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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
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
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)
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
Climate TRACE coalition: Climate TRACE - Tracking Real-time Atmospheric Carbon Emissions. Climate TRACE Emissions Inventory (2022). https://climatetrace.org/
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
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)
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)
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)
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)
Jacobson, A.R., Schuldt, K.N., Tans, P.: CarbonTracker CT2022. NOAA Global Monitoring Laboratory (2023). https://doi.org/10.25925/Z1GJ-3254
Laughner, J.L., et al.: The total carbon column observing network’s GGG2020 data version. Earth Syst. Sci. Data Discuss. 2023, 1–86 (2023)
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)
Li, S., et al.: PyTorch distributed: experiences on accelerating data parallel training. arXiv preprint arXiv:2006.15704 (2020)
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)
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)
Tsai, R.Y., Huang, T.S.: Multiframe image restoration and registration, vol. 1, pp. 317–339 (1984)
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)
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
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)
Weir, B., Ott, L.: OCO-2 Science Team: OCO-2 GEOS level 3 daily, 0.5 \(\times \) 0.625 assimilated CO2 v10r (2021)
Wunch, D., et al.: The total carbon column observing network. Philos. Trans. R. Soc. A: Math. Phys. Eng. Sci. 369(1943), 2087–2112 (2011)
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
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-031-63775-9_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-63774-2
Online ISBN: 978-3-031-63775-9
eBook Packages: Computer ScienceComputer Science (R0)