Vol. 36, issue 12, article # 5

Rozanov A. P., Zаdvornykh I. V., Gribanov K. G., Zakharov V. I. Estimates of CO2 flux into the forest ecosystem based on the results of ground-based hyperspectral sounding of the atmosphere and an artificial neural network model. // Optika Atmosfery i Okeana. 2023. V. 36. No. 12. P. 991–997. DOI: 10.15372/AOO20231205 [in Russian].
Copy the reference to clipboard


The results of hyperspectral sounding of the atmosphere at the Ural Atmospheric Station in Kourovka from 2012–2022 are presented. It is shown that the average rate of CO2 growth in the atmosphere of this region is about 2.5 ppm per year. The amount of carbon dioxide absorbed from the atmosphere by the forest ecosystem per unit area during the growing season (April–September) in the vicinity of the carbon landfill in Kourovka is estimated using two independent methods. One method is based on the data on the CO2 total column obtained from sounding the atmosphere with a ground-based high-resolution infrared Fourier spectrometer. The second method is based on the use of an artificial neural network with data from spectral channels of the MODIS satellite sensor as injnit. The results obtained by both methods demonstrate good agreement. The estimates made show that the amount of CO2 absorbed from the atmosphere by the forest ecosystem in the vicinity of the carbon landfill site during the growing season of 2022 is about 1.5 t/ha (the first method) and about 1.3 t/ha (the second method).


atmosphere, carbon dioxide, hyperspectral sounding, artificial neural networks, MODIS


1. Summary for Policymakers // Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press., 2021. P. 4–5.
2. Pan Y., Birdsey R.A., Fang J., Houghton R., Kauppi P.E., Kurz W.A., Phillips O.L., Shvidenko A., Lewis S.L., Canadell J.G., Ciais P., Jackson R.B., Pacala S.W., McGuire A.D., Piao S., Rautiainen A., Sitch S., Hayes D. A large and persistent carbon sink in the world's forests // Science. 2011. V. 333. P. 988–993.
3. Strategiya sotsial'no-ekonomicheskogo razvitiya RF s nizkim urovnem vybrosov parnikovyh gazov do 2050 year. Utverzhdena rasporyazheniem Pravitel'stva RF N 3052р ot 29 october 2021 year.
4. Zamolodchikov D.G. Sistemy otsenki i prognoza zapasov ugleroda v lesnyh ekosistemah // Ustojchivoe lesopol'zovanie. 2011. V. 29, N 4. P. 15–22.
5. Romanovskaya A.A., Trunov A.A., Korotkov V.N., Karaban' R.T. Problema ucheta pogloshchayushchej sposobnosti lesov Rossii v Parizhskom soglashenii // Lesovedenie. 2018. N 5. P. 323–334.
6. Gribanov K.G., Zakharov V.I., Beresnev S.A., Rokotyan N.V., Poddubny V.A., Imasu R., Chistyakov P.A., Skorik G.G., Vasin V.V. Zondirovanie HDO/H2O v atmosfere Urala metodom nazemnyh izmerenij IK-spektrov solnechnogo izlucheniya s vysokim spektral'nym razresheniem // Optika atmosf. i okeana. 2011. V. 24, N 2. P. 124–127; Gribanov K.G., Zakharov V.I., Beresnev S.A., Rokotyan N.V., Poddubny V.A., Imasu R., Chistyakov P.A., Skorik G.G., Vasin V.V. Sensing HDO/H2O in the Ural’s atmosphere using ground-based measurements of IR solar radiation with a high spectral resolution // Atmos. Ocean. Opt. 2011. V. 24, N 4. Р. 369–372.
7. Rokotyan N.V., Imasu R., Zakharov V.I., Gribanov K.G., Khamatnurova M.Yu. Amplituda sezonnogo tsikla СО2 v atmosfere Ural'skogo regiona po rezul'tatam nazemnogo i sputnikovogo IK-zondirovaniya // Optika atmosf. i okeana. 2014. V. 27, N 9. P. 819–825; Rokotyan N.V., Imasu R., Zakharov V.I., Gribanov K.G., Khamatnurova M.Yu. The amplitude of the CO2 seasonal cycle in the atmosphere of the Ural Region retrieved from ground-based and satellite near IR measurements // Atmos. Ocean. Opt. 2015. V. 28, N 1. P. 49–55.
8. Chesnokova T.Yu., Makarova M.V., Chentsov A.V., Voronina Yu.V., Zakharov V.I., Rokotyan N.V., Langerock B. Opredelenie soderzhaniya monooksida ugleroda v atmosfere iz atmosfernyh spektrov vysokogo razresheniya // Optika atmosf. i okeana. 2019. V. 32, N 4. P. 257–265; Chesnokova T.Yu., Makarova M.V., Chentsov A.V., Voronina Yu.V., Zakharov V.I., Rokotyan N.V., Langerock B. Retrieval of carbon monoxide total column in the atmosphere from high resolution atmospheric spectra // Atmos. Ocean. Opt. 2019. V. 32, N 4. P. 378–386. DOI: 10.1134/ S1024856019040031.
9. Chesnokova T.Yu., Makarova M.V., Chentsov A.V., Kostsov V.S., Poberovskii A.V., Zakharov V.I., Rokotyan N.V. Estimation of the impact of differences in the CH4 absorption line parameters on the accuracy of methane atmospheric total column retrievals from ground-based FTIR spectra // J. Quant. Spectrosc. Radiat. Transfer. 2020. V. 254. P. 107187. DOI: 10.1016/j.jqsrt.2020.107187.
10. Wunch D., Toon G.C., Blavier J.F.L., Washenfelder R.A., Notholt J., Connor B.J., Griffith D.W.T., Sherlock V., Wennberg P.O. The total carbon column observing network // Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 2011. V. 369. P. 2087–2112.
11. Wunch D., Toon G.C., Wennberg P.O., Wofsy S.C., Stephens B.B., Fischer M.L., Uchino O., Abshire J.B., Bernath P., Biraud S.C., Blavier J.-F.L., Boone C., Bowman K.P., Browell E.V., Campos T., Connor B.J., Daube B.C., Deutscher N.M., Diao M., Elkins J.W., Gerbig C., Gottlieb E., Griffith D.W.T., Hurst D.F., Jimenez R., Keppel-Aleks G., Kort E.A., Macatangay R., Machida T., Matsueda H., Moore F., Morino I., Park S., Robinson J., Roehl C.M., Sawa Y., Sherlock V., Sweeney C., Tanaka T., Zondlo M.A. Calibration of the total carbon column observing Network using aircraft profile data // Atmos. Meas. Technol. 2010. V. 3, N 5. P. 1351–1362.
12. Feldman A.F., Zhang Z., Yoshida Y., Chatterjee A., Poulter B. Using Orbiting Carbon Observatory-2 (OCO-2) column CO2 retrievals to rapidly detect and estimate biospheric surface carbon flux anomalies // Atmos. Chem. Phys. 2023. V. 23. P. 1545–1563.
13. Alemohammad S.H., Fang B., Konings A.G., Green J.K., Kolassa J., Prigent C., Aires F., Gonzalez Miralles D., Gentine P. Water, Energy, and Carbon with Artificial Neural Networks (WECANN): A statistically based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence // Biogeosci. 2017. V. 14, N 18. P. 4101–4124.
14. Dou X., Yang Y. Comprehensive evaluation of machine learning techniques for estimating the responses of carbon fluxes to climatic forces in different terrestrial ecosystems // Atmosphere. 2018. V. 9, N 3. P. 83.
15. Dou X., Yang Y., Luo J. Estimating forest carbon fluxes using machine learning techniques based on eddy covariance measurements // Sustainability. 2018. V. 10, N 1. P. 203.
16. Zeng J., Matsunaga T., Tan Zh.-H., Saigusa N., Shirai T., Tang Y., Peng Sh., Fukuda Y. Global terrestrial carbon fluxes of 1999–2019 estimated by upscaling eddy covariance data with a random forest // Scientific Data. 2020. V. 7, N 1. P. 1–11.
17. Haykin S. Neural networks: A comprehensive foundation.New Jersey: Prentice Hall PTR, 1998.
18. Paszke A., Gross S., Massa F., Lerer A., Bradbury J., Chanan G., Killeen T., Lin Z., Gimelshein N., Antiga L., Desmaison A., Köpf A., Yang E., DeVito Z., Raison M., Tejani A., Chilamkurthy S., Steiner B., Fang L., Bai J., Chintala S. Pytorch: An imperative style, high-performance deep learning library // Advances in Neural Information Processing Systems 32 (Neur/ IPS 2019). Vancouver, Canada. 2019. V. 1–20.
19. Rozanov A.P. Svidetel'stvo o gosudarstvennoj registratsii programmy dlya EVM N 2023682424 North Flux. Data registratsii v Reestre programm dlya EVM 25 october 2023 year.
20. Gardner M.W., Dorling S.R. Artificial neural networks (the multilayer perceptron) – a review of applications in the atmospheric sciences // Atmos. Environ. 1998. V. 32, N 14–15. P. 2627–2636.
21. Hornik K., Stinchcombe M., White H. Multilayer feedforward network are universal approximators // Neural Networks. 1989. N 2. P. 359–366.
22. He K., Zhang X., Ren S., Sun J. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification // Proc. IEEE. 2015. P. 1026–1034.
23. Schmidt-Hieber Johannes. Nonparametric regression using deep neural networks with ReLU activation function. Annals of Statistics. 2017. 48. DOI: 10.1214/19-AOS1875.
24. Baldocchi D., Falge E., Gu L., Olson R, Hollinger D., Running S., Anthoni P., Bernhofer C., Davis K., Evans R., Fuentes J., Goldstein A., Katul G., Law B., Lee X., Malhi Y., Meyers T., Munger W., Oechel W., Paw K.T., Pilegaard K., Schmid H., Valentini R., Verma S., Vesala T., Wilson K., Wofsy S. FLUXNET: A new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities // Bul. Am. Meteorol. Soc. 2001. V. 82, N 11. P. 2415–2434.
25. Vermote E. MODIS/Terra Surface Reflectance Daily L3 Global 0.05Deg CMG V061. NASA EOSDIS Land Processes DAAC. 2021. DOI: 10.5067/MODIS/ MOD09CMG.061 (last access: 7.10.2023).
26. Friedl M., Sulla-Menashe D. MCD12C1 MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 0.05Deg CMG V006. NASA EOSDIS Land Processes DAAC. 2015. DOI: 10.5067/MODIS/MCD12C1.006 (last access: 7.10.2023).