linear fitted line with zero intercept for SIF Far-red -GPP was obviously stronger than that for SIF Red -GPP. PAR was the dominant factor among the three environmental factors in determining the diurnal variation of the slope of SIF-GPP. At a seasonal scale, the SIF Far-red /GPP was susceptible to PAR, Ta, and VPD, while the SIF Red /GPP remained relatively stable at different levels of Ta and VPD, and it was only weakly affected by PAR, suggesting that SIF Red was more consistent than SIF Far-red with GPP in response to seasonal variations in environmental factors. These results indicate that SIF Red has more potential than SIF Far-red for monitoring the GPP of alpine meadow ecosystems and can also assist researchers in gaining a more comprehensive understanding of the diversity of SIF-GPP relationships in different ecosystems. [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]

Space-based solar-induced chlorophyll fluorescence (SIF) has been widely demonstrated as a great proxy for monitoring terrestrial photosynthesis and has been successfully retrieved from satellite-based hyperspectral observations using a data-driven algorithm. As a semi-empirical algorithm, the data-driven algorithm is strongly affected by the empirical parameters in the model. Here, the influence of the data-driven algorithm's empirical parameters, including the polynomial order (np), the number of feature vectors (nSV), the fluorescence emission spectrum function, and the fitting window used in the retrieval model, were quantitatively investigated based on the simulations of the SIF Imaging Spectrometer (SIFIS) onboard the First Terrestrial Ecosystem Carbon Inventory Satellite (TECIS-1). The results showed that the fitting window, np, and nSV were the three main factors that influenced the accuracy of retrieval. The retrieval accuracy was relatively higher for a wider fitting window; the root mean square error (RMSE) was lower than 0.7 mW m−2 sr−1 nm−1 with fitting windows wider than 735–758 nm and 682–691 nm for the far-red band and the red band, respectively. The RMSE decreased first and then increased with increases in np range from 1 to 5 and increased in nSV range from 2 to 20. According to the specifications of SIFIS onboard TECIS-1, a fitting window of 735–758 nm, a second-order polynomial, and four feature vectors are the optimal parameters for far-red SIF retrieval, resulting in an RMSE of 0.63 mW m−2 sr−1 nm−1. As for red SIF retrieval, using second-order polynomial and seven feature vectors in the fitting window of 682–697 nm was the optimal choice and resulted in an RMSE of 0.53 mW m−2 sr−1 nm−1. The optimized parameters of the data-driven algorithm can guide the retrieval of satellite-based SIF and are valuable for generating an accurate SIF product of the TECIS-1 satellite after its launch. [ABSTRACT FROM AUTHOR]

Solar-induced chlorophyll fluorescence (SIF) provides a new and direct way of monitoring photosynthetic activity. However, current SIF products are limited by low spatial resolution or sparse sampling. In this paper, we present a data-driven method of generating a global, spatially continuous TanSat SIF product. Firstly, the key explanatory variables for modelling canopy SIF were investigated using in-situ and satellite observations. According to theoretical and experimental analysis, the solar radiation intensity was found to be a dominant driving environmental variable for the SIF yield at both the canopy and global scales; this has, however, been neglected in previous research. The cosine value of the solar zenith angle at noon (cos (SZA0)), a proxy for solar radiation intensity, was found to be a dominant abiotic factor for the SIF yield. Next, a Random Forest (RF) approach was employed for SIF prediction based on Moderate Resolution Imaging Spectroradiometer (MODIS) visible-to-NIR reflectance data, the normalized difference vegetation (NDVI), cos (SZA0), and air temperature. The machine learning model performed well at predicting SIF, giving R2 values of 0.73, an RMSE of 0.30 mW m−2 nm−1 sr−1 and a bias of 0.22 mW m−2 nm−1 sr−1 for 2018. If cos (SZA0) was not included, the accuracy of the RF model decreased: the R2 value was then 0.65, the RMSE 0.34 mW m−2 nm−1 sr−1 and an bias of 0.26 mW m−2 nm−1 sr−1, further verifying the importance of cos (SZA0). Finally, the globally continuous TanSat SIF product was developed and compared to the TROPOspheric Monitoring Instrument (TROPOMI) SIF data. The results showed that the globally continuous TanSat SIF product agreed well with the TROPOMI SIF data, with an R2 value of 0.73. Thus, this paper presents an improved approach to modelling satellite SIF that has a better accuracy, and the study also generated a global, spatially continuous TanSat SIF product with a spatial resolution of 0.05°. [ABSTRACT FROM AUTHOR]

Solar-induced chlorophyll fluorescence (SIF) has been proven to be well correlated with vegetation photosynthesis. Although multiple studies have found that SIF demonstrates a strong correlation with gross primary production (GPP), SIF-based GPP estimation at different temporal scales has not been well explored. In this study, we aimed to investigate the quality of GPP estimates produced using the far-red SIF retrieved at 760 nm (SIF760) based on continuous tower-based observations of a maize field made during 2017 and 2018, and to explore the responses of GPP and SIF to different meteorological conditions, such as the amount of photosynthetically active radiation (PAR), the clearness index (CI, representing the weather condition), the air temperature (AT), and the vapor pressure deficit (VPD). Firstly, our results showed that the SIF760 tracked GPP well at both diurnal and seasonal scales, and that SIF760 was more linearly correlated to PAR than GPP was. Therefore, the SIF760–GPP relationship was clearly a hyperbolic relationship. For instantaneous observations made within a period of half an hour, the R2 value was 0.66 in 2017 and 2018. Based on daily mean observations, the R2 value was 0.82 and 0.76 in 2017 and 2018, respectively. Secondly, it was found that the SIF760–GPP relationship varied with the environmental conditions, with the CI being the dominant factor. At both diurnal and seasonal scales, the ratio of GPP to SIF760 decreased noticeably as the CI increased. Finally, the SIF760-based GPP models with and without the inclusion of CI were trained using 70% of daily observations from 2017 and 2018 and the models were validated using the remaining 30% of the dataset. For both linear and non-linear models, the inclusion of the CI greatly improved the SIF760-based GPP estimates based on daily mean observations: the value of R2 increased from 0.71 to 0.82 for the linear model and from 0.82 to 0.87 for the non-linear model. The validation results confirmed that the SIF760-based GPP estimation was improved greatly by including the CI, giving a higher R2 and a lower RMSE. These values improved from R2 = 0.66 and RMSE = 7.02 mw/m2/nm/sr to R2 = 0.76 and RMSE = 6.36 mw/m2/nm/sr for the linear model, and from R2 = 0.71 and RMSE = 4.76 mw/m2/nm/sr to R2 = 0.78 and RMSE = 3.50 mw/m2/nm/sr for the non-linear model. Therefore, our results demonstrated that SIF760 is a reliable proxy for GPP and that SIF760-based GPP estimation can be greatly improved by integrating the CI with SIF760. These findings will be useful in the remote sensing of vegetation GPP using satellite, airborne, and tower-based SIF data because the CI is usually an easily accessible meteorological variable. [ABSTRACT FROM AUTHOR]

The global monitoring of solar-induced chlorophyll fluorescence (SIF) using satellite-based observations provides a new way of monitoring the status of terrestrial vegetation photosynthesis on a global scale. Several global SIF products that make use of atmospheric satellite data have been successfully developed in recent decades. The Terrestrial Ecosystem Carbon Inventory Satellite (TECIS-1), the first Chinese terrestrial ecosystem carbon inventory satellite, which is due to be launched in 2021, will carry an imaging spectrometer specifically designed for SIF monitoring. Here, we use an extensive set of simulated data derived from the MODerate resolution atmospheric TRANsmission 5 (MODTRAN 5) and Soil Canopy Observation Photosynthesis and Energy (SCOPE) models to evaluate and optimize the specifications of the SIF Imaging Spectrometer (SIFIS) onboard TECIS for accurate SIF retrievals. The wide spectral range of 670−780 nm was recommended to obtain the SIF at both the red and far-red bands. The results illustrate that the combination of a spectral resolution (SR) of 0.1 nm and a signal-to-noise ratio (SNR) of 127 performs better than an SR of 0.3 nm and SNR of 322 or an SR of 0.5 nm and SNR of 472 nm. The resulting SIF retrievals have a root-mean-squared (RMS) diff* value of 0.15 mW m−2 sr−1 nm−1 at the far-red band and 0.43 mW m−2 sr−1 nm−1 at the red band. This compares with 0.20 and 0.26 mW m−2 sr−1 nm−1 at the far-red band and 0.62 and 1.30 mW m−2 sr−1 nm−1 at the red band for the other two configurations described above. Given an SR of 0.3 nm, the increase in the SNR can also improve the SIF retrieval at both bands. If the SNR is improved to 450, the RMS diff* will be 0.17 mW m−2 sr−1 nm−1 at the far-red band and 0.47 mW m−2 sr−1 nm−1 at the red band. Therefore, the SIFIS onboard TECIS-1 will provide another set of observations dedicated to monitoring SIF at the global scale, which will benefit investigations of terrestrial vegetation photosynthesis from space. [ABSTRACT FROM AUTHOR]

Solar-induced chlorophyll fluorescence (SIF) is regarded as a proxy for photosynthesis in terrestrial vegetation. Tower-based long-term observations of SIF are very important for gaining further insight into the ecosystem-specific seasonal dynamics of photosynthetic activity, including gross primary production (GPP). Here, we present the design and operation of the tower-based automated SIF measurement (SIFSpec) system. This system was developed with the aim of obtaining synchronous SIF observations and flux measurements across different terrestrial ecosystems, as well as to validate the increasing number of satellite SIF products using in situ measurements. Details of the system components, instrument installation, calibration, data collection, and processing are introduced. Atmospheric correction is also included in the data processing chain, which is important, but usually ignored for tower-based SIF measurements. Continuous measurements made across two growing cycles over maize at a Daman (DM) flux site (in Gansu province, China) demonstrate the reliable performance of SIF as an indicator for tracking the diurnal variations in photosynthetically active radiation (PAR) and seasonal variations in GPP. For the O2–A band in particular, a high correlation coefficient value of 0.81 is found between the SIF and seasonal variations of GPP. It is thus concluded that, in coordination with continuous eddy covariance (EC) flux measurements, automated and continuous SIF observations can provide a reliable approach for understanding the photosynthetic activity of the terrestrial ecosystem, and are also able to bridge the link between ground-based optical measurements and airborne or satellite remote sensing data. [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]

• 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]

• 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]