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# Constraining China's land carbon sink from emerging satellite CO 2 observations: progress and challenges

Yilong Wang, Xiangjun Tian, Frederic Chevallier, Matthew Johnson, Sajeev Philip, David Baker, Andrew Schuh, Feng Deng, Xingying Zhang, Lu Zhang, et al.

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## To cite this version:

Yilong Wang, Xiangjun Tian, Frederic Chevallier, Matthew Johnson, Sajeev Philip, et al., Constraining China's land carbon sink from emerging satellite CO 2 observations: progress and challenges. Global Change Biology, 2022, 28 (23), pp.6838-6846. 10.1111/gcb.16412. hal-03775340

HAL Id: hal-03775340

https://hal.science/hal-03775340v1

Submitted on 8 Aug 2025

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OPINION

# Constraining China's land carbon sink from emerging satellite CO₂ observations: Progress and challenges

Yilong Wang<fnref n="1" /> ID | Xiangjun Tian<fnref n="1" />,<fnref n="2" /> | Frédéric Chevallier<fnref n="3" /> | Matthew S. Johnson<fnref n="4" /> |
Sajeev Philip<fnref n="5" /> | David F. Baker<fnref n="6" /> | Andrew E. Schuh<fnref n="6" /> | Feng Deng<fnref n="7" /> | Xingying Zhang<fnref n="8" /> |
Lu Zhang<fnref n="8" /> | Dan Zhu<fnref n="9" /> ID | Xuhui Wang<fnref n="9" /> ID

Correspondence

Yilong Wang, State Key Laboratory of
Tibetan Plateau Earth System, Resources
and Environment (TPESRE), Institute
of Tibetan Plateau Research, Chinese
Academy of Sciences, Beijing, China.
Email: wangyilong@itpcas.ac.cn

Funding information

Second Tibetan Plateau Scientific
Expedition and Research Program,
Grant/Award Number: 2019QZKK0208;
National Natural Science Foundation of
China, Grant/Award Number: 42001104
and 42101090; NASA's Carbon Cycle
Science Program, Grant/Award Number:
80HQTR21T0101

Abstract

Land carbon sink is a vital component for the achievement of China's ambitious carbon neutrality goal, but its magnitude is poorly known. Atmospheric observations and inverse models are valuable tools to constrain the China's land carbon sink. Space-based CO₂ measurements from satellites form an emerging data stream for application of such atmospheric inversions. Here, we reviewed the satellite missions that is dedicated to the monitoring of CO₂, and the recent progresses on the inversion of China's land carbon sink using satellite CO₂ measurements. We summarized the limitations and challenges in current space platforms, retrieval algorithms, and the inverse modeling. It is shown that there are large uncertainties of contemporary satellite-based estimates of China's land carbon sink. We discussed future opportunities of continuous improvements in three aspects to better constrain China's land carbon sink with space-based CO₂ measurements.

KEYWORDS

atmospheric inversion, China's land carbon sink, retrieval algorithm, satellite, XCO₂

1 | INTRODUCTION

The threats of climate change have made reducing net carbon emissions toward carbon neutrality an international consensus. To date, more than 100 countries have committed to achieving carbon neutrality around the middle of this century (Chen, 2021). Among them,

China has announced a particularly ambitious goal of carbon neu-
trality in just 30 years after reaching peak emissions around 2030.
This goal can be achieved mainly by reducing China's carbon diox-
ide (CO₂) emissions from the use of fossil fuels and the production
of cement, by using carbon credits to offset the emissions, and by
enhancing China's land carbon sink from agriculture, forestry, and

<footnote n="1">¹State Key Laboratory of Tibetan Plateau Earth System, Resources and Environment (TPESRE), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China</footnote>

<footnote n="2">²University of Chinese Academy Sciences, Beijing, China</footnote>

<footnote n="3">³Laboratoire des Sciences du Climat et de l'Environnement, LSCE-IPSL (CEA-CNRS-UVSQ), Université Paris-Saclay, Gif-sur-Yvette, France</footnote>

<footnote n="4">⁴Earth Science Division, NASA Ames Research Center, Moffett Field, California, USA</footnote>

<footnote n="5">⁵Centre for Atmospheric Sciences, Indian Institute of Technology Delhi, New Delhi, India</footnote>

<footnote n="6">⁶CIRA, Colorado State University, Fort Collins, Colorado, USA</footnote>

<footnote n="7">⁷Department of Physics, University of Toronto, Toronto, Ontario, Canada</footnote>

<footnote n="8">⁸Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, National Satellite Meteorological Center (National Center for Space Weather) and Innovation Center for FengYun Meteorological Satellite (FYSIC), China Meteorological Administration (CMA), Beijing, China</footnote>

<footnote n="9">⁹Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China</footnote>
other land use (Mallapaty, 2020; Piao, Yue, et al., 2022), but the magnitude of the latter is poorly known (Chevallier, 2021; Piao, He, et al., 2022; Wang, Wang, et al., 2022).

Accounting for the land carbon sink under the Enhanced Transparency framework has become a priority for both the scientific community and policymakers (UNFCCC. Conference of the Parties (COP), 2015). The 2006 IPCC Guidelines for National Greenhouse Gas Inventories of the Intergovernmental Panel on Climate Change (IPCC) highlighted the value of atmospheric observations (e.g., surface-based and airborne measurements, satellite retrievals) and inverse models to evaluate the reported fluxes. This position was reinforced in the 2019 refinement of these guidelines. However, current atmospheric inversions for China using in situ CO₂ observations differ by up to an order of magnitude (Chen et al., 2021; Wang, Wang, et al., 2022). One of the important reasons is the scarcity of in situ CO₂ observation sites: there are only seven sites in mainland China under the World Meteorological Organization/Global Atmosphere Watch program (WMO/GAW) (Wang et al., 2020).

Extending the in situ CO₂ observation network would certainly help, but only if carefully selected sites with good representative-ness were to be added (Wang, Wang, et al., 2022). In the case of Europe, a continent with an area similar to China, an extensive network of 39 operational measurement sites still leaves a large range of inversion estimates for the land carbon flux, from a net source of 0.28 Pg C year⁻¹ to a net sink of 0.41 Pg C year⁻¹ (Monteil et al., 2020).

Clear-sky column-average CO₂ dry air-mole fractions (XCO₂) retrieved from satellite radiance measurements (Eldering et al., 2017; Rayner & O'Brien, 2001) form an emerging data stream of atmospheric CO₂ observations for application in atmospheric inversions. With a wide spatial coverage that includes regions poorly covered by surface in situ observation networks, satellite retrievals have been shown as an important tool for identifying continental land carbon sink anomalies in recent years (Liu et al., 2017; Palmer et al., 2019). Retrieving CO₂ mole fractions from satellite measurements can be summarized in two steps: (1) acquiring measurements of reflected light within CO₂ absorption bands by satellite-borne sensors and (2) using a retrieval algorithm to estimate XCO₂ and other relevant atmospheric and surface variables. In the following sections of this paper, we review satellite missions with CO₂ observing capability, the retrieval algorithms, and the inversion estimates of China's land carbon sink using satellite CO₂ measurements. Finally, we discuss future opportunities to constrain China's land carbon sink with space-based CO₂ measurements.

## 2 | GREENHOUSE GAS SATELLITE MISSIONS: TOWARD SMALLER MEASUREMENT UNCERTAINTY AND BROADER SPATIOTEMPORAL COVERAGE

The earliest efforts to retrieve atmospheric CO₂ abundance from satellite radiance measurements used thermal infrared (TIR) sounders, including the High-resolution Infrared Radiation Sounder-2 on the

NOAA-10 satellite (Chédin et al., 2005), the Atmospheric Infrared Sounder on the National Aeronautics and Space Administration (NASA) Aqua mission (Chahine et al., 2005; Crevoisier et al., 2004), the Infrared Atmospheric Sounding Interferometer on the European MetOp platform (Crevoisier et al., 2009), and the Tropospheric Emission Spectrometer on NASA's Aura satellite (Kulawik et al., 2010). TIR measurements are sensitive to CO₂ in the middle to upper troposphere, but not to the lower troposphere where the CO₂ mole fraction is most directly affected by land-atmosphere fluxes. Meanwhile, TIR CO₂ retrievals have had significant regional biases reaching 1–2 ppm (Chevallier, Engelen, et al., 2005; Chevallier, Fisher, et al., 2005). These factors greatly limit the application of TIR measurements for the study of the carbon cycle.

The CO₂ spectral bands in the shortwave infrared (SWIR) are sensitive to CO₂ near the surface and are thus better suited for inferring CO₂ fluxes. The SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY) was the first instrument to collect reflected solar radiation in the SWIR CO₂ bands. SCIAMACHY also measures the spectra of the near-infrared molecular oxygen (O₂) A-band to provide information about surface pressure, thin clouds, and small aerosols. SCIAMACHY was launched in 2002 onboard the European Space Agency (ESA) Environmental Satellite, but the communication was lost in 2012. SCIAMACHY had a broad swath of about 1000km and a large surface footprint (the area of the Earth surface covered by an individual sounding) of 30km × 60km. The large footprint, however, has a high chance of including clouds and aerosols in the light path. The presence of clouds and aerosols can modify the light path in ways that the measurements in the O₂ band cannot account for well enough, leading to significant biases in most retrievals. Moreover, the soundings heavily contaminated by clouds and aerosols are usually screened out before the estimation of fluxes, limiting the overall spatial coverage of SCIAMACHY.

Currently, operational missions dedicated to CO₂ detection and monitoring equipped with passive sensors include the Japanese Greenhouse gases Observing SATellite (GOSAT) and GOSAT-2, NASA's second and third Orbiting Carbon Observatory (OCO-2 and OCO-3) and China's TanSat. They all include SWIR sensors with smaller footprint and higher signal-to-noise ratios than SCIAMACHY. These missions explored various sampling strategies (Figure 1b) to depict the spatial distribution of XCO₂. GOSAT and GOSAT-2 acquire discrete soundings with medium-sized footprints (~10 km diameter) and large intervals between individual soundings (>100 km), while OCO-2 and TanSat collect contiguous data at smaller footprints (~2 km) along a narrow swath (<20 km). These SWIR-observing satellites have also been joined by the Chinese meteorological satellite FengYun-3D (FY-3D) and the Chinese High-Resolution Earth-observing satellite Gaofen-5, both of which adopted a sampling strategy similar to that of the GOSAT instruments.

Early simulations indicated that space-based XCO₂ retrievals with a 1-sigma random uncertainty as high as 2.5 ppm after averaging at monthly and regional scales could equal the skill of existing
<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /><figcaption>FIGURE 1 The retrieval uncertainty (a), coverage (b) and timeline (c) of SWIR CO₂ satellite missions. ACGS, atmospheric CO₂ grating spectrometer; GAS, greenhouse gases absorption spectrometer; GMI, greenhouse-gases monitoring instrument; TANSO-FTS, Thermal and near infrared sensor for carbon observation-Fourier transform spectrometer. [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

surface in situ measurement networks (56 stations at the writing of Rayner & O'Brien, 2001) for the estimation of surface CO₂ fluxes (Rayner & O'Brien, 2001), but also that even sub-ppm systematic errors could harm the inversion results (Chevallier, Engelen, et al., 2005). The performance of the instrument is among the most important determinants of the quality of retrieved XCO₂. Through the last decades, the precision (single sounding random error) and trueness (closeness of the mean of the measurements to an accepted reference that follows the World Meterological Organization Global Atmospheric Watch standards) of space-based XCO₂ measurements from current missions have been largely improved. In the case of OCO-2, using the advanced retrieval algorithm, the precision and trueness of official OCO-2 retrievals are now better than 1 ppm for individual soundings (Eldering et al., 2017; Wunch et al., 2017). However, cloud-free observations from existing missions only represent less than 2% of the Earth's surface area even on monthly scales, due to three limiting factors: (1) the sampling strategy of the instrument, for example, the size of surface footprint and the density of the soundings are limited; (2) the presence of optically thick clouds and aerosols; and (3) the inability of acquiring data in the absence of

sunlight (e.g., during the night time at low- or mid-latitudes or during the winter seasons at higher latitudes).

To improve the ability to capture the spatial and temporal variations of XCO₂, and thus better quantify the fluxes, future missions will further improve the precision and trueness of the observations and explore new observing strategies to enhance the coverage (Figure 1). For example, the MicroCarb mission led by the French Space agency/Centre National d'Études Spatiales, as well as the Copernicus Anthropogenic Carbon Dioxide Monitoring (CO2M) mission planned by the ESA and the European Commission aim to acquire observations with regional-scale biases smaller than 0.3 ppm and precision better than 1 ppm (Figure 1a) using complementary radiance measurements that will help characterize the light path in the presence of optically thin clouds and aerosols. NASA's Geostationary Carbon Cycle Observatory (GeoCarb) and the CO2M missions will acquire images of contiguous CO₂ data across a wide swath with high spatial resolution (Lespinas et al., 2020; O'Brien et al., 2016). Apart from the wider spatial coverage, the GeoCarb mission will sample the same locations for multiple times within a day during the day time. In addition, China launched the first satellite
carrying active sensors DQ-1 on April 16, 2022. This new satellite is expected to extend remotely sensed XCO₂ to night time. These geostationary and active sensors will largely improve the ability of space-based observations to understand the full diurnal changes of carbon cycle. Furthermore, the active sensors like DQ-1 not only have the ability to measure XCO₂ during the whole day, but also reduce errors in the retrievals induced by cloud and aerosol scattering using the timing of the laser pulses to get a more accurate light path length.

## 3 | CONTINUING IMPROVEMENTS IN CO₂ RETRIEVALS

A typical retrieval algorithm includes a radiative transfer model that simulates the solar radiation reflected by the Earth's surface and atmosphere to the observatory based on estimates of CO₂ mixing ratios and other atmospheric (e.g., clouds and aerosol distributions, temperature) and surface input variables (e.g., surface pressure, reflectance). The properties of the optics and electronics (e.g., instrument line shape, polarization, spectral resolution) were also accounted for to simulate the digital signals of spectra recorded by the instrument. An inverse procedure of the retrieval algorithm compares the modeled spectra with measured ones and looks for the optimal CO₂ mixing ratios and other parameters that minimize the difference between the modeled and measured spectra while they are most consistent with a prior constraint. In the retrieval algorithm, soundings significantly contaminated by clouds and aerosols must be identified and screened out, because it is too difficult to model them accurately.

The trueness of the CO₂ retrievals is primarily dependent on the understanding of the physics of radiative transfer, such as spectroscopy, gas absorption properties, cloud and aerosol scattering, surface reflectance, etc., and the radiometric, geometric, and electronic knowledge of the instrument. For example, gas absorption cross-section is a key parameter that describes the amount of radiation absorbed by gas molecules at a particular wavelength, and the uncertainties in such a parameter for CO₂ and O₂ within the satellite's spectral bands are among the critical limiting factors of the trueness of CO₂ retrievals. Guided by the latest laboratory measurements (Drouin et al., 2017), an update of O₂ A-band gas absorption cross-section going from NASA's OCO-2 version 7 to the version 8 retrieval algorithm improved the normalization of CO₂ amount to airmass and reduced biases and random errors in the retrieved XCO₂. Similarly, laboratory measurements and new analyses have improved the understanding of the line shape functions and are active research fields for further improving the retrieval trueness.

The identification, characterization, and quantification of clouds and aerosols are also critical for the proper functioning of the retrieval algorithms. GOSAT and TanSat include cloud imagers to identify soundings contaminated by optically thick clouds and aerosols, and detailed algorithms were developed for both of them as well as

for OCO-2 to screen out such soundings. However, substantial biases for some soundings impacted by cloud and aerosol remain and further improvements are required. Unlike optically thick clouds and aerosols, which may be identified and avoided, optically thin clouds and aerosols will introduce errors in the estimate of the light path length, and retrieval algorithms usually cannot fully filter observations influenced by these layers and need to account for the associated uncertainties. For example, in the case of the OCO-2 retrieval, the inclusion of an optically thin stratospheric aerosol layer in the radiative transfer model mitigated biases in the XCO₂ estimates at mid- to high-latitudes of the southern hemisphere during southern winter (O'Dell et al., 2018).

The calibration of the instrument performance is also critical for the reliability of retrieval products. While the challenges in the understanding of the physics and the filtering of impacts from clouds and aerosols are ubiquitous in retrieval algorithms across satellite missions, the instrument calibration requirements are more mission specific. For example, a pointing error in the OCO-2 observatory was identified to cause systematic biases and large variations in retrieved XCO₂ in regions with complex topography. Correcting this error was among the major improvements of the OCO-2 version 9 retrieval algorithm and it did increase the trueness of retrieved XCO₂ products (Kiel et al., 2019).

Apart from the above aspects, current retrieval algorithms are also limited by the computational speed of the radiative transfer model, since they must efficiently process the large volume of data (tens of thousands to millions each day) collected by current and future satellites. This will become even more important as the size of data is expected to be several times larger for a future constellation with multiple satellites. In addition to the full physics retrieval algorithms, simplified methods applying fast-speed emulators or machine-learning models have been developed to accelerate the computation of the radiative transfer model while still maintaining the trueness. For example, the Fast atmOspheric traCe gAs retrieval (FOCAL) algorithm approximates the multiple scattering effect with a single isotropic layer and an analytic solution, leading to a speed gain of 1–2 orders of magnitude (Noël et al., 2021). An artificial neural network technique was recently developed and used fewer input and computational resources than the full physics algorithm, substantially enhancing the speed without explicit physical interpretation (David et al., 2021).

The quality of the retrieved CO₂ data is usually evaluated using column CO₂ measurements from the Total Carbon Column Observing Network (TCCON) as well as CO₂ profiles collected from aircrafts and balloons (Karion et al., 2010). Evaluations show that the state-of-the-art CO₂ retrievals have an uncertainty of ~2 ppm for GOSAT and ~1 ppm for OCO-2 (Crisp, 2018; Noël et al., 2021; OCO-2 Science Team/Michael Gunson, Annmarie Eldering, 2019; Wunch et al., 2017), which are now the most widely used XCO₂ products. It should be noted that these metrics are still indicative since the coverage of the TCCON network is limited, with about 30 sites, and most of them were located in the mid- and high-latitudes of the northern hemisphere.
4 | INVERSION ESTIMATES OF CHINA'S LAND CARBON SINK USING SATELLITE OBSERVATIONS: LARGE UNCERTAINTIES IN BUDGETS AND SPATIOTEMPORAL VARIATIONS

We took the Multi-model Intercomparison Project which is an en-
semble of inverse models which assimilated version 9 OCO-2 obser-
vations (OCO-2 v9 MIP, https://gml.noaa.gov/ccgg/OCO2_v9mip/
index.php) as an example to discuss the current knowledge and chal-
lenges in the top-down estimates of the Chinese land carbon sink
constrained by satellite observations.

We collected flux results from the OCO-2 v9 MIP which in-
cludes 10 global CO₂ inverse models under a protocol that con-
trolled the satellite retrievals and uncertainties (Peiro et al., 2022).
We chose the inversions that assimilated OCO-2 land nadir and
land glint observations, and compared them to those assimilated in
situ CO₂ measurements from the OCO2MIP v2.2 ObsPack (https://
gml.noaa.gov/ccgg/OCO2_v9mip/index.php). Figure 2 shows
that the OCO-2-based inversion estimates of China's land car-
bon sink over 2015-2018 vary strongly between 0.00 Pg C year⁻¹
and 1.14 Pg C year⁻¹, with an ensemble mean of 0.61 Pg C year⁻¹.
It should be noted that the estimates by different models are not
evenly distributed within this range: the inversions using GEOS-
Chem as the atmospheric transport model have significantly higher
(p = .01) sink estimates (0.90 ±0.17 Pg C year⁻¹) than other inver-
sions (0.32 ±0.31 Pg C year⁻¹) (Figure 2a). A similar contrast be-
tween inversions using GEOS-Chem and other transport models
is also evident among in situ-based inversions. Schuh et al. (2019)
pointed out that there is a significant difference in the simulation
of 3D fields of CO₂ with GEOS-Chem, in comparison to the TM5
model, due to the representation of vertical motions such as deep
convection, planetary boundary layer mixing, and vertical advec-
tion/diffusion, which are known to be an important source of un-
certainty in the inversion estimates (Gaubert et al., 2019). And the
differences appeared to be somewhat directly correlated to varia-
tions in the China's land sink intensity reported by the correspond-
ing inversion models (Schuh et al., 2022).

Although the volume of satellite XCO₂ data is larger than that
of the in situ CO₂ measurements by about 2 orders of magnitude
within any given year, the uncertainties of contemporary satellite-
based estimates of China's land carbon sink are similar to those
using just the in situ estimates (Figure 2a,b). From the ensemble of
OCO-2 inversions, it is almost certain that China's land ecosystem
is a significant carbon sink, but satellite inversions do not constrain
its magnitude and interannual variations with a high degree of cer-
tainty. Figure 2c shows the spatial distribution of the annual mean
land-atmosphere CO₂ flux over China between 2015 and 2018
estimated as the ensemble mean of the OCO-2 inversions, which
highlights stronger land carbon sink intensity over the south and the
east than the north and the west. This is broadly consistent with
the inventory-based estimates and process-based modeling stud-
ies (Piao et al., 2009, 2012). However, uncertainties in this spatial

distribution remain large, as the standard deviation of the flux esti-
mates among inversions (Figure 2d) can be as large as the magnitude
of the fluxes themselves. Thus, the better spatial coverage of XCO₂
retrievals alone is not yet sufficient to provide robust constraints
on the spatial pattern of land-atmosphere CO₂ fluxes. Beyond the
uncertainties in atmospheric transport modeling mentioned above
and differences in prior fluxes, inversion algorithms, and observation
selection (Crowell et al., 2019; Philip et al., 2019), the very nature
of XCO₂, which combines air of various ages throughout the atmo-
spheric column, blurs the view of the surface fluxes more than what
the retrieval spatial density would suggest.

Two studies (Wang, Jiang, et al., 2022; Yang, Liu, Feng, et al., 2021) have used the retrieval products for 15 months (Yang, Liu, Boesch, et al., 2021) released by the TanSat group to estimate the global and regional land carbon sink by atmospheric inversion. The inversion estimates of global land carbon flux using TanSat XCO₂ retrievals are generally consistent with the inversions using OCO-2 retrievals. The inversion estimate of China's carbon land sink (Wang, Jiang, et al., 2022), in which GEOS-Chem was used as atmospheric transport model, is 0.95 Pg C year⁻¹, within the ensemble of those based on OCO-2 retrievals. These results provide first evidence that the TanSat platform obtained reasonable XCO₂ data and it could be promising to use such data to constrain carbon fluxes over northern middle latitudes.

5 | FUTURE PERSPECTIVES

Carbon sink accounting in a “measurable, reportable and verifiable” manner requires the establishment of an operational carbon monitoring system that provides accurate, reliable, and timely reports on regional carbon budgets. Satellite-based inversions will likely be part of such a system. While substantial progress has been made in the remote sensing of atmospheric CO₂ and atmospheric inverse modeling over the last two decades, there is a clear need for more improvements in the techniques for observing, analyzing, and communicating the results from the end user perspective (Figure 3).

Precise and accurate retrievals with higher spatial resolution and wider coverage are an essential prerequisite. Demanding requirements have been proposed for future missions whereby the CO₂ spectrometer must yield single sounding random errors in XCO₂ of better than 0.5 ppm and with systematic errors also better than 0.5 ppm to avoid large errors in the inversed fluxes (Chevallier, 2018; Crisp, 2018). Satellites with the capability to sample the atmosphere at a high spatial resolution are also required: footprints of single soundings of smaller than 5 km² along a wide swath (>200km), to have more satellite measurements free of cloud and aerosol scattering and to better sample the signals from point sources. In the near future, these would be realized through the improved radiometric, spectroscopic, geometric, and electronic performance of the spectrometers used in the upcoming missions. Besides instrument improvements, we expect the future missions to better depict optically thin clouds and aerosols, which should reduce biases in XCO₂ measurements.
<figure><img src="image_6.png" /><figcaption>FIGURE 2 Inversion results of Chinese land carbon sink over 2015–2018 from the OCO-2 V9 MIP. (a) Time series of Chinese land carbon sink from individual inversions (solid lines) assimilating OCO-2 version 9.1 retrievals over land. The bars show the comparison of the mean annual sink from inversions using GEOS-Chem (GC) as the transport model (Ames, CMS-Flux, CSU, UT) and the other inversions (nGC, including CT, OU, TM5-4DVAR, CAMS, G5APR, PCTM). (b) The same as (a), but assimilating in situ observations. (c) Mean distribution of the annual CO₂ fluxes over China from the 10 inversions. Positive values mean a carbon source to the atmosphere, and negative values indicate a carbon sink. (d) Standard deviation of the annual CO₂ fluxes over China among the 10 inversions. Map lines delineate study areas and do not necessarily depict accepted national boundaries. [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>FIGURE 3 Schematic diagram for future development of an operational space-based system to quantify China's land carbon sink. Map lines delineate study areas and do not necessarily depict accepted national boundaries. [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>
Forming a CO₂ constellation through the integration of satellites in LEO (low Earth orbit) and GEO (geostationary orbit) should also improve the spatial and temporal coverage of space-borne CO₂ measurements. As current satellites are all operating in LEO and collecting observations at a local time of around noon, GEO satellites and satellites carrying active sensors would complement the temporal sampling of the LEO passive sensors by providing a more complete diurnal coverage. Meanwhile, satellites in highly elliptical orbits with a focus on boreal and Arctic regions could help to fill the current spatial gaps (Nassar et al., 2014). Based on a well-designed instrument architecture, improved retrieval algorithms will be required to guarantee timely delivery of spatiotemporal gradients in atmospheric XCO₂ with a high precision and trueness to feed the inversion models.

With the precision and trueness of satellite XCO₂ retrievals improving, the inversion models deriving land-atmosphere CO₂ flux from XCO₂ would then become the limiting factor in reliable regional carbon sink accounting. Ensembles of atmospheric inversion systems will likely be needed for the foreseeable future. Furthermore, frequent evaluation of transport models in terms of boundary layer mixing, vertical convective transport, large-scale circulation (such as inter-hemisphere exchange), and stratosphere-troposphere exchange will be needed to improve the performance of these models. In addition to atmospheric transport modeling, careful consideration of uncertainties of prior fluxes could also help in improving the robustness of inversions. Close collaborations between communities in atmospheric inversion and biogeochemical flux inventories are thus needed to develop robust uncertainty estimates.

The great density of XCO₂ measurements from the emerging CO₂ constellations in combination with an increased focus on anthropogenic carbon sources such as cities and power plants, will also push the inversion modeling community to work at higher spatial resolution. For example, it would seem quite beneficial to be able to move from the 2° or coarser resolution typically used in global inversions today to below 1°, or down to 10 km in regional inversions (Monteil et al., 2020). These more advanced systems could also be adapted to perform a series of observation system simulation experiments, which provide guidance on the need to enlarge the constellation and to adapt the design of spectrometers (Broquet et al., 2018; Lespinas et al., 2020). In the areas and seasons where the viewing of satellites is obscured by clouds and aerosols often, observations from surface sites are also valuable data streams that will provide complementary and independent information than satellite measurements and make important contributions to the estimation of China's land carbon fluxes.

China joined the global efforts to monitor atmospheric CO₂ with its TanSat mission in 2016, and has gained extensive experience. The data acquired by TanSat have become less reliable after 2018 than its earlier stage. But in the coming decades, China will play an increasingly active role with several new satellites dedicated to CO₂ monitoring that have been launched (e.g., DQ-1) or are under consideration (e.g., the TanSat-2 constellation). A GEO satellite stationed over East Asia, in addition to the planned GeoCARB over North and

South America, would also be extremely useful to complement the global measurements from planned LEO ones. This blue-print of building a high-resolution inversion system with a CO₂ constellation consisting of multiple LEO and GEO satellites requires close international collaboration. Ensuring the high quality of the multiple sensors requires cross-calibration of the measurements and cross-validation of the retrievals among the countries and institutions operating the satellites; this should rely on a transparent data policy that guarantees long-term open access to global and regional data from all sensors. Like the pioneered framework of international meteorological measurements and re-analyses, international partnerships will also be critical in the efforts to deliver robust space-based CO₂ flux estimates to policymakers.

## 6 | CONCLUSIONS

In this study, we summarized the satellite missions that are dedicated to the monitoring of CO₂, and their usage for estimating China's CO₂ fluxes. We showed the progresses achieved in the precision, trueness, and spatial coverage of satellite measurements in the last decades. A Multi-model Intercomparison Project showed that there are still considerable uncertainties in the inversed CO₂ fluxes, given the state-of-the-art satellite observations. This result highlights urgent needs for continuous improvements in the satellite design, retrieval algorithms, and inversion techniques toward a better constraint of China's land carbon sink with space-based CO₂ measurements.

## ACKNOWLEDGMENTS

This study is supported by the Second Tibetan Plateau Scientific Expedition and Research Program (2019QZKK0208) and the National Natural Science Foundation of China (42001104). M. J.'s contribution was supported by NASA's Carbon Cycle Science Program (grant no. 80HQTR21T0101). D. Z. acknowledges support from the National Natural Science Foundation of China (42101090).

## CONFLICT OF INTEREST

The authors declare no conflict of interest.

## DATA AVAILABILITY STATEMENT

The atmospheric inversion results that support the findings of this study are openly available at https://gml.noaa.gov/ccgg/OCO2_v9mip/.

## ORCID

Yilong Wang https://orcid.org/0000-0001-7176-2692

Dan Zhu https://orcid.org/0000-0002-5857-1899

Xuhui Wang https://orcid.org/0000-0003-0818-9816

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**How to cite this article:** Wang, Y., Tian, X., Chevallier, F., Johnson, M. S., Philip, S., Baker, D. F., Schuh, A. E., Deng, F., Zhang, X., Zhang, L., Zhu, D., & Wang, X. (2022). Constraining China's land carbon sink from emerging satellite CO₂ observations: Progress and challenges. *Global Change Biology*, 28, 6838-6846. https://doi.org/10.1111/gcb.16412