Retrieval of Solar-induced Chlorophyll Fluorescence from TanSat

Solar-induced chlorophyll fluorescence (SIF) is emitted during photosynthesis in plant leaves. It constitutes a 17 small additional offset to reflected radiance and can be observed by sensitive instruments. The Chinese global carbon dioxide 18 monitoring satellite (TanSat), as its mission, acquires greenhouse gas column density. The advanced technical characteristics 19 of the hyper-spectrum grating spectrometer (ACGS) onboard TanSat enable SIF retrieval from space observations in the O220 A band. In this study, one-year SIF data was processed from Orbiting Carbon Observatory-2 (OCO-2) and TanSat using a 21 physical-based algorithm. A comparison between the SIF retrieved from OCO-2 and its official product shows their strong 22 linear relationship (R2 > 0.85) and suggests the reliability of the algorithm. The global distribution showed that the SIF 23 retrieved from the two satellites shared the same spatial pattern for all seasons with the grided SIF difference less than 0.3 W 24 m−2 μm−1 sr−1, and they also agreed with the official OCO-2 SIF product. The retrieval uncertainty of seasonal-grided TanSat 25 SIF is less than 0.03 W m−2 μm−1 sr−1 whereas the uncertainty of each sounding ranges from 0.1 to 0.6 W m−2 μm−1 sr−1. The 26 relationship between SIF and terrestrial gross primary productivity was also estimated for data quality testing. The 27 spatiotemporal consistency between TanSat and OCO-2 and their comparable data quality make the comprehensive usage of 28 the two mission products possible. Data supplemented by TanSat observations are expected to contribute to the development 29 of global SIF maps with more spatiotemporal detail, which will advance global research on vegetation photosynthesis. 30 https://doi.org/10.5194/amt-2021-66 Preprint. Discussion started: 27 September 2021 c © Author(s) 2021. CC BY 4.0 License.


Introduction
Terrestrial vegetation accounts for a large part of the ecosystem, with its photosynthesis and respiration processes playing important roles in the global carbon cycle.Incoming radiation is absorbed, reflected, and/or transmitted by plant leaves.A portion of the absorbed radiation is used by the chlorophyll in plant leaves for carbon fixation, while the rest is either dissipated as heat or re-emitted as solar-induced chlorophyll fluorescence (SIF) at longer wavelengths (Frankenberg et al., 2011a(Frankenberg et al., , 2014;;).In contrast to the traditional remotely sensed vegetation indices obtained from some studies (Frankenberg et al., 2011b;Guanter et al., 2014;Li et al., 2018;Sun et al., 2017a;Yang et al., 2015;Zhang et al., 2014), SIF offers the potential to measure photosynthesis activity and gross primary production (GPP), due to the strong correlation between these measures (Frankenberg et al., 2011b;Guanter et al., 2012Guanter et al., , 2014)).The fluorescence emission (Fs) adds a low-intensity radiance less than 10 W m −2 μm −1 sr −1 and fills in the solar absorption features of the reflected spectrum (Frankenberg et al., 2011a).The filling-in effect of the solar lines (Fraunhofer lines) is the basic principle applied to measure SIF from space using the capabilities of hyperspectral observation (Frankenberg et al., 2011b;Guanter et al., 2012).
The first attempt at SIF research based on space-based observations was performed using images acquired by the Middle Resolution Imaging Spectrometer (MERIS) onboard the ENVIronmental SATellite (ENVISAT) (Guanter et al., 2007).This led to a new idea for conducting SIF studies on a global scale.The first global SIF map was retrieved with high-resolution spectra from the Greenhouse-gases Observing SATellite (GOSAT) (Joiner et al., 2011).After that, SIF retrievals were implemented from a variety of satellite measurements, such as those from the Global Ozone Monitoring Experiment-2 (GOME-2) instruments onboard meteorological operational satellites, SCIAMACHY on board ENVISAT, and Orbiting Carbon Observatory-2 (OCO-2) (Joiner et al., 2016;Köhler et al., 2015).The TROPOspheric Monitoring Instrument (TROPOMI) on board Sentinel 5 Precursor (S-5P) provides more efficient SIF observations in terms of global coverage and new opportunities for exploring the application potential of SIF data in the terrestrial biosphere as well as in climate research (Doughty et al., 2019;Köhler et al., 2018b).Furthermore, an upcoming European Space Agency mission called FLuorescence EXplorer (FLEX), the first satellite dedicated to SIF emission observation, will launch in the middle of 2024 (Drusch et al., 2017).Many studies on SIF applications have been initiated with the accumulation of SIF products in recent years.The responses of satellite-measured SIF to environmental conditions have been applied to drought dynamics monitoring and regional vegetation water stress estimation (Lee et al., 2013;Sun et al., 2015;Yoshida et al., 2015).As a proxy of photosynthesis, SIF acts as a powerful constraint parameter in estimating carbon exchange in an ecosystem between the atmosphere, ocean, and soil; as such, the analysis of the relationship between SIF and GPP has become an important research topic (Li et al., 2018;Köhler et al., 2018a;Sun et al., 2017a;Zhang et al., 2018).The strong linear relationship between them paves the way for improving terrestrial ecosystem model simulation of GPP, along with consequent improvement of global carbon flux estimation (MacBean et al., 2018;Yin et al., 2020).GPP estimations based on satellitemeasured SIF have proven to be an effective method validated by in-situ flux observations (Joiner et al., 2018;Qiu et al., 2020).However, uncertainty in the factors that determine the relationship between SIF and GPP still exists and is a key  (Chen et al., 2020;Doughty et al., 2019;Li et al., 2020;Qiu et al., 2020;Yin et al., 2020).
The Chinese global carbon dioxide monitoring satellite (TanSat) was launched in December 2016.TanSat flies in a sunsynchronous orbit at approximately 700 km in height with a 16-day repeat cycle and an equator crossing time of ~1:30 p.m. local time (Cai et al., 2014;Liu et al., 2018;Yang et al., 2018).Onboard TanSat, the hyperspectral Atmospheric Carbondioxide Grating Spectrometer (ACGS) is designed to separately record solar backscatter spectra in three channels centered at 0.76 μm (O2-A band), 1.61 μm (weak CO2 absorption band), and 2.06 μm (strong CO2 absorption band).The high spectral resolution of ~0.044 nm and a signal-to-noise ratio of ~360 in the O2-A band makes it possible to obtain SIF from space measurements, with a spatial resolution of 2 km × 2 km in nadir mode (Liu et al., 2018).
Various approaches have been used to infer SIF from satellite measurements (Frankenberg et al., 2011b(Frankenberg et al., , 2014;;Guanter et al., 2007Guanter et al., , 2012Guanter et al., , 2015;;Joiner et al., 2011Joiner et al., , 2013Joiner et al., , 2016;;Köhler et al., 2015Köhler et al., , 2018b)).The SIF signal induces a filling-in effect of solar lines, which can be used for SIF retrieval, as the fractional depth of solar Fraunhofer lines does not change during radiation transmission in the atmosphere.To recognize the filling-in features by SIF, high-resolution spectra and an instrument spectral response function (ISRF) are required to describe subtle changes in the spectral absorption lines.With the detailed spectrum features, a method was developed based on solar line fitting and the Beer-Lambertian law.This method is robust and accurate when the spectrum is out of the influence of telluric absorptions, even in the presence of aerosols (Frankenberg et al., 2011a;Joiner et al., 2011); in the current study, this method was applied to develop the IAPCAS/SIF algorithm.Another SIF retrieval method is the data-driven algorithm based on the singular value decomposition (SVD) technique (Guanter et al., 2012), which has been broadly applied in GOSAT and TROPOMI SIF retrieval.In the data-driven method, the spectrum is represented as a linear combination of the SIF signal and several singular vectors that are trained from non-fluorescent scenes by SVD; thus, the SIF signal can be obtained with linear leastsquares fitting (Du et al., 2018;Guanter et al., 2012).In a previous study, a preliminary comparison between the TanSat SIF https://doi.org/10.5194/amt-2021-66Preprint.Discussion started: 27 September 2021 c Author(s) 2021.CC BY 4.0 License.products retrieved by IAPCAS/SIF algorithm and the SVD data-driven method was performed to test the IAPCAS/SIF algorithm and for further algorithm optimization (Yao et al., 2021).In this study, we introduce TanSat SIF retrieval using the IAPCAS/SIF algorithm and the comparison of SIF products between TanSat and OCO-2.To avoid duplication of information, we use the SIF product at 757nm as the example in the analysis.

Data and retrieval algorithm 2.1 Retrieval Principle and Method
We used TanSat version 2 Level 1B (L1B) nadir-mode earth observation data in the retrieval process.The measurements covered the period from March 2017 to February 2018.Polarized radiance in the O2-A band with a spectral resolution of 0.044 nm was provided in the L1B data, and two micro-windows near 757 nm (758.3-759.2nm) and 771 nm (769.6-770.3nm) were chosen to retrieve the top-of-atmosphere (TOA) SIF while avoiding the contamination from strong lines of atmospheric gas absorption.The retrieval was independent for each micro-window as shown in Figure 1.Filling-in on solar lines by chlorophyll fluorescence in the O2-A band can be detected in the hyperspectral measurements from TanSat.This effect on spectral radiance is different from the impact of atmospheric and surface processes, e.g., scattering and absorption.For example, scattering by aerosols and clouds does not change the relative depth of clear solar lines, unlike the SIF emission signal.We applied the differential optical absorption spectroscopy (DOAS) technique to IAPCAS/SIF algorithm for TanSat measurement.
The TOA spectral radiance ( ) at wavelength can be represented as follows: where t is the incident solar irradiance at the TOA, 0 is the cosine of the solar zenith angle (SZA), 0 is atmospheric path reflectance, is surface reflectance, and ↓ and ↑ are the total atmospheric transmittances along the light-path in the downstream and upstream directions, respectively.
is the SIF radiance at TOA.
The first term on the right of Eq. ( 1) represents the transmission process of solar radiance.In the micro-windows used in SIF retrieval, gas absorption is very weak and smooth, and hence, the atmosphere term 0 ) can be simplified to a low-order polynomial that varies with λ (Joiner et al., 2013;Sun et al., 2018); this is always valid as long as the spectrum fitting range is out of sharp atmospheric absorptions.In the retrieval, the spectral radiance measurement was converted to logarithmic space by the instrument and the radiative transfer process , was represented as follows: where < > denote the convolution with the ISRF from line-by-line spectra.The polynomial coefficient determines the wavelength dependence polynomial for the atmosphere term; in the retrieval, we used a second-order polynomial (n = 2).
The radiance is normalized to the continuum level; hence, t is a normalized disk-integrated solar transmission model, and is the normalized relative SIF.In the micro-window, SIF was regarded as a constant signal due to its small changes.
Although the atmospheric gas absorption was very weak in the micro-window, the weak absorption and the far-wing effects (O2 lines) can still change spectral features, which induces errors in spectrum fitting.Therefore, we used the European Centre for Medium-Range Weather Forecasts (ECMWF) interim surface pressure (0.75°× 0.75°) to estimate O2 absorption firstly and modified the absorption feature by a scale factor to reduce the error induced by the uncertainty in surface pressure.
The scale factor is obtained simultaneously in SIF retrieval.As described by Yang (2020), there is also a continuum feature in TanSat L1B data that needs to be considered for the high-quality fitting of the O2-A band.However, in this study, this continuum feature was not corrected, as the impact of such a smooth continuum variation in the micro-window is weak and the polynomial continuum model is capable of compensating for most of this effect.
The state vector list in the retrieval includes the relative SIF signal , a wavenumber shift, the scale of O2 column absorption for surface pressure correction, and coefficients of the polynomial.The continuum level radiance within the fitting window is calculated using the side radiance in the micro-window and is then used for the actual SIF signal calculation thus: = • .
In the IAPCAS/SIF algorithm, we used an OEM for state vector optimization in the retrieval process.Considering that the complexity of SIF retrieval is lower than that of XCO2 retrieval, the Gauss-Newton method was applied in inversion iteratively to find the optimal solution.

Bias Corrections
A significant error remains in the raw SIF retrieval output if no bias correction is performed; similar results have been reported in GOSAT and OCO-2 SIF retrieval studies (Frankenberg et al., 2011a(Frankenberg et al., , 2011b;;Sun et al., 2018).This is because the SIF signal is weak (e.g., typically ~1-2% of the continuum level radiance), which means that even a small issue in the measurement, such as a zero-offset caused by radiometric calibration error, could induce significant bias.Unfortunately, the lack of knowledge on in-flight instrument performance makes it difficult to perform a direct systematic bias correction in the measured spectrum.In the retrieval, a continuum level radiance bin fit was used to estimate the bias.The bins have a continuum level radiance interval of 5 W m −2 μm −1 sr −1 .In each bin, the mean bias was estimated using all non-fluorescence measurements, and a piecewise linear function was built from the mean bias of each continuum level radiance interval.
The non-fluorescence soundings that were used in the bias estimation were based on the dataset "sounding_landCover" in TanSat L1B data.This dataset depends on the MODIS land cover product and provides a scheme consisting of 17 land cover classifications defined by the International Geosphere-Biosphere Programme.These retrieved measurements marked as "snow and ice," "barren," and "sparsely vegetated" were chosen to estimate the bias.Calibrations compensated for most of the instrument degradations, but this alone was not perfect.To reduce the impact from the remaining minor discrepancies, we built the bias correction function daily to obtain bias for each sounding via interpolation of the continuum level radiance (Sun et al., 2017b(Sun et al., , 2018)).
The bias curves shown in Figure 2 differ significantly between TanSat and OCO-2.This is mostly due to the differences in instrument performance and radiometric calibration.In general, the TanSat bias curves exhibited two peaks at radiance levels of approximately 40 and 125 W m −2 μm −1 sr −1 , separately, and most biases were larger than 0.015.For OCO-2, the curves dropped sharply at low radiance levels, reaching the valley at a radiance level of approximately 40 W m −2 μm −1 sr −1 , and then increased slowly with the radiance level.

Data Quality Controls
Only data that passed quality control were used in further applications.There were two data quality control processes for the SIF products: pre-screening and post-screening.Pre-screening focused mainly on cloud screening; only cloud-free measurements were used in SIF retrieval.A surface pressure difference (SPD), defined as: where , , and represent the model fitting spectrum, observation spectrum, and spectrum noise, respectively.
is the apparent surface pressure obtained from O2-A band surface pressure retrieval, assuming a single scattering atmosphere. is the ECMWF interim (0.75°× 0.75°) surface pressure.A "cloud-free" measurement was required to simultaneously satisfy an SPD of less than 20 hPa and a 2 value of less than 80.Here, post-screening was applied to filter out "bad" retrievals; this screening process involved the following steps: (1) SIF retrievals with reduced 2 ( 2 ) values ranging from 0.7 to 1.3 were considered "good" fitting, (2) continuum level radiance between 15 and 200 W m −2 μm −1 sr −1 was screened out to avoid scenes too bright or too dark, and (3) soundings with the SZA higher than 60°were also filtered out.
Table 1 displays the relationship of OCO-2 SIF values between the IAPCAS/SIF and IMAP-DOAS at 757 nm microwindow for each month.Overall, the two SIF products were in good agreement.The linear fitting of the two SIF products suggests that they are highly correlated, as indicated by the strong linear relationship with R 2 mostly larger than 0.85 and the root mean square error (RMSE) of about 0.2 W m −2 μm −1 sr −1 .Good consistency between the two SIF products implies the reliability of the IAPCAS/SIF algorithm; thus, it was further applied for TanSat SIF retrieval.However, there was still a small bias remained in the comparisons, which was due, most likely, to the impact of differences in the bias correction method, retrieval algorithm, and fitting window.

Comparison between TanSat and OCO-2 SIF Measurements
Directly comparing OCO-2 and TanSat SIF measurements could provide information on joint data application at the sounding scale for further studies.However, an identical sounding overlap is unlikely because the two satellites have different nadir tracks on the ground, which means that the orbits normally remain temporal and spatial interval.Fortunately, the ground tracks of the two satellites were relatively close from April 17 to April 23, 2017.A couple of overlapping orbits were found in the measurements obtained from Africa with the orbit number of 1733 from TanSat and 14890a from OCO-2 (Figure 3).These overlapping measurements encompassed multiple land cover types, in which the SIF varied within an acceptable time difference (<5 min).
Overall, measurements from the two satellites indicated SIF variation with land cover type.The SIF emission over evergreen broadleaf forests was larger than that over savannas, and grasslands exhibited the lowest SIF emission in April (Figure 3a,b).
Furthermore, we also found a significant difference in the SIF emission intensity over tropical savannas, which was observed by both satellites (Figure 3c,d).Because the footprint sizes of the two satellites are different, it is difficult to make a direct footprint-to-footprint comparison.
Therefore, we made the comparison between the two satellite measurements based on a small area average.Each small area spans a degree in latitude and continues along the track.The small area-averaged SIF comparison is shown in Figure 3e.The results indicate good agreement, with an R 2 of 0.94 and an RMSE of 0.096 W m −2 μm −1 sr −1 .Additional ground-based SIF measurement setups (Guanter et al., 2007;Liu et al., 2019;van der Tol et al., 2016;Yang et al., 2015a;Yu et al., 2019) should allow for direct evaluation of satellite retrieval accuracy in the future.Figure 4 shows the global SIF comparison between OCO-2 and TanSat; this comparison is only performed at 1°× 1°spatial resolution.In general, the difference in SIF globally is mostly less than 0.3 W m −2 μm −1 sr −1 for all seasons, and on average, the smallest difference appears in fall.There are regional biases observed in North Africa, South Africa, South America, and Europe in all seasons except fall.This is mainly caused by the differences in instrument performance between TanSat and OCO-2, as well as bias corrections.The bias correction compensates for most of the bias caused by instrument performance; however, small biases could remain.Furthermore, the hundreds of kilometers of distance between the OCO-2 and TanSat footprints, for example, over different vegetation regions, will also cause some measurement discrepancies.
The uncertainty σ of each sounding was estimated to validate SIF reliability and is provided in the product.σ is derived from the retrieval error covariance matrix, = 0 −1 −1 , where is the Jacobian matrix from the forward model fitting and 0 is the measurement error covariance matrix that is calculated from the instrument spectrum noise.In general, σ ranges from 0.1 to 0.6 W m −2 μm −1 sr −1 for both TanSat and OCO-2 measurements in the 757 nm fitting window, which is of a similar magnitude and data range as those of previous studies (Du et al., 2018;Frankenberg et al., 2014).Meanwhile, the retrieval uncertainty σ of each grid was estimated from TanSat SIF values with σ = , where σ represents the standard deviation of the grid cell with soundings, is the retrieved SIF values of each sounding, and is the mean SIF value for all measurements in the grid.As depicted in the right column of Figure 4, the σ of each grid cell is much lower than the precision of a single sounding.The retrieval uncertainty for South America is larger than that for any other region on the globe (Figure 4i-l).This is similar to that of OCO-2 SIF retrieval and caused by fewer effective measurements due to the South Atlantic Anomaly (Sun et al., 2018).The difference in SIF emission values between the two satellites indicates that the collaborative usage of two satellite SIF products still requires analysis of the impact of instrument differences, although the two satellite SIF products share the same spatiotemporal pattern on a global scale.

SIF Global Distribution and Temporal Variation
The SIF emission intensity reflects the growth status of vegetation due to its correlation with photosynthetic efficiency; hence, the overall global vegetation status can be represented by global SIF maps for each season.TanSat SIF over a whole year's cycle, from March 2017 to February 2018, is represented seasonally as a 1°× 1°grid spatially.The seasonal variation in SIF emission is clear in the Northern Hemisphere, i.e., it is enhanced from spring to summer and then decreases (Sun et al., 2018).
In general, the SIF emission varied with latitude and the vegetation-covered areas near the equator maintained a continuous SIF emission throughout the year.Large SIF emissions in the Northern Hemisphere, above 1.5 W m −2 μm −1 sr −1 , mostly from the eastern U.S., southeast of China, and southern Asia in summer, were due to the large areas of cropland.There was also an obvious SIF emission of 1-1.2 W m −2 μm −1 sr −1 observed over Central Europe and northeastern China during the summer.
In these regions, croplands and deciduous forests contribute to SIF emissions.In the Southern Hemisphere, the strongest SIF where there is an evergreen broadleaf rainforest.Africa, which is covered by evergreen broadleaf rainforests and woody savannas, had an average SIF value of 0.7-1.5 W m −2 μm −1 sr −1 during the year.
The SIF-GPP relationship over different vegetation types was also investigated by comparing the annual mean satellite SIF measurements with the FLUXCOM GPP (Jung et al., 2020;Tramontana et al., 2016) dataset in a 1°× 1°grid over the globe.
The FLUXCOM GPP dataset used in the study comprises monthly global gridded flux products with remote sensing and meteorological/climate forcing (RS+METEO) setups, which are derived from mean seasonal cycles according to MODIS data and daily meteorological information (Jung et al., 2020;Tramontana et al., 2016).The satellite-measured SIF is an instantaneous emission signal that varies with incident solar radiance within the day.To reduce the differences caused by the observation time and SZA at different latitudes, we applied a daily adjustment factor to convert the instantaneous SIF emission into a daily mean SIF (Du et al., 2018;Frankenberg et al., 2011b;Sun et al., 2018).The daily adjustment factor d is calculated as follows:  Figure 5 shows the linear fits for six vegetation types, including needle leaf forest, evergreen broadleaf forest, shrubland, savanna, grassland, and cropland.Recent studies have shown a strong linear correlation between SIF and GPP.To make a direct comparison of the relationship between SIF and GPP among various vegetation types, we used non-offset linear fitting to indicate the correlation between satellite SIF and FLUXCOM GPP.For savanna and cropland, there were strong relationships between the mean SIF and GPP with an R-value above 0.84.The fitting results show that the SIF products of the two satellites have similar capabilities in characterizing GPP, especially for the evergreen broadleaf forest, savanna, and cropland, with slopes of approximately 21, 18, and 13, respectively.The markedly different fitting slopes across various biomes suggest that the application of SIF in GPP estimation needs more detailed analysis although the evidence of the strong linear relationship between them.

Conclusions
In this paper, we introduced the retrieval algorithm IAPCAS/SIF and its application in TanSat and OCO-2 measurements.
https://doi.org/10.5194/amt-2021-66Preprint.Discussion started: 27 September 2021 c Author(s) 2021.CC BY 4.0 License.limitation in the application of SIF to flux estimation.Based on multi-satellite SIF products, eddy covariance flux tower observations, and ecological models, the relationship between SIF and GPP under different environmental conditions has been discussed in a number of studies to analyze the dominant factors for the growing status of different biomes, such as temperature, soil moisture, and vegetation types

Figure 1 :
Figure 1: The fitted spectra and residuals for the (a) 757 nm and (b) 771 nm micro-windows of TanSat measurement.The error bar of the measured spectra depicts the estimated precision of each TanSat sounding.

Figure 3 :
Figure 3: Overlapping orbits of TanSat and OCO-2 on April 19, 2017 over Africa displayed in Google Earth, (a) the SIF measurements of the two satellites and (b) the footprint land cover type were compared.Compared to OCO-2, TanSat has a wider swath width.A zoom-in view over savannas shows variations in the SIF signal measured by (c) OCO-2 and (d) TanSat.The land surface image shown in Google earth is provided by Landsat/Copernicus team.The vertical legend on the bottom right corner depicts the land cover type occurs in the study area following the International Geosphere-Biosphere Programme classification scheme, and the middle horizontal color bar represents the intensity of the SIF radiance.(e) Small-area SIF comparison between OCO-2 and TanSat; each data point represents the mean SIF of a degree in latitude (colors) along the track.The marker legend that is shown on the bottom right of the plot indicates the dominant land cover (defined as the majority land cover type of each sounding) in each small area.There are six land cover types including evergreen broadleaf forest (EBF), open shrubland (OSL), woody savanna (WSAV), savanna (SAV), grassland (GRA), and barren land (BL).The red dashed line represents the linear fit between the two SIF products with statistics shown in the upper left of the plot.The gray line indicates a 1:1 relationship for reference.

) where 0
is the observation time in fractional days and is a function of latitude, longitude, and time for calculating the SZA of the measurements.The annual averaged SIF is calculated from the daily mean SIF.To evaluate the relationship between SIF and GPP on the periodic scale of vegetation growth status, annually-averaged data were used in the regression fitting analysis.https://doi.org/10.5194/amt-2021-66Preprint.Discussion started: 27 September 2021 c Author(s) 2021.CC BY 4.0 License.

Figure 5 :
Figure 5: Relationship between annual mean SIF and FLUXCOM gross primary production (GPP) from March 2017 to February 2018.Red and blue dots represent OCO-2 and TanSat SIF grids, respectively.Fitted lines and statistics for OCO-2 and TanSat are shown in each plot.
One-year (March 2017-February 2018) TanSat SIF data was introduced and compared with OCO-2 measurements in this study.The TanSat and OCO-2 SIF products based on the IAPCAS/SIF algorithm are available on the Cooperation on the Analysis of carbon SAtellites data (CASA) website, www.chinageoss.org/tansat.Comparisons between TanSat and OCO-2 measurements directly, using a case study, and indirectly, with global 1°×1°grid data, showed consistency between the two satellite missions, indicating that the coordinated usage of the two data products is possible in future studies.With increasing satellites becoming available for SIF observations, space-based SIF observations have recently expanded in range to provide broad spatiotemporal coverage.The next-generation Chinese carbon monitoring satellite (TanSat-2) is now in the preliminary design phase.SIF measurements from TanSat-2 will provide global data products over broader coverage areas with less noise.The improvement in the spatiotemporal resolution of SIF data will benefit GPP predictions based on the numerous studies of the linear relationship between SIF and GPP.In future work, the measurement accuracy should be validated directly using ground-based measurements to ensure data quality.

Table 1 :
Summary of the relationship between the Institute of Atmospheric Physics Carbon Dioxide Retrieval Algorithm for Due to the lack of OCO-2 measurements in August 2017, the comparison is only performed for 11 months.