Solar-induced chlorophyll fluorescence (SIF) has been widely utilized to track the dynamics of gross primary productivity (GPP). It has been shown that the photochemical reflectance index (PRI), which may be utilized as an indicator of non-photochemical quenching (NPQ), improves SIF-based GPP estimation. However, the influence of weather conditions on GPP estimation using SIF and PRI has not been well explored. In this study, using an open-access dataset, we examined the impact of the clearness index (CI), which is associated with the proportional intensity of solar incident radiation and can represent weather conditions, on soybean GPP estimation using SIF and PRI. The midday PRI (xanthophyll de-epoxidation state) minus the early morning PRI (xanthophyll epoxidation state) yielded the corrected PRI (ΔPRI), which described the amplitude of xanthophyll pigment interconversion during the day. The observed canopy SIF at 760 nm (SIFTOC_760) was downscaled to the broadband photosystem-level SIF for photosystem II (SIFTOT_FULL_PSII). Our results show that GPP can be accurately estimated using a multi-linear model with SIFTOT_FULL_PSII and ΔPRI. The ratio of GPP measured using the eddy covariance (EC) method (GPPEC) to GPP estimated using SIFTOT_FULL_PSII and ΔPRI exhibited a non-linear correlation with the CI along both the half-hourly (R2 = 0.21) and daily scales (R2 = 0.25). The GPP estimates using SIFTOT_FULL_PSII and ΔPRI were significantly improved by the addition of the CI (for the half-hourly data, R2 improved from 0.64 to 0.71 and the RMSE decreased from 8.28 to 7.42 μmol•m−2•s−1; for the daily data, R2 improved from 0.71 to 0.81 and the RMSE decreased from 6.69 to 5.34 μmol•m−2•s−1). This was confirmed by the validation results. In addition, the GPP estimated using the Random Forest method was also largely improved by considering the influences of the CI. Therefore, our findings demonstrate that GPP can be well estimated using SIFTOT_FULL_PSII and ΔPRI, and it can be significantly enhanced by accounting for the CI. These results will be beneficial to vegetation GPP estimation using different remote sensing platforms, especially under various weather conditions.

Photosynthesis is influenced by dynamic energy allocation under various environmental conditions. Solar-induced chlorophyll fluorescence (SIF), an important pathway for dissipating absorbed energy, has been extensively used to evaluate gross primary productivity (GPP). However, the potential for photochemical reflectance index (PRI), as an indicator of non-photochemical quenching (NPQ), to improve the SIF-based GPP estimation, has not been thoroughly investigated. In this study, using continually tower-based observations, we examined how PRI affected the link between SIF and GPP for corn and soybean at half-hourly and daily timescales. The relationship of GPP to SIF and PRI is impacted by stress indicated by vapor pressure deficit (VPD) and crop water stress index (CWSI). Moreover, the ratio of GPP to SIF of corn was more sensitive to PRI compared to soybean. Whether in Pearson or Partial correlation analysis, the relationships of PRI to the ratio of GPP to SIF were almost all significant, regardless of controlling structural-physiological (stomatal conductance, vegetation indices) and environmental variables (light intensity, etc.). Therefore, PRI significantly affects the SIF–GPP relationship for corn (r > 0.31, p < 0.01) and soybean (r > 0.22, p < 0.05). After combining SIF and PRI using the multi-variable linear model, the GPP estimation has been largely improved (the coefficient of determination, abbreviated as R2, increased from 0.48 to 0.49 to 0.78 to 0.84 and the Root Mean Square Error, abbreviated as RMSE, decreased from 6.38 to 10.22 to 3.56 to 6.60 μmol CO2·m−2·s−1 for corn, R2 increased from 0.54 to 0.62 to 0.78 to 0.82 and RMSE decreased from 6.25 to 9.59 to 4.34 to 6.60 μmol CO2·m−2·s−1 for soybean). It suggests that better GPP estimations for corn and soybean can be obtained when SIF is combined with PRI.

日 光诱导叶绿素荧光(Solar-Induced Chlorophyll Fluorescence,SIF)与植被光合作用关系密切,能够揭示植被真实生理状况,准确获取SIF信息对陆地生态碳循环和全球植被监测具有重要意义.以3FLD算法的SIF反演结果和NIRvR为参考,开展了基于塔基平台的数据驱动SIF反演算法性能评价研究.首先利用塔基连续冠层光谱观测数据,分析了 SVD算法在不同大气窗口的SIF反演效果;其次利用大气校正前后的实测数据,探究大气因素对SVD算法反演SIF的影响程度;最后,将实测数据按照光照条件区分,对比了天气稳定与天气波动条件下,基于SVD算法的SIF反演结果的稳定性.结果表明:(1)SVD算法在735～759 nm(不包含大气吸收波段)、745～780 nm(包含大气吸收波段)窗口的SIF反演精度较高;(2)SVD算法的SIF反演精度受大气影响远小于3FLD算法;(3)当光照条件剧烈变化时,使用SVD算法可以有效克服FLD类SIF反演算法对同步太阳光谱观测的依赖;即使光照迅速变化,基于SVD算法依旧能够得到稳定可靠的SIF反演结果.综上所述,SVD算法对于塔基SIF反演来说具有非常大的应用潜力.

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 degrees 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.

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 TROPOSIFCaltech (containing far-red and red TROPOSIFCaltech datasets) and TROPOSIFESA (containing TROPOSIF735 and TROPOSIF743 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 TROPOSIFCaltech, TROPOSIF735, and TROPOSIF743 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 TROPOSIFCaltech 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.

Solar-induced fluorescence (SIF) is a promising proxy for photosynthesis, but it is unclear whether it performs well in tracking the gross primary productivity (GPP) under different environmental conditions. In this study, we investigated the dynamics of the two parameters from October 2020 to June 2021 in field-grown winter wheat (Triticum aestivum) and found that the ability of SIF to track GPP was weakened at low temperatures. Accounting for the coupling of light and temperature at a seasonal scale, we found that SIF yield showed a lower temperature sensitivity and had a lower but broader optimal temperature range compared with light-use efficiency (LUE), although both SIF yield and LUE decreased in low-temperature conditions. The discrepancy between the temperature responses of SIF yield and GPP caused an increase in the ratio of SIF/GPP in winter, which indicated the variation in the relationship between them during this period. The results of our study highlight the impact of low temperature on the relationship between SIF and GPP and show the necessity of reconsidering the dynamics of energy distribution inside plants under changing environments.

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 (SIFRed) and far-red SIF (SIFFar-red) were both strongly correlated with GPP. SIFRed was comparable to SIFFar-red for monitoring GPP based on comparisons of both half-hourly averaged and daily averaged datasets. Moreover, the relationship between SIFRed and GPP was linearly correlated, while the relationship between SIFFar-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 SIFRed-GPP and SIFFar-red-GPP, and the effect on the slope of the linear fitted line with zero intercept for SIFFar-red-GPP was obviously stronger than that for SIFRed-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 SIFFar-red/GPP was susceptible to PAR, Ta, and VPD, while the SIFRed/GPP remained relatively stable at different levels of Ta and VPD, and it was only weakly affected by PAR, suggesting that SIFRed was more consistent than SIFFar-red with GPP in response to seasonal variations in environmental factors. These results indicate that SIFRed has more potential than SIFFar-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.

Solar-induced chlorophyll fluorescence (SIF) is closely related to the light-reaction process and has been recognized as a good indicator for tracking gross primary productivity (GPP). Nevertheless, it has not been widely examined how SIF and GPP respond to temperature. Here, we explored the linkage mechanisms between SIF and GPP in winter wheat based on continuous measurements of canopy SIF (cSIF), GPP, and meteorological data. To separately explore the structural and physiological mechanisms underlying the SIF–GPP relationship, we studied the temperature responses of the estimated light use efficiency (LUEp), canopy-level chlorophyll fluorescence yield (cSIFyield) and photosystem-level chlorophyll fluorescence yield (ΦF) estimated using canopy-scale remote sensing measurements. We found that GPP, red canopy SIF (cSIF688) and far-red canopy SIF (cSIF760) all exhibited a decreasing trend during overwintering periods. However, GPP and cSIF688 showed relatively more obvious changes in response to air temperature (Ta) than cSIF760 did. In addition, the LUEp responded sensitively to Ta (the correlation coefficient, r = 0.83, p-value < 0.01). The cSIFyield_688 and ΦF_688 (ΦF at 688 nm) also exhibited significantly positive correlations with Ta (r > 0.7, p-value < 0.05), while cSIFyield_760 and ΦF_760 (ΦF at 760 nm) were weakly correlated with Ta (r < 0.3, p-value > 0.05) during overwintering periods. The results also show that LUEp was more sensitive to Ta than ΦF, which caused changes in the LUEp/ΦF ratio in response to Ta. By considering the influence of Ta, the GPP estimation based on the total SIF emitted at the photosystem level (tSIF) was improved (with R2 increased by more than 0.12 for tSIF760 and more than 0.05 for tSIF688). Therefore, our results indicate that the LUEp/ΦF ratio is affected by temperature conditions and highlights that the SIF–GPP model should consider the influence of temperature.

Solar-induced chlorophyll fluorescence (SIF) has provided novel methods for monitoring vegetation growth and the carbon cycle of terrestrial ecosystems. However, the effects of spatial heterogeneity on canopy SIF measurements remain unclear. The unmanned aerial vehicle (UAV) platform provides a unique opportunity to assess the impact of spatial heterogeneity on SIF variability due to its adjustable observational height at the intermediate canopy scale. In this work, we used a UAV-based SIF system to investigate the influences of fractional vegetation cover (FVC) on SIF measurements over (1) a homogeneous rice paddy field and (2) a heterogeneous planted forest characterized by unevenly distributed differing plant species. We first simultaneously conducted an experiment with UAV- and tower-based SIF systems in a homogeneous paddy field to test the reliability of SIF measurements from UAV-based system. The results showed that the SIF measured by UAV- and tower-based systems have a strong linear relationship (R2 = 0.98), demonstrating that UAV-based SIF system is capable of capturing the diurnal variations of canopy SIF. Then, we operated the UAV flying over the heterogeneous planted forest field in five flights, with each flight observing the same plant species across different heights to investigate the influences of FVC on the spatial variability of SIF. We found that FVC exerted substantial effects on the spatial variability of SIF, with the coefficient of variation (CV) of SIF observations at different flying heights increasing from 5% (high FVC) to 30% (low FVC), which was consistent with DART (Discrete Anisotropic Radiative Transfer) model simulations. Furthermore, our results indicated that the escaping probability of SIF and total emitted SIF showed nonlinear responses to FVC at individual observational heights. In particular, the escaping probability reached its lowest value when FVC was at an intermediate level (FVC = 0.6). These findings highlight the significant effect of spatial heterogeneity on canopy SIF measurements, especially at low FVC. Therefore, an exhaustive consideration of the SIF measurement footprint on different underlying surfaces (homogeneous or heterogeneous) is essential to advance SIF applications in terrestrial vegetation science.

Using the proper means to improve seed germination is of great significance in agriculture and forestry. Here, a meta-analysis was used to examine whether metal nanoparticle treatments have a specific effect on the seed germination and seedling growth of agricultural species. Using the Web of Science (1950–2021), PubMed (1950–2021), and Scopus (1950–2021) databases, a paper search was conducted using the following items (“nanoparticles” and “seed germination”, “nanomaterials” and “seed germination”) to filter the references in the title, abstract, and keywords of the published articles. The results indicated that nanoparticle (NP) treatments had a significantly positive effect on the final germination percentage (FGP), with a mean difference (MD) (that is, the overall effect) of 1.97 (0.96, 2.98) for the silver (Ag)-NP subgroup, 1.21 (0.34, 2.09) for the other-NP subgroup, 1.40 (0.88, 1.92) for the total based on the NP types, 1.47 (0.85, 2.09) for the “Concentrations: <50 mg/L” subgroup, and 1.40 (0.88, 1.92) for the total based on the NP concentrations. Similarly, root length (RL) was positively and significantly affected by NP treatment, with an MD (95% CI) of 0.92 (0.76, 1.09) for the zinc (Zn)-NP subgroup, 0.79 (0.65, 0.92) for the other-NP subgroup, 0.82 (0.72, 0.93) for the total based on the NP types, 0.90 (0.77, 1.04) for the “Concentrations: ≤50 mg/L” subgroup, 0.80 (0.60, 0.99) for the “Concentrations: >50 mg/L” subgroup, and 0.82 (0.72, 0.93) for the total based on the NP concentrations. However, there was no statistical correlation between the nanoparticle concentrations and shoot length (SL), due to the inclusion of zero in the 95% CI of the overall effect. Therefore, Ag-NPs could increase the FGP more than other-NPs, while Zn-NPs enhanced RL more. Moreover, NPs at lower concentrations could improve the FGP and RL of crop species to a larger extent than NPs at higher concentrations. This meta-analysis can provide a reference for the nanoparticle treatment technology utilization in agricultural and forest seeds.

The importance of solar-induced chlorophyll fluorescence (SIF) to monitoring vegetation photosynthesis has attracted much attention from the ecological and remote sensing research communities. Space-borne SIF products have been obtained owing to the rapid development of atmospheric satellites in recent years. The SIF Imaging Spectrometer (SIFIS) is a payload onboard the upcoming Terrestrial Ecosystem Carbon Inventory Satellite (TECIS-1) that is specifically designed for SIF monitoring. We conducted an in situ experiment to evaluate the performance of SIFIS on spectral measurement and SIF retrieval through comparison to the commercial spectrometer QE Pro. Disregarding the spatiotemporal mismatch between the collected measurements of the two spectrometers, the radiance spectra obtained synchronously by SIFIS and QE Pro showed a high level of consistency. The SIF retrieval, normalized difference vegetation index (NDVI), and near-infrared radiance of vegetation (NIRvR) results for a push-broom image shows consistent spatial distributions over both vegetated and nonvegetated surfaces. A quantitative comparison was conducted by strictly filtering matching pixels. For the far-red band, a high correlation was obtained between the SIF retrieval performances of SIFIS and QE Pro with R 2 = 0.70 and RMSE = 0.30 mW m − 2 s r − − 1 n m − 1 . However, a relatively poor correlation was observed for the red band with an R 2 value of 0.23 and an RMSE of 0.26 mWm −2 sr -−1 nm −1 . Despite the large uncertainties associated with this experiment, the results indicate that TECIS-1 should offer a reliable SIF monitoring performance after its launch.

Solar-induced chlorophyll fluorescence (SIF) has been recognized as a proxy of gross primary production (GPP) across various terrestrial biomes. However, the effects of low temperature on SIF and GPP among different plant function types (PFTs) have not yet been well-explored. To gain a better understanding of the relationship between SIF and GPP, we investigated the variation in the GPP/SIF ratio in response to low-temperature conditions using satellite and tower-based datasets. Based on the TROPOMI SIF product and FLUXCOM GPP data, we found that the SIF and GPP exhibited consistent seasonal and spatial patterns, while the GPP/SIF ratio differed for different PFTs. The GPP/SIF ratio for forest types was generally higher than 10 gC·d−1·mw−1·nm·sr, whereas the GPP/SIF ratio for grass and crop types was generally lower than 10 gC·d−1·mw−1·nm·sr. In addition, there were noticeable differences in the seasonal pattern of the GPP/SIF ratio between the selected samples that experienced low-temperature stress (below 10 °C, defined as group A) and those that grew under relatively warm conditions (above 10 °C throughout the year, defined as group B). The GPP/SIF ratio for group A generally exhibited a “hump-shaped” seasonal pattern, and that for group B showed a slightly “bowl-shaped” seasonal pattern, which means it is important to consider the effects of temperature on the SIF-GPP relationship. Through linear regression and correlation analysis, we demonstrate that there was a positive correlation between the GPP/SIF ratio and temperature for group A, with a wide temperature range including low-temperature conditions, indicating that, in this case, temperature affected the SIF–GPP relationship; however, for group B—with a temperature higher than 10 °C throughout the year—the GPP/SIF ratio was not consistently affected by temperature. The response of GPP/SIF to low temperature stress was confirmed by tower-based observations at a C3 cropland (C3CRO) site and a boreal evergreen needleleaf forest (BoENF) site. Although the relationship between the GPP/SIF ratio and temperature differed among PFTs, the GPP/SIF ratio decreased under low-temperature conditions for PFTs. Therefore, the GPP/SIF ratio was not constant and was largely influenced by low temperature for different PFTs, thus highlighting the importance of incorporating temperature into SIF-based GPP estimation.

Sun-induced chlorophyll fluorescence (SIF) is a proxy for plant photosynthesis. However, available SIF products either have a low spatial resolution or are spatially discontinuous. This paper proposes an improved downscaling method to generate a continuous 0.05-degree SIF dataset from GOME-2 retrievals, covering the period from February 2007 to March 2019. First, a random forest model was developed to predict SIF using training samples from GOME-2 SIF and explanatory variables with a resolution of 0.5 degrees (including reflectance at visible and near-infrared bands, vegetation index, temperature, and cosine values of the sun-zenith angle). Then, a 0.05-degree SIF dataset was predicted using the trained model and the corresponding explanatory variables at a resolution of 0.05 degrees. Subsequently, the predicted 0.05-degree SIF dataset was used as the weighting coefficient to redistribute the original 0.5-degree GOME-2 SIF to a downscaled 0.05-degree SIF dataset (DSIF) based on the energy conservation principle. The results showed that DSIF was more consistent with the original 0.5-degree retrievals than the similar GOME-2 derived spatially extended SIF dataset, which produced spatial details with a resolution of 0.05 degrees. The structure and physiology of SIF information were well represented in the DSIF, which is imperative for assessing global photosynthetic activity.

Structural–physiological factors affect the accurate estimation of vegetation gross primary production (GPP) under various types of environmental stress. Solar-induced chlorophyll fluorescence (SIF), which is directly linked to photosynthesis, has been effectively used to estimate and monitor GPP. However, understanding of the physiological mechanism linking SIF to GPP under stress remains limited. In this article, the link between SIF and GPP at diurnal and seasonal timescales was explored for a maize field and its response to drought stress (as defined by the crop water stress index, CWSI) using three-years of continuous tower-based measurements was investigated. The results show that the ratio of GPP to SIF decreased with increasing drought stress levels, and the canopy stomata conductance (Gs) declined synchronously. Compared to two canopy structural factors (NDVI and NIRv), both the Pearson and partial correlation coefficients for the relationship between Gs and the ratio of GPP to the total SIF was higher (0.38, p < 0.01 and 0.32, p < 0.01 with photosynthetically active radiation as the control variable, respectively). We also found that ${{\bf \Phi }_F}$ tracked the changes in LUE well under drought conditions (CWSI > 0.6), which demonstrated that SIF can be a powerful parameter for estimating GPP under drought stress. However, there was a smaller drop in ${{\bf \Phi }_F}$ under drought stress (slope = –0.002) compared to the slope for the relationship between LUE and CWSI (–0.08). The response of light reactions to drought stress may be muted compared to the stomatal response. These findings confirm that the Gs is sensitive to drought and is important for the SIF-based GPP estimation model. It also provides reliable evidence that SIF data include a large amount of physiological information and can serve as a potential indicator for detecting drought and estimating GPP.

Reliable estimation of gross primary production (GPP) of terrestrial ecosystems is crucial in the global carbon cycle and ecosystem functioning studies. Solar-induced chlorophyll fluorescence (SIF) has emerged as an unprecedented proxy for terrestrial GPP estimation. TanSat SIF with a 2-km fine spatial resolution provides an excellent opportunity to investigate the relationships of satellite SIF and eddy covariance (EC) GPP at ecosystem or site scale, while has not been investigated. In this article, we investigated the relationship between TanSat SIF and EC GPP based on flux tower sites encompassing different biomes over the globe. TanSat SIF exhibited strong relationships with EC GPP at Fe and KI bands for instantaneous and daily timescales. The correlations between EC GPP and TanSat SIF (R2 = 0.61) were much better than moderate resolution imaging spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF)-corrected vegetation indices (VIs) (R2 = 0.48–0.58). Further comparisons between SIF and GPP with absorbed photosynthetically active radiation (APAR) and two environmental factors (fTmin and fVPD) showed that SIF responded to APAR and was similarly affected by environmental factors as GPP. GPP derived from SIF (R2 = 0.64) also performed slightly better than enhanced vegetation index and near-infrared reflectance of vegetation and even light use efficiency MODIS GPP algorithm (R2 = 0.58–0.60). Therefore, our results demonstrated the feasibility of SIF on estimating GPP at sites or ecosystem levels; in addition, incorporating TanSat SIF with other satellite SIF products will open a new era for carbon cycling and ecosystem functioning studies.

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 (SIFyield = SIFtotal/APAR) is an important source of uncertainty in SIF-GPP models. Incident radiation is one of the key factors influencing LUE and SIFyield. In this study, to investigate the influence of PAR on LUE and SIFyield, 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 (R-2 = 0.978) and canopy level (R-2 = 0.460 for half-hourly averaged dataset; R-2 = 0.341 for daily averaged dataset). However, the variation of SIFyield 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 R-2 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 mu mol CO2 m(-2) s(-1) for the half-hourly NIR band SIF). These results highlight that the ratio of LUE to SIFyield 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.

This is the dataset used in the paper "Improving SIF-based GPP estimation for maize by integrating a response model of LUE to PAR" by the same authors. Please let us know if you want to use the dataset before the paper is published. If there is any question, please contact liuxj@radi.ac.cn.

Gross primary production (GPP) is a fundamental variable in estimating crop yield and terrestrial-vegetation carbon cycling. This paper tested the near-infrared (NIR) radiance of vegetation (NIRVRad) for estimating GPP, where NIRVRad is defined as the product of observed NIR radiance and the normalized difference vegetation index (NDVI). Continuous observations of the canopy spectra and carbon flux of maize in 2017 and 2018 ​at two flux tower sites were used to evaluate the performance of NIRVRad for GPP estimation. The results show that NIRVRad well tracks the GPP at both half-hourly and daily timescales, with more accurate results than the parameterized LUE model employing the MOD17A2 GPP product, the solar-induced chlorophyll fluorescence (SIF), or the absorbed photosynthetically active radiation by vegetation (APAR). Furthermore, the results suggest that the temporal covariant relationship between NIR reflectance and LUE may be the real basis for the improved GPP estimation using NIRVRad. Therefore, NIRVRad, as a proxy for the canopy structure-radiation component of SIF, has potential to be a promising approach to estimating GPP by remote sensing.

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.