ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2023, Vol. 20, No. 6, pp. 313-325

Development of a methodology for balance calculations of greenhouse gas emissions based on satellite monitoring data using the example of large forest fires

E.V. Pashinov 1 , S.A. Vturin 1 , D.M. Ermakov 1, 2 , I.N. Sadovsky 1 
1 Space Research Institute RAS, Moscow, Russia
2 Kotelnikov Institute of Radioengineering and Electronics RAS, Fryazino Branch, Fryazino, Moscow Region, Russia
Accepted: 25.11.2023
DOI: 10.21046/2070-7401-2023-20-6-313-325
Currently, studies of the balance of carbon-containing gases in the atmosphere over various territories are of great interest. Existing approaches to assessing the balance of gas components of the atmosphere (cadastral, direct measurements at test sites, inverse modeling) have known disadvantages and limitations. Therefore, it is important to develop new techniques based on direct analysis of remote sensing data. The work describes a method of balance calculations that is closed with respect to remote data from satellite IR spectrometers, which ensure the restoration of concentration fields of target gas components. The original version of the methodology was developed in relation to data on precipitable water vapor of the atmosphere for the task of analyzing the regional hydrological regime. This work analyzes the features of the remote sensing data used, which require additional adaptation of the calculation algorithms of the methodology. The methodology was developed and tested using the example of monitoring CO emissions from large forest fires in Siberia. Balance calculations using the proposed method gave a total CO emission of 2.9•109 kg in the time interval from July 10 to August 10, 2022. Independent model estimates, according to the Global Fire Emissions Database, give an upper limit of 3.9•109 kg. The lower limit of the same emission can be estimated at 1.4•109 kg.
Keywords: greenhouse gases, fluxes, balance calculations, remote sensing of the Earth
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References:

  1. Vaganov E. A., Porfir’ev B. N., Shirov A. A. et al., Assessment of the Contribution of Russian Forests to Climate Change Mitigation, Ekonomika regiona, 2021, Vol. 17, No. 4, pp. 1096–1109 (in Russian). https://doi.org/10.17059/ekon.reg.2021-4-4.
  2. Gessen S. M., Vorotnikov A. M., Carbon polygons, a new tool for climate change management in the Russian Federation, Zhurnal sotsiologicheskikh issledovanii, 2021, Vol. 6, No. 2, pp. 22–30 (in Russian), https://naukaru.ru/ru/nauka/article/45155/view.
  3. Ermakov D. M., Pashinov E. V., Kuz’min A. V. et al., The concept of calculating the elements of the regional hydrological balance with the use of satellite radiothermovision, Gidrometeorologiya i ehkologiya, 2023, No. 72, pp. 470–493 (in Russian), DOI: 10.33933/2713-3001-2023-72-470-492.
  4. Kurganova I. N., Lopes D. G. V. O., Ipp S. L. et al., Pilot carbon polygon in Russia: analysis of carbon stocks in soils and vegetation), Pochvy i okruzhayushchaya sreda, 2022, Vol. 5, No. 2, 16 p. (in Russian), DOI: 10.31251/pos.v5i2.169.
  5. Kaehler A., Bradski G., Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library, Sebastopol: O’Reilly Media, Inc., 2016, 1022 p.
  6. Loupian E. A., Proshin A. A., Burtsev M. A. et al., Vega-Science system: design features, main capabilities and usage experience, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2021, Vol. 18, No. 6, pp. 9–31 (in Russian), DOI:10.21046/2070-7401-2021-18-6-9-31.
  7. Romanovskaya A. A., Trunov A. A., Korotkov V. N., Karaban’ R. T., The Problem of Accounting for Carbon Sequestration Ability of Russian Forests in Paris Climatic Agreement, Lesovedenie, 2018, No. 5, pp. 323–334 (in Russian), DOI: 10.1134/S0024114818050066.
  8. Forsyth D. A., Ponce J. Computer vision: A modern approach, Englewood Cliffs: Prentice-Hall Publ., 2002, 693 p.
  9. Bergamaschi P., Danila A., Weiss R. et al., Atmospheric monitoring and inverse modelling for verification of greenhouse gas inventories, Luxembourg: Publications Office of the European Union, 2018, 114 p., DOI: 10.2760/02681.
  10. Ermakov D., Kuzmin A., Pashinov E. et al., Comparison of Vertically Integrated Fluxes of Atmospheric Water Vapor According to Satellite Radiothermovision, Radiosondes, and Reanalysis, Remote Sensing, 2021, Vol. 13, Article 1639, https://doi.org/10.3390/rs13091639.
  11. Inness A., Ades M., Agustí-Panareda A. et al., The CAMS reanalysis of atmospheric composition, Atmospheric Chemistry and Physics, 2019, Vol. 19, pp. 3515–3556, https://doi.org/10.5194/acp-19-3515-2019.
  12. Kaiser J. W., Heil A., Andreae M. O. et al., Biomass burning emissions estimated with a global fire assimilation system based on observed fire radiative power, Biogeosciences, 2012, No. 9, pp. 527–554, https://doi.org/10.5194/bg-9-527-2012.
  13. Kroeger T., Timofte R., Dai D., Van Gool L., Fast optical flow using dense inverse search, European conference on computer vision, Cham: Springer, 2016, pp. 471–488, https://doi.org/10.1007/978-3-319-46493-0_29.
  14. Telea A., An Image Inpainting Technique Based on the Fast Marching Method, J. Graphics Tools, 2004, Vol. 9, No. 1, pp. 25-36, https://doi.org/10.1080/10867651.2004.10487596.
  15. van Wees D., van der Werf G. R., Randerson J. T. et al., Global biomass burning fuel consumption and emissions at 500 m spatial resolution based on the Global Fire Emissions Database (GFED), Geoscientific Model Development, 2022, No. 15, pp. 8411–8437, https://doi.org/10.5194/gmd-15-8411-2022.