China's Terrestrial Carbon Sink Over 2010–2015 Constrained by Satellite Observations of Atmospheric CO<sub>2</sub> and Land Surface Variables

He, Wei; Jiang, Fei; Wu, Mousong; Ju, Weimin; Scholze, Marko; Chen, Zhi; Byrne, Brendan; Liu, Junjie; Wang, Hengmao; Wang, Jun; Wang, Songhan; Zhou, Yanlian; Zhang, Chunhua; Nguyen, Ngoc Tu; Shen, Yang; Chen, Jing M.

doi:10.1029/2021jg006644

Cited by 10 publications

(11 citation statements)

References 77 publications

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“…The CCDAS inversion with constraints from remotely sensed land variables is able to reasonably indicate drought-induced NEP reductions in both 2012 and 2015, yet not so in the prior simulated by TBM. The CCDAS flux estimates have also been effective in indicating environmental stresses in previous studies (He, Jiang, Wu, et al, 2022;Wu et al, 2020). Different from the way adopted by Jena inversions, CCDAS assimilates remote sensing data (e.g., SM, VOD, and FAPAR) in addition to in situ atmospheric CO 2 concentrations to constrain carbon fluxes through the optimization of underlying parameters in TBM.…”

Section: Importance Of Ancillary Environmental Variables For Capturin...mentioning

confidence: 99%

“…Over most of their history, AIMs have primarily been employed to study the continental‐to‐global scale carbon cycle by assimilating CO 2 measurements from a sparse network of surface sites. However, AIMs are increasingly being applied to regional carbon flux estimation (Byrne, Liu, Lee, et al., 2020; Friedlingstein et al., 2019; He, Jiang, Wu, et al., 2022; He, van der Velde, et al., 2018; Monteil et al., 2020) and assess carbon cycle response to climate extremes (He, Ju, et al., 2018; J. Liu et al., 2018; Molina et al., 2015; van der Laan‐Luijkx et al., 2015; Wolf et al., 2016).…”

Section: Introductionmentioning

confidence: 99%

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Do State‐Of‐The‐Art Atmospheric CO₂ Inverse Models Capture Drought Impacts on the European Land Carbon Uptake?

Jiang

et al. 2023

J Adv Model Earth Syst

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The European land carbon uptake has been heavily impacted by several recent severe droughts, yet quantitative estimates of carbon uptake anomalies are uncertain. Atmospheric CO 2 inverse models (AIMs) provide observation-based estimates of the large-scale carbon flux dynamics, but how well they capture drought impacts on the terrestrial carbon uptake is poorly known. Here we assessed the capacity of state-of-the-art AIMs in monitoring drought impacts on the European carbon uptake over 2001-2015 using observations of environmental variability and vegetation function and made comparisons with bottom-up estimates of carbon uptake anomalies. We found that global inversions with only limited surface CO 2 observations give divergent estimates of drought impacts. Regional inversions assimilating denser CO 2 observations over Europe demonstrated some improved consistency, with all inversions capturing a reduction in carbon uptake during the 2012 drought. However, they failed to capture the reduction caused by the 2015 drought. Finally, we found that a set of inversions that assimilated satellite XCO 2 or assimilated environmental variables plus surface CO 2 observations better captured carbon uptake anomalies induced by both the 2012 and 2015 droughts. In addition, the recent Orbiting Carbon Observatory-2 XCO 2 inversions showed good potential in capturing drought impacts, with better performances for larger-scale droughts like the 2018 drought. These results suggest that surface CO 2 observations may still be too sparse to fully capture the impact of drought on the carbon cycle at subcontinental scales over Europe, and satellite XCO 2 and ancillary environmental data can be used to improve observational constraints in atmospheric inversion systems.Plain Language Summary Atmospheric CO 2 inverse models (AIMs) are useful tools for quantifying the response of large-scale carbon uptake to climate extremes, but their capacity for monitoring drought impacts, particularly at regional scales, is not fully explored. In this study, we assessed the capacity of state-of-the-art AIMs for monitoring drought impacts on the European land carbon uptake over 2001-2015 using a large array of observational and model data sets. We found: (a) global inversions with only limited surface CO 2 observations face a great challenge in monitoring drought impacts on the European carbon uptake; (b) Regional inversions assimilated denser CO 2 observations over Europe, for the EUROCOM project, demonstrated some improved consistency but are still deficient, showing divergent estimates in interannual variability of carbon uptake for most years; and (c) A set of inversion systems that assimilated satellite XCO 2 or assimilated environmental variables plus surface CO 2 observations better captured annual and seasonal HE ET AL.

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Section: Importance Of Ancillary Environmental Variables For Capturin...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Do State‐Of‐The‐Art Atmospheric CO₂ Inverse Models Capture Drought Impacts on the European Land Carbon Uptake?

Jiang

et al. 2023

J Adv Model Earth Syst

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Section: Discussionmentioning

confidence: 99%

“…Overall, the regional inversion system has demonstrated a comparable carbon sink to those obtained with other methods, which reinforces our confidence in the validity of the approach in future observations. Complementarity of in situ and satellite retrievals could enhance uncertainty reductions in inversions (Crowell et al., 2019; He et al., 2022; Jiang et al., 2022; S. J. Wang, Kawa et al., 2018). In the longer term, due to the significant interannual variation of the terrestrial carbon sink, this work also serves as a basis for future multi‐year retrospective analysis of biosphere–atmosphere exchanges in different climatic conditions.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, at the General Debate of the 75th Session of the United Nations General Assembly on 22 September 2020, China pledged to reach a peak in CO 2 emissions by 2030 and achieve carbon neutrality by 2060. However, both natural carbon uptake and anthropogenic emissions show significant spatial differences and temporal variations (Andres et al., 2012; J. M. Chen, Ju, et al., 2019; He et al., 2022; Piao et al., 2012). Therefore, accurately estimating CO 2 flux is one of the critical points of the carbon budget when evaluating the effectiveness of China's progress toward carbon neutrality, as well as meeting the “Monitoring, Verification, Supporting” mitigation commitment of the UNFCCC.…”

Section: Introductionmentioning

confidence: 99%

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CO₂ Flux Inversion With a Regional Joint Data Assimilation System Based on CMAQ, EnKS, and Surface Observations

et al. 2023

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Most of China's carbon sink inversion research uses global atmospheric transport models to assimilate natural fluxes, which quantifies the biosphere and ocean carbon budget with a relative coarse spatial resolution and long timescale from a weekly or monthly perspective. Toward high‐resolution inversion of CO2 fluxes, a novel carbon flux forecast model was developed in this study, which was then further used to carry out carbon assimilation based on a regional chemical transport model (CMAQ) at higher spatial (64 × 64 km2) and temporal (1 hr) resolutions. An Ensemble Kalman Smoother was applied as the assimilation algorithm and further extended to assimilate surface CO2 observations. Concentrations and fluxes were simultaneously assimilated as state variables to help reduce the uncertainty in the initial CO2 fields with the joint data assimilation framework (JDAS). In general, the posterior fluxes reproduced the seasonal, daily and hourly variation effectively, demonstrating the ability to fully absorb and utilize observations. Moreover, the influence of the choice of assimilation window on the carbon flux inversion was assessed via sensitivity experiments, revealing 36 hr to be the optimum length. Evaluation of the prior and posterior flux simulations also indicated that JDAS offers reasonable improvements, making it suitable for fine‐scale flux optimization and estimation. In addition, the posterior biosphere estimation in mainland China (−682 TgC yr−1) tends to be the optimal mathematical solution under current sparse observation coverage with daytime photosynthetic uptake, which likely leads to the overestimation of the optimized CO2 sink. This study serves as a basis for future regional and urban assessment.

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A Globally Applicable Method for NDVI Estimation from Sentinel-1 SAR Backscatter Using a Deep Neural Network and the SEN12TP Dataset

Roßberg

Schmitt

2023

PFG

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Vegetation monitoring is important for many applications, e.g., agriculture, food security, or forestry. Optical data from space-borne sensors and spectral indices derived from their data like the normalised difference vegetation index (NDVI) are frequently used in this context because of their simple derivation and interpretation. However, optical sensors have one major drawback: cloud coverage hinders data acquisition, which is especially troublesome for moderate and tropical regions. One solution to this problem is the use of cloud-penetrating synthetic aperture radar (SAR) sensors. Yet, with very different image characteristics of optical and SAR data, an optical sensor cannot be easily replaced by SAR sensors. This paper presents a globally applicable model for the estimation of NDVI values from Sentinel-1 C-band SAR backscatter data. First, the newly created dataset SEN12TP consisting of Sentinel-1 and -2 images is introduced. Its main features are the sophisticated global sampling strategy and that the images of the two sensors are time-paired. Using this dataset, a deep learning model is trained to regress SAR backscatter data to NDVI values. The benefit of auxiliary input information, e.g., digital elevation models, or land-cover maps is evaluated experimentally. After selection of the best model configuration, another experimental evaluation on a carefully selected hold-out test set confirms that high performance, low error, and good level of spatial detail are achieved. Finally, the potential of our approach to create dense NDVI time series of frequently clouded areas is shown. One limit of our approach is the neglect of the temporal characteristics of the SAR and NDVI data, since only data from a single date are used for prediction.

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China's Terrestrial Carbon Sink Over 2010–2015 Constrained by Satellite Observations of Atmospheric CO₂ and Land Surface Variables

Cited by 10 publications

References 77 publications

Do State‐Of‐The‐Art Atmospheric CO₂ Inverse Models Capture Drought Impacts on the European Land Carbon Uptake?

Do State‐Of‐The‐Art Atmospheric CO₂ Inverse Models Capture Drought Impacts on the European Land Carbon Uptake?

CO₂ Flux Inversion With a Regional Joint Data Assimilation System Based on CMAQ, EnKS, and Surface Observations

A Globally Applicable Method for NDVI Estimation from Sentinel-1 SAR Backscatter Using a Deep Neural Network and the SEN12TP Dataset

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China's Terrestrial Carbon Sink Over 2010–2015 Constrained by Satellite Observations of Atmospheric CO2 and Land Surface Variables

Cited by 10 publications

References 77 publications

Do State‐Of‐The‐Art Atmospheric CO2 Inverse Models Capture Drought Impacts on the European Land Carbon Uptake?

Do State‐Of‐The‐Art Atmospheric CO2 Inverse Models Capture Drought Impacts on the European Land Carbon Uptake?

CO2 Flux Inversion With a Regional Joint Data Assimilation System Based on CMAQ, EnKS, and Surface Observations

A Globally Applicable Method for NDVI Estimation from Sentinel-1 SAR Backscatter Using a Deep Neural Network and the SEN12TP Dataset

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Product

Resources

About

China's Terrestrial Carbon Sink Over 2010–2015 Constrained by Satellite Observations of Atmospheric CO₂ and Land Surface Variables

Do State‐Of‐The‐Art Atmospheric CO₂ Inverse Models Capture Drought Impacts on the European Land Carbon Uptake?

Do State‐Of‐The‐Art Atmospheric CO₂ Inverse Models Capture Drought Impacts on the European Land Carbon Uptake?

CO₂ Flux Inversion With a Regional Joint Data Assimilation System Based on CMAQ, EnKS, and Surface Observations