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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., Competing Interests: The authors declare no conflict of interest.

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 O 2 -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.

The measurement of solar-induced chlorophyll fluorescence (SIF) is a new tool for estimating gross primary production (GPP). Continuous tower-based spectral observations together with flux measurements are an efficient way of linking the SIF to the GPP. Compared to conical observations, hemispherical observations made with cosine-corrected foreoptic have a much larger field of view and can better match the footprint of the tower-based flux measurements. However, estimating the equivalent radiation transfer path length (ERTPL) for hemispherical observations is more complex than for conical observations and this is a key problem that needs to be addressed before accurate retrieval of SIF can be made. In this paper, we first modeled the footprint of hemispherical spectral measurements and found that, under convective conditions with light winds, 90% of the total radiation came from an FOV of width 72°, which in turn covered 75.68% of the source area of the flux measurements. In contrast, conical spectral observations covered only 1.93% of the flux footprint. Secondly, using theoretical considerations, we modeled the ERTPL of the hemispherical spectral observations made with cosine-corrected foreoptic and found that the ERTPL was approximately equal to twice the sensor height above the canopy. Finally, the modeled ERTPL was evaluated using a simulated dataset. The ERTPL calculated using the simulated data was about 1.89 times the sensor's height above the target surface, which was quite close to the results for the modeled ERTPL. Furthermore, the SIF retrieved from atmospherically corrected spectra using the modeled ERTPL fitted well with the reference values, giving a relative root mean square error of 18.22%. These results show that the modeled ERTPL was reasonable and that this method is applicable to tower-based hemispherical observations of SIF.

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]

Leaf chlorophyll content (LCC) is a prominent plant physiological trait and a proxy for leaf photosynthetic capacity. The acquisition of LCC data over large spatial and temporal scales facilitates vegetation growth monitoring and terrestrial carbon cycle modeling. In this study, a global 500 m LCC weekly dataset (GLCC) was produced from ENVISAT MERIS and Sentinel-3 OLCI satellite data using a physical radiative transfer modeling approach that considers the influence of canopy structure and soil background. Firstly, five look-up-tables (LUTs) were generated using PROSPECT-D+4-Scale and PROSAIL-D models for woody and non-woody plants. For the four LUTs applicable to woody plants, each LUT contains three sub-LUTs corresponding to three types of crown height. The one LUT applicable to non-woody vegetation type includes 25 sub-LUTs corresponding to five kinds of canopy structures and five kinds of soil backgrounds. The final retrieval was considered the aggregation of the LCC inversion results of all sub-LUTs for each plant function type (PFT). Then, the GLCC dataset was generated and validated using field measurements, yielding an overall accuracy of R2 = 0.41 and RMSE = 8.94 μg cm−2. Finally, the GLCC dataset presented acceptable consistency with the existing MERIS LCC dataset. OLCI, as the successor to MERIS data, was used for the first time to co-produce LCC data from 2003–2012 to 2018–2020 in conjunction with MERIS data. This new GLCC dataset spanning nearly 20 years will provide a valuable opportunity to analyze variations in vegetation dynamics. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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

• The SIF/GPP ratio of maize changes during the seasonal cycles. • LCC dominates the seasonal dynamics of the ratio. • The contribution of LCC was reinforced when the impacts of canopy structure was minimized. The coupling of solar-induced chlorophyll fluorescence (SIF) and gross primary production (GPP) is the foundation of SIF-based GPP estimations; however, the relationship between them varies in different conditions. Structural changes contribute much to the dynamics of their relationship at the canopy scale, whereas the role of physiological mechanisms is not very clear. Here, based on three-year continuous observations from 2018 to 2020 in a maize field in Northwest China, we obtained the total SIF (tSIF) at the photosystem scale and investigated the seasonal dynamics of its link with GPP. Using the ratio of tSIF to GPP, we eliminated the contribution of canopy structure and discovered an increase in the ratio during the late reproductive and ripening stages. Seasonal variation in the ratio was tracked by the leaf chlorophyll contents (LCC) related to the photosynthetic functional maturity (represented by maximum carboxylation rate, Vcmax). In addition, we also found that there was variation in the regression slope of the relationship between SIF/GPP and LCC at different growth stages. The correlation between tSIF/GPP and LCC was better than that between dSIF/GPP (dSIF, the ratio of directional SIF at canopy scale to GPP) and LCC, which demonstrated that the physiological information is reinforced after the elimination of structural contributions. Overall, the seasonal dynamics of the GPP–tSIF relationship in our study highlighted the necessity of considering the growing stage in SIF-based GPP estimations. Although they are usually covered up by the contribution of the canopy structure, physiological mechanisms still impacted the dynamics of the GPP–SIF relationship. [Display omitted] [ABSTRACT FROM AUTHOR]

Three diurnal experiments were conducted on winter wheat to investigate how solar-induced chlorophyll fluorescence (SIF) responded to Paraquat treatments (a PSI herbicide). First, a slight increase in spectral reflectance in the red region and an obvious decrease in reflectance in the NIR regions was observed due to crop injury from Paraquat. Secondly, a significant increase in SIF at 688 nm, but a significant decrease at 760 nm was observed. The opposite response trends of SIF confirm the mechanism of PSI herbicides in photosynthetic injury. Finally, the sensitivity of SIF signals were compared to other vegetation indices, and the Fr688/760 ratio turned out to be a sensitive tool to rapidly detect herbicide injury. [ABSTRACT FROM PUBLISHER]

Numerous researchers have described the chlorophyll fluorescence (ChlF) difference of C3 and C4 plants under different environmental conditions. This study presents a novel passive detection method to discriminate between the C3 and C4 plant functional types (PFTs) using solar-induced ChlF. The solar-induced ChlF radiation was extracted from airborne hyperspectral data acquired by the Operational Modular Imaging Spectrometer (OMIS) II instrument. Our results showed that the fluorescence signal of C4 species was about 2.2 times greater than that of C3 species at the same normalized difference vegetation index (NDVI) level. The average value of the relative ChlF for C4 species was 18.2 w m−2 μm−1 compared with 7.8 w m−2 μm−1 for C3 species. The classification potential of the solar-induced ChlF for C3 and C4 species was compared with the spectral reflectance and NDVI. The spectral separation rate of the solar-induced ChlF was 1.31, but the maximum value of the spectral reflectances was 0.63 at the 750 nm band, and the spectral separation rate of NDVI was only 0.04. (For Gaussian distributions, pairwise separation rates of 90% corresponded to a value of 1.5, whereas a value of 0.55 corresponded to a separation rate of about 70%.) A simple decision tree was built to discriminate between C3 and C4 species based on the difference between their ChlF. An accuracy assessment indicated that C3 and C4 species have been well classified; the overall classification accuracy was 92% and the kappa coefficient was 0.84. Therefore, although it is quite challenging to classify C3 and C4 species based on the reflectance signal, our results present a novel method for successfully discriminating between C3 and C4 species. [ABSTRACT FROM AUTHOR]

To develop a time-Specific and time-critical spatial variable rate nitrogen application (VRN) method and to overcome the limitations of traditional field sampling methods, this study focused on the relationship between SPAD chlorophyll meter readings and nitrogen content in leaves in order to determine the amount of nitrogen fertilisation required for agricultural objectives. Field experiments were conducted in three wheat growth duration stages from 2003 to 2006. Grain yields and soil NO3-N contents were measured in all plots. Our results indicated that VRN technology reduced wheat yield spatial variabitity. The benefits of VRN included low soil residual NO3-N content and NO3-N leaching potential, suggesting that VRN technology based on SPAD readings can potentially reduce groundwater pollution and therefore protect our limited environmental resources. [ABSTRACT FROM AUTHOR]

Estimates of crop canopy chlorophyll content (CCC) can be used to monitor vegetation productivity, manage crop resources, and control disease and pests. However, making these estimates using conventional ground-based methods is time-consuming and resource-intensive when deployed over large areas. Although vegetation indices (VIs), derived from satellite sensor data, have been used to estimate CCC, they suffer from problems related to spectral saturation, soil background, and canopy structure. A new method was, therefore, proposed for combining the Medium Resolution Imaging Spectrometer (MERIS) terrestrial chlorophyll index (MTCI) and LAI-related vegetation indices (LAI-VIs) to increase the accuracy of CCC estimates for wheat and soybeans. The PROSAIL-D canopy reflectance model was used to simulate canopy spectra that were resampled to match the spectral response functions of the MERIS carried on the ENVISAT satellite. Combinations of the MTCI and LAI-VIs were then used to estimate CCC via univariate linear regression, binary linear regression and random forest regression. The accuracy using the field spectra and MERIS data was determined based on field CCC measurements. All the MTCI and LAI-VI combinations for the selected regression techniques resulted in more accurate estimates of CCC than the use of the MTCI alone (field spectra data for soybeans and wheat: R2 = 0.62 and RMSE = 77.10 μg cm−2; MERIS satellite data for soybeans: R2 = 0.24 and RMSE = 136.54 μg cm−2). The random forest regression resulted in better accuracy than the other two linear regression models. The combination resulting in the best accuracy was the MTCI and MTVI2 and random forest regression, with R2 = 0.65 and RMSE = 37.76 μg cm−2 (field spectra data) and R2 = 0.78 and RMSE = 47.96 μg cm−2 (MERIS satellite data). Combining the MTCI and a LAI-VI represents a further step towards improving the accuracy of estimation CCC based on multispectral satellite sensor data. [ABSTRACT FROM AUTHOR]

Leaf chlorophyll content (LCC) is a pivotal parameter in the monitoring of agriculture and carbon cycle modeling at regional and global scales. ENVISAT MERIS and Sentinel-3 OLCI data are suitable for use in the global monitoring of LCC because of their spectral specifications (covering red-edge bands), wide field of view and short revisit times. Generally, remote sensing approaches for LCC retrieval consist of statistically- and physically-based models. The physical approaches for LCC estimation require the use of radiative transfer models (RTMs), which are more robust and transferrable than empirical models. However, the operational retrieval of LCC at large scales is affected by the large variability in canopy structures and soil backgrounds. In this study, we proposed an improved look-up-table (LUT) approach to retrieve LCC by combining multiple canopy structures and soil backgrounds to deal with the ill-posed inversion problem caused by the lack of prior knowledge on canopy structure and soil-background reflectance. Firstly, the PROSAIL-D model was used to simulate canopy spectra with diverse imaging gometrics, canopy structures, soil backgrounds and leaf biochemical contents, and the canopy spectra were resampled according to the spectral response functions of ENVISAT MERIS and Sentinel-3 OLCI instruments. Then, an LUT that included 25 sub-LUTs corresponding to five types of canopy structure and five types of soil background was generated for LCC estimation. The mean of the best eight solutions, rather than the single best solution with the smallest RMSE value, was selected as the retrieval of each sub-LUT. The final inversion result was obtained by calculating the mean value of the 25 sub-LUTs. Finally, the improved LUT approach was tested using simulations, field measurements and ENVISAT MERIS satellite data. A simulation using spectral bands from the MERIS and Sentinel-3 OLCI simulation datasets yielded an R2 value of 0.81 and an RMSE value of 10.1 μg cm−2. Validation performed well with field-measured canopy spectra and MERIS imagery giving RMSE values of 9.9 μg cm−2 for wheat and 9.6 μg cm−2 for soybean using canopy spectra and 8.6 μg cm−2 for soybean using MERIS data. The comparison with traditional chlorophyll-sensitive indices showed that our improved LUT approach gave the best performance for all cases. Therefore, these promising results are directly applicable to the use of ENVISAT MERIS and Sentinel-3 OLCI data for monitoring of crop LCC at a regional or global scale. [ABSTRACT FROM AUTHOR]

Solar-induced chlorophyll fluorescence (SIF) has been proven to be an efficient indicator of vegetation photosynthesis. To investigate the relationship between SIF and Gross Primary Productivity (GPP), tower-based continuous spectral observations coordinated with eddy covariance (EC) measurements are needed. As the strong absorption effect at the O2-A absorption bands has an obvious influence on SIF retrieval based on the Fraunhofer Line Discrimination (FLD) principle, atmospheric correction is required even for tower-based SIF observations made with a sensor tens of meters above the canopy. In this study, an operational and simple solution for atmospheric correction of tower-based SIF observations at the O2-A band is proposed. The aerosol optical depth (AOD) and radiative transfer path length (RTPL) are found to be the dominant factors influencing the upward and downward transmittances at the oxygen absorption band. Look-up tables (LUTs) are established to estimate the atmosphere transmittance using AOD and RTPL based on the MODerate resolution atmospheric TRANsmission 5 (MODTRAN 5) model simulations, and the AOD is estimated using the ratio of the downwelling irradiance at 790 nm to that at 660 nm (E790/E660). The influences of the temperature and pressure on the atmospheric transmittance are also compensated for using a corrector factor of RTPL based on an empirical equation. A series of field measurements were carried out to evaluate the performance of the atmospheric correction method for tower-based SIF observations. The difference between the SIF retrieved from tower-based and from ground-based observations decreased obviously after the atmospheric correction. The results indicate that the atmospheric correction method based on a LUT is efficient and also necessary for more accurate tower-based SIF retrieval, especially at the O2-A band. [ABSTRACT FROM AUTHOR]

• LCC was retrieved from GF-6 WFV data based on the red-edge VIs. • The applicability of VIs for LCC estimation was evaluated. • Canopy structure-insensitive VI showed generality in LCC estimation. • GF-6 LCC maps provided accurate decametric-resolution LCC information. Accurate and timely retrieval of leaf chlorophyll content (LCC) at fine spatial scales is crucial for monitoring plants' photosynthetic capacity and nutritional status. The wide field-of-view camera (WFV) onboard Gaofen-6 (GF-6) satellite has two red-edge bands at 16-meter spatial resolution, providing an opportunity to derive decametric-resolution LCC. However, the potential of GF-6 WFV data in LCC estimation has yet to be studied. Vegetation indices (VIs) are widely used in LCC estimation due to their simplicity and efficiency, and the accuracy of LCC inversion can be improved with red-edge VIs. This study focused on exploring the retrieval of LCC with GF-6 WFV using the VI-based method by assessing the applicability of nine red-edge VIs derived from GF-6 WFV. Our results revealed that (1) the newly developed canopy structure-insensitive CSI (chlorophyll sensitive index) achieved comparable accuracy to the physically-based method and reduced the limitation of the VI-based method being species- and time-specific. Validation results showed that the root-mean-square error (RMSE) of the estimated LCC from the CSI-based model was 7.91–11.38 μg/cm2 for four plant functional types (similar accuracy to Croft et al. (2020) based on the lookup table method with RMSE of 9.25–13.18 μg/cm2). The accuracy of the CSI-based model remained stable across species and growth conditions, indicating its feasibility for large-scale applications. (2) GF-6 WFV provided accurate LCC estimates and well-captured the spatial and seasonal variations in LCC. GF-6 LCC maps showed comparable accuracy (R2 = 0.53, RMSE = 9.78 μg/cm2) and similar spatiotemporal distribution to the 30-m Sentinel-2 LCC products. Additionally, GF-6 LCC maps outperformed Sentinel-2 LCC products regarding observation frequency and spatial continuity. Our study highlights the potential of GF-6 WFV in retrieving decametric-resolution LCC and provides further support for applications of GF-6 WFV data in precision agriculture and forestry. [ABSTRACT FROM AUTHOR]