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Evaluating the Impact of Atmospheric CO2 Emissions via Super Resolution of Remote Sensing Data

  • Conference paper
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Computational Science – ICCS 2024 (ICCS 2024)

Abstract

Understanding how emissions from point sources affect the atmospheric concentrations of Greenhouse Gases (GHGs) locally and on a wider scale is crucial to quantify their impact on climate change. To this end, different ways of performing global monitoring of GHGs concentration using remote sensing data have been explored. The main difficulty remains to find the right balance between high resolution monitoring, which is often incomplete, and global monitoring, but at a coarser resolution. This study proposes the application of Super Resolution (SR), a Deep Learning (DL) technique commonly employed in Computer Vision, to increase the resolution of atmospheric CO2 L3 satellite data. The resulting maps are achieving an approximate resolution of 1 km * 1 km and are then compared with a benchmark of existing methods, before being used for emissions monitoring.

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Abbreviations

CTM:

Chemical Transport Model

CV:

Computer Vision

DA:

Data Assimilation

DL:

Deep Learning

GHG:

Greenhouse Gas

HR:

high resolution

IR:

Improvement Ratio

LR:

Low Resolution

LST:

Land Surface Temperature

MAE:

Mean Absolute Error

ML:

Machine Learning

OCO-2:

Orbiting Carbon Observatory 2

RMSE:

Root Mean Square Error

SISR:

Single Image Super Resolution

SR:

Super Resolution

TCCON:

Total Column Carbon Network

XCO2:

column-averaged dry air mole fraction of atmospheric CO2

References

  1. Balashov, N., Weir, B., Ott, L., Basu, S.: Generating global CH4 NASA GEOS product by assimilating TROPOMI. In: AGU Fall Meeting. No. A15L-1387 (2022)

    Google Scholar 

  2. Buizza, C., et al.: Data learning: integrating data assimilation and machine learning. J. Comput. Sci. 58, 101525 (2022). https://doi.org/10.1016/j.jocs.2021.101525, https://www.sciencedirect.com/science/article/pii/S1877750321001861

  3. Climate TRACE coalition: Climate TRACE - Tracking Real-time Atmospheric Carbon Emissions. Climate TRACE Emissions Inventory (2022). https://climatetrace.org/

  4. Core Writing Team, Lee, H., Romero, J.: Climate change 2023: synthesis report. Contribution of working groups I, II and III to the sixth assessment report of the intergovernmental panel on climate change, pp. 35–115 (2023). https://doi.org/10.59327/IPCC/AR6-9789291691647

  5. Eldering, A., Boland, S., Solish, B., Crisp, D., Kahn, P., Gunson, M.: High precision atmospheric CO2 measurements from space: the design and implementation of OCO-2. In: 2012 IEEE Aerospace Conference, pp. 1–10. IEEE (2012)

    Google Scholar 

  6. Haris, M., Shakhnarovich, G., Ukita, N.: Deep back-projection networks for single image super-resolution. IEEE Trans. Pattern Anal. Mach. Intell. 43(12), 4323–4337 (2020)

    Article  Google Scholar 

  7. He, C., Ji, M., Grieneisen, M.L., Zhan, Y.: A review of datasets and methods for deriving spatiotemporal distributions of atmospheric CO2. J. Environ. Manage. 322, 116101 (2022)

    Article  Google Scholar 

  8. He, Z., et al.: Spatio-temporal mapping of multi-satellite observed column atmospheric CO2 using precision-weighted Kriging method. Remote Sens. 12(3), 576 (2020)

    Article  Google Scholar 

  9. Jacobson, A.R., Schuldt, K.N., Tans, P.: CarbonTracker CT2022. NOAA Global Monitoring Laboratory (2023). https://doi.org/10.25925/Z1GJ-3254

  10. Laughner, J.L., et al.: The total carbon column observing network’s GGG2020 data version. Earth Syst. Sci. Data Discuss. 2023, 1–86 (2023)

    Google Scholar 

  11. Li, J., et al.: High-spatiotemporal resolution mapping of spatiotemporally continuous atmospheric CO2 concentrations over the global continent. Int. J. Appl. Earth Obs. Geoinf. 108, 102743 (2022)

    Google Scholar 

  12. Li, S., et al.: PyTorch distributed: experiences on accelerating data parallel training. arXiv preprint arXiv:2006.15704 (2020)

  13. Pillai, D., Neininger, B.: Comparing Lagrangian and Eulerian models for CO 2 transport-a step towards Bayesian inverse modeling using WRF/STILT-VPRM. Atmos. Chem. Phys. 12(19), 8979–8991 (2012)

    Article  Google Scholar 

  14. Sheng, M., Lei, L., Zeng, Z.C., Rao, W., Song, H., Wu, C.: Global land 1\(^{\circ }\) mapping dataset of XCO2 from satellite observations of GOSAT and OCO-2 from 2009 to 2020. Big Earth Data 7(1), 170–190 (2023)

    Article  Google Scholar 

  15. Tsai, R.Y., Huang, T.S.: Multiframe image restoration and registration, vol. 1, pp. 317–339 (1984)

    Google Scholar 

  16. Veefkind, J.P., et al.: TROPOMI on the ESA Sentinel-5 precursor: a GMES mission for global observations of the atmospheric composition for climate, air quality and ozone layer applications. Remote Sens. Environ. 120, 70–83 (2012)

    Google Scholar 

  17. Wan, Z., Hook, S., Hulley, G.: MOD11C1 MODIS/Terra land surface temperature/emissivity daily L3 global 0.05Deg CMG V006 [data set] (2015). https://doi.org/10.5067/MODIS/MOD11C1.006

  18. Wang, Y., Yuan, Q., Li, T., Yang, Y., Zhou, S., Zhang, L.: Seamless mapping of long-term (2010–2020) daily global XCO 2 and XCH 4 from the greenhouse gases observing satellite (GOSAT), orbiting carbon observatory 2 (OCO-2), and CAMS global greenhouse gas reanalysis (CAMS-EGG4) with a spatiotemporally self-supervised fusion method. Earth Syst. Sci. Data 15(8), 3597–3622 (2023)

    Google Scholar 

  19. Weir, B., Ott, L.: OCO-2 Science Team: OCO-2 GEOS level 3 daily, 0.5 \(\times \) 0.625 assimilated CO2 v10r (2021)

    Google Scholar 

  20. Wunch, D., et al.: The total carbon column observing network. Philos. Trans. R. Soc. A: Math. Phys. Eng. Sci. 369(1943), 2087–2112 (2011)

    Google Scholar 

  21. Yang, W., Zhang, X., Tian, Y., Wang, W., Xue, J.H., Liao, Q.: Deep learning for single image super-resolution: a brief review. IEEE Trans. Multimedia 21(12), 3106–3121 (2019). https://doi.org/10.1109/TMM.2019.2919431

  22. Zammit-Mangion, A., Cressie, N., Shumack, C.: On statistical approaches to generate level 3 products from satellite remote sensing retrievals. Remote Sens. 10(1), 155 (2018)

    Article  Google Scholar 

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Correspondence to Andrianirina Rakotoharisoa .

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Rakotoharisoa, A., Cenci, S., Arcucci, R. (2024). Evaluating the Impact of Atmospheric CO2 Emissions via Super Resolution of Remote Sensing Data. In: Franco, L., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2024. ICCS 2024. Lecture Notes in Computer Science, vol 14836. Springer, Cham. https://doi.org/10.1007/978-3-031-63775-9_28

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  • DOI: https://doi.org/10.1007/978-3-031-63775-9_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-63774-2

  • Online ISBN: 978-3-031-63775-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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