Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2026, V. 23, No. 2, pp. 343-358
Evaluation of the accuracy of greenhouse gas content measurement in the Earth’s atmosphere by Driada high-resolution orbital spectrometer
A.V. Nazarova 1 , A.A. Fedorova 1 , M.S. Zharikova 1 , A.Yu. Trokhimovskiy 1 , A.S. Patrakeev 1 , O.I. Korablev 1 1 Space Research Institute RAS, Moscow, Russia
Accepted: 27.01.2026
DOI: 10.21046/2070-7401-2026-23-2-343-358
The Driada high-resolution spectrometer, planned for installation on the International Space Station (ISS), is designed for global measurement of greenhouse gases CO2 and CH4 in the near-infrared range from 1.4 to 1.65 microns. CO2 absorption will be recorded in the bands of 1.58 and 1.6 microns, and CH4 in the band of 1.64 microns, which corresponds to the spectral range of a number of recognized space experiments for monitoring greenhouse gas concentrations, GOSAT (Greenhouse Gases Observing Satellite), OCO-2 (Orbiting Carbon Observatory-2), etc. To estimate green-house gas fluxes, high accuracy of their measurement is required. The work is devoted to assessing the accuracy of greenhouse gas content measurements by the main channel of Driada, taking into account the main characteristics of the device in terms of resolution, noise and operating spectral range. To calculate the reflection spectra, a radiation transfer model based on a line-by-line calculation in the near infrared range has been created. To study sensitivity on a global scale and through-out the year, 1,200 atmospheric profiles were prepared based on global atmospheric chemistry fore-casts from the CAMS (Copernicus Atmospheric Monitoring Service) center of ECMWF (European Center for Medium-range Weather Forecast), 100 profiles for each month of the year. The illumination of the surface, which is determined both by the solar zenith angle at the observation point and by the albedo of the Earth’s surface, plays a key role in calculating sensitivity in the near-infrared range. We have adapted a data set of HAMSTER (Hyperspectral Albedo Maps dataset with high Spatial and TEmporal Resolution) into the model. The sensitivity calculation was carried out by solving the inverse problem. The lowest measurement accuracy from 1.65 to 20.32 % for CO2 and from 3.16 to 40.07 % for CH4 is observed above the water surface. The highest accuracy from 0.051 to 0.3 % for CO2 and from 0.17 to 1.28 % for CH4 is observed in the latitude range over the African continent due to the high albedo of the surface throughout the year. In the mid-northern latitudes, the accuracy varies from 0.047 to 0.47 % for CO2 and from 0.093 to 4.21 % for CH4. The results obtained confirm the capabilities of Driada with current characteristics to ensure the necessary accuracy of measurements of both gases over land.
Keywords: atmosphere, greenhouse gases, spectroscopy, remote sensing
Full textReferences:
- Korablev O. I., Kalinnikov Yu. K., Titov A. Yu. et al., The RUSALKA device for measuring the carbon dioxide and methane concentration in the atmosphere from on board the International Space Station, J. Optical Technology, 2011, V. 78, No. 5, pp. 317–327, DOI: 10.1364/JOT.78.000317.
- Trokhimovsky A. Yu., Korablev O. I., Ivanov Yu. S. et al., Infrared channel of the Driada spectrometer for greenhouse gases measurement from space, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2022, V. 19, No. 6, pp. 50–60 (in Russian), DOI: 10.21046/2070-7401-2022-19-6-50-60.
- Anderson G. P., Clough S. A., Kneizys F. X., Chetwynd J. H., Shettle E. P., AFGL atmospheric constituent profiles (0–120 km), Environmental Research Papers, 1986, No. 954, 48 p.
- Bao Z., Zhang X., Yue T. et al., Retrieval and validation of XCO2 from TanSat target mode observations in Beijing, Remote Sensing, 2020, V. 12, No. 18, Article 3063, DOI: 10.3390/rs12183063.
- Basu S., Guerlet S., Butz A. et al., Global CO2 fluxes estimated from GOSAT retrievals of total column CO2, Atmospheric Chemistry and Physics, 2013, V. 13, No. 17, pp. 8695–8717, DOI: 10.5194/acp-13-8695-2013.
- Borbas E. E., Ruston B. C. The RTTOV UWiremis IR land surface emissivity module. Darmstadt: EUMETSAT, 2010, Article NWPSAF-MO-VS-042, 25 p.
- Bucholtz A., Rayleigh-scattering calculations for the terrestrial atmosphere, Applied optics, 1995, V. 34, No. 15, pp. 2765–2773, DOI: 10.1364/AO.34.002765.
- Buchwitz M., Reuter M., Bovensmann H. et al., Carbon Monitoring Satellite (CarbonSat): Assessment of atmospheric CO2 and CH4 retrieval errors by error parameterization, Atmospheric Measurement Techniques, 2013, V. 6, No. 12, pp. 3477–3500, DOI: 10.5194/amt-6-3477-2013.
- Butz A., Hasekamp O. P., Frankenberg C., Aben I., Retrievals of atmospheric CO2 from simulated space-borne measurements of backscattered near-infrared sunlight: accounting for aerosol effects, Applied Optics, 2009, V. 48, No. 18, pp. 3322–3336, DOI: 10.1364/AO.48.003322.
- Chevallier F., Fisher M., Peylin P. et al., Inferring CO2 sources and sinks from satellite observations: Method and application to TOVS data, J. Geophysical Research: Atmospheres, 2005, V. 110, No. D24, Article D24309, DOI: 10.1029/2005JD006390.
- Clough S. A., Shephard M. W., Mlawer E. J. et al., Atmospheric radiative transfer modeling: A summary of the AER codes, J. Quantitative Spectroscopy and Radiative Transfer, 2005, V. 91, No. 2, pp. 233–244, DOI: 10.1016/j.jqsrt.2004.05.058.
- Crisp D., Measuring atmospheric carbon dioxide from space with the Orbiting Carbon Observatory-2 (OCO-2), Proc. SPIE. Earth observing systems XX, 2015, V. 9607, Article 960702, 7 p.
- Dogniaux M., Crevoisier C., Mapping the CO2 total column retrieval performance from shortwave infra-red measurements: synthetic impacts of the spectral resolution, signal-to-noise ratio, and spectral band selection, Atmospheric Measurement Techniques, 2024, V. 17, No. 17, pp. 5373–5396, DOI: 10.5194/ amt-17-5373-2024.
- Emde C., Barlakas V., Cornet C. et al., IPRT polarized radiative transfer model intercomparison proj-ect — Phase A, J. Quantitative Spectroscopy and Radiative Transfer, 2015, V. 164, pp. 8–36, DOI: 10.1016/j. jqsrt.2015.05.007.
- Fedorova A., Marcq E., Luginin M. et al., Variations of water vapor and cloud top altitude in the Venus’ mesosphere from SPICAV/VEx observations, Icarus, 2016, V. 275, pp. 143–162, DOI: 10.1016/j. icarus.2016.04.010.
- Fedorova A. A., Montmessin F., Korablev O. et al., Stormy water on Mars: The distribution and saturation of atmospheric water during the dusty season, Science, 2020, V. 367, No. 6475, pp. 297–300, DOI: 10.1126/ science.aay9522.
- Gordon I. E., Rothman L. S., Hargreaves E. R. et al., The HITRAN2020 molecular spectroscopic data-base, J. Quantitative Spectroscopy and Radiative Transfer, 2022, V. 277, Article 107949, DOI: 10.1016/j. jqsrt.2021.107949.
- Guerri G., Albini G., Casali A. et al., Earth observation analysis and microclimate simulations of mitiga-tion scenarios to support urban planning: The MIRIFICUS project, In: Geographic Approaches to Climate Change and Mitigation: Urban and Rural Perspectives (V. 1), Cham: Springer Nature Switzerland, 2024, pp. 43–56, DOI: 10.1007/978-3-031-92119-3_4.
- Kasuya M., Nakajima M., Hamazaki T., Greenhouse gases observing satellite (GOSAT) program over-view and its development status, Trans. of Japan Soc. for Aeronautical and Space Sciences, Space Technology Japan, 2009, V. 7, No. ists26, pp. To_4_5–To_4_10, DOI: 10.2322/tstj.7.To_4_5.
- Kurucz R. L., Synthetic infrared spectra, Symp. — Intern. Astronomical Union, V. 154, Cambridge University Press, 1994, pp. 523–531, DOI: 10.1017/S0074180900124805.
- Kurucz R. L., New atlases for solar flux, irradiance, central intensity, and limb intensity, Memorie della Società Astronomica Italiana Suppl., 2005, V. 8, Article 189.
- Lu S., Landgraf J., Fu G. et al., Simultaneous retrieval of trace gases, aerosols, and cirrus using RemoTAP — The global orbit ensemble study for the CO2M mission, Frontiers in Remote Sensing, 2022, V. 3, Article 914378, DOI: 10.3389/frsen.2022.914378.
- Meerdink S. K., Hook S. J., Roberts D. A., Abbott E. A., The ECOSTRESS spectral library version 1.0, Remote Sensing of Environment, 2019, V. 230, Article 111196, DOI: 10.1016/j.rse.2019.05.015.
- Menang K. P., Coleman M. D., Gardiner T. D. et al., A high-resolution near-infrared extraterrestrial solar spectrum derived from ground-based Fourier transform spectrometer measurements, J. Geophysical Research: Atmospheres, 2013, V. 118, No. 11, pp. 5319–5331, DOI: 10.1002/jgrd.50425.
- Mlawer E. J., Payne V. H., Moncet J.-L. et al., Development and recent evaluation of the MT_CKD model of continuum absorption, Philosophical Trans. Royal Soc. A: Mathematical, Physical and Engineering Sciences, 2012, V. 370, No. 1968, pp. 2520–2556, DOI: 10.1098/rsta.2011.0295.
- Mlawer E. J., Cady-Pereira K. E., Mascio J., Gordon I. E., The inclusion of the MT_CKD water vapor continuum model in the HITRAN molecular spectroscopic database, J. Quantitative Spectroscopy and Radiative Transfer, 2023, V. 306, Article 108645, DOI: 10.1016/j.jqsrt.2023.108645.
- O’Dell C. W., Connor B., Bösch H. et al., The ACOS CO2 retrieval algorithm — Part 1: Description and validation against synthetic observations, Atmospheric Measurement Techniques, 2012, V. 5, No. 1, pp. 99–121, DOI: 10.5194/amt-5-99-2012.
- Press W. H., Teukolsky S. A., Vetterling W. T., Flannery B. P., Numerical recipes in Fortran 90: The art of scientific computing, 2nd ed., New York: Cambridge University Press, 1996, 578 p.
- Reuter M., Buchwitz M., Schneising O. et al., A fast atmospheric trace gas retrieval for hyperspectral instruments approximating multiple scattering — Part 1: Radiative transfer and a potential OCO-2 XCO2 retrieval setup, Remote Sensing, 2017, V. 9, No. 11, Article 1159, DOI: 10.3390/rs9111159.
- Roccetti G., Bugliaro L., Gödde F. et al., HAMSTER: Hyperspectral Albedo Maps dataset with high Spatial and TEmporal Resolution, Atmospheric Measurement Techniques, 2024, V. 17, No. 20, pp. 6025–6046, DOI: 10.5194/amt-17-6025-2024.
- Rothman L. S., Gordon I. E., Babikov Y. et al., The HITRAN2012 molecular spectroscopic database, J. Quantitative Spectroscopy and Radiative Transfer, 2013, V. 130, pp. 4–50, DOI: 10.1016/j.jqsrt.2013.07.002.
- Rozanov V. V., Dinter T., Rozanov A. V. et al., Radiative transfer modeling through terrestrial atmosphere and ocean accounting for inelastic processes: Software package SCIATRAN, J. Quantitative Spectroscopy and Radiative Transfer, 2017, V. 194, pp. 65–85, DOI: 10.1016/j.jqsrt.2017.03.009.
- Saunders R., Matricardi M., Geer A., Rayer P., Embury O., Merchant C., RTTOV9 science and validation plan, EUMETSAT, 2010, Article NWPSAF-MO-TV-020, 75 p. https://nwp-saf.eumetsat.int/oldsite/deliv-erables/rtm/rttov9_files/rttov9_svr.pdf.
- Taylor T. E., O’Dell C. W., Frankenberg C. et al., Orbiting Carbon Observatory-2 (OCO-2) cloud screening algorithms: validation against collocated MODIS and CALIOP data, Atmospheric Measurement Techniques, 2016, V. 9, No. 3, pp. 973–989, DOI: 10.5194/amt-9-973-2016.
- Trokhimovskiy A., Fedorova A., Korablev O. et al., Mars’ water vapor mapping by the SPICAM IR spectrometer: Five Martian years of observations, Icarus, 2015, V. 251, pp. 50–64, DOI: 10.1016/j. icarus.2014.10.007.
- Turner E., Diverse profile datasets from the ECMWF CAMS 137-level short range forecasts, EUMETSAT, 2025, Article NWPSAF-EC_TR-044, 47 p.
- Vidot J., Borbás É., Land surface VIS/NIR BRDF atlas for RTTOV-11: model and validation against SEVIRI land SAF albedo product, Quarterly J. Royal Meteorological Soc., 2014, V. 140, No. 684, pp. 2186–2196, DOI: 10.1002/qj.2288.
- Wang Q., Yang Z.-D., Bi Y.-M., Spectral parameters and signal-to-noise ratio requirement for TANSAT hyper spectral remote sensor of atmospheric CO2, Proc. SPIE 9259. Remote Sensing of the Atmosphere, Clouds, and Precipitation V, 2014, Article 92591T, 16 p., DOI: 10.1117/12.2067572.
- Wu W., Liu X., Yang Q. et al., All sky single field of view retrieval system for hyperspectral sounding, IGARSS 2019 — 2019 IEEE Intern. Geoscience and Remote Sensing Symp., IEEE, 2019, pp. 7560–7563, DOI: 10.1109/IGARSS.2019.8898307.
- Yoshida Y., Ota Y., Eguchi N. et al., Retrieval algorithm for CO2 and CH4 column abundances from short-wavelength infrared spectral observations by the Greenhouse gases observing satellite, Atmospheric Measurement Techniques, 2011, V. 4, No. 4, pp. 717–734, DOI: 10.5194/amt-4-717-2011.
- Zhou M., Dils B., Wang P. et al., Validation of TANSO-FTS/GOSAT XCO2 and XCH4 glint mode retriev-als using TCCON data from near-ocean sites, Atmospheric Measurement Techniques, 2016, V. 9, No. 3, pp. 1415–1430, DOI: 10.5194/amt-9-1415-2016.