Журнал

About Editorial Board Information for authors and reviewers Publication ethics Contacts User Account: send / review a manuscript
Archive
Volume 23, 2026
Number 1 Number 2 Number 3 Number 4
Volume 22, 2025
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 21, 2024
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 20, 2023
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 19, 2022
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 18, 2021
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 17, 2020
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 16, 2019
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 15, 2018
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 14, 2017
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 13, 2016
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 12, 2015
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 11, 2014
Number 1 Number 2 Number 3 Number 4
Volume 10, 2013
Number 1 Number 2 Number 3 Number 4
Volume 9, 2012
Number 1 Number 2 Number 3 Number 4 Number 5
Volume 8, 2011
Number 1 Number 2 Number 3 Number 4
Volume 7, 2010
Number 1 Number 2 Number 3 Number 4
Issue 6, 2009
Volume 1 Volume 2
Issue 5, 2008
Volume 1 Volume 2
Issue 4, 2007
Volume 1 Volume 2
Issue 3, 2006
Volume 1 Volume 2
Issue 2, 2005
Volume 1 Volume 2
Issue 1, 2004
Volume 1
Search
Find:
русский
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, 2026, V. 23, No. 4, pp. 137-151

Forecast of the yield of spring grain crops for the south of Western Siberia based on fluorescent radiation flows

A.E. Karamzina 1 , E.Yu. Mordvin 1 , A.A. Lagutin 1 
1 Altai State University, Barnaul, Russia
Accepted: 30.06.2026
DOI: 10.21046/2070-7401-2026-23-4-137-151
The paper discusses a model linking the yield of spring cereals and grain legumes in the southern region of Western Siberia with fluxes of fluorescent radiation emitted in the 600–800 nm range, which represent a by-product of the light phase of photosynthesis. The primary source of information on fluorescent radiation fluxes is the TROPOMI (TROPOspheric Monitoring Instrument) spectrometer mounted on the Sentinel-5 Precursor satellite. The developed methodology combines fluorescent radiation data with information on cropland distribution obtained from MODIS (Moderate Resolution Imaging Spectroradiometer) satellite observations and official yield data from Rosstat (Russian Federal State Statistics Service). The key hypothesis is based on a linear relationship between regionally averaged fluorescent radiation levels and gross primary production, which governs the biomass accumulation process of cereals and grain legumes. Analysis of TROPOMI data for the period of 2020–2024 revealed a robust correlation between peak fluorescence values and yield indicators. It was found that incorporating a loss function into the model, based on the Selyaninov hydrothermal coefficient P for the August – September period, allowed for better alignment between the fluorescence maximum and yield data from Rosstat. The evaluation of P was performed using results from the SEAS5 (Seasonal Forecasting System 5) subseasonal climate model. For yield forecasting in the Novosibirsk, Omsk, and Kemerovo regions, as well as Altai Krai, the coefficient of determination R2 for the case without accounting for losses was 0.17, 0.79, 0.53, and 0.90, respectively, while for the model with losses it was 0.89, 0.88, 0.86, and 0.97, respectively. The research results confirm the potential for using fluorescent radiation data for monitoring and forecasting spring crop yields in regions of risky farming.
Keywords: SIF, GPP, chlorophyll fluorescence, photosynthesis, yield, Sentinel-5 Precursor, TROPOMI, remote sensing, SEAS5, south of Western Siberia
Full text

References:

  1. Amirova T. N., Impact of moisture loss on crop yields during irrigation using the pivot center irrigation method, Melioratsiya i vodnoe khozyaistvo, 2023, No. 2, pp. 4–7 (in Russian), DOI: 10.32962/0235-2524-2023-2-4-7.
  2. Eroshenko F. V., Bartalev S. A., Storchak I. G., Plotnikov D. E., The possibility of winter wheat yield estimation based on vegetation index of photosynthetic potential derived from remote sensing data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2016, V. 13, No. 4, pp. 99–112 (in Russian), DOI: 10.21046/2070-7401-2016-13-23-99-112.
  3. Kelchevskaya L. S., Metody obrabotki nablyudenii v agroklimatologii: metodicheskoe posobie (Methods of processing observations in agroclimatology: Methodological manual), Leningrad: Gidrometeorologicheskoe izd., 1971, 215 p. (in Russian).
  4. Lupyan E. A., Bartalev S. A., Krasheninnikova Yu. S. et al., Abnormal development of spring crops in European Russia in 2017, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2017, V. 14, No. 3, pp. 324–329 (in Russian), DOI: 10.21046/2070-7401-2017-14-3-324-329.
  5. Simonenko E. I., Planning the yield of winter wheat taking into account the influence of natural and climatic factors, Aktualnaya nauka, 2017, No. 2, pp. 21–25 (in Russian).
  6. Spiridonov Yu. Ya., Budynkov N. I., Azizov Z. M. et al., The impact of integrated weed control measures on the yield and quality of winter wheat, Vavilovskie chteniya — 2019: Mezhdunarodnaya nauchno-prakticheskaya konferentsiya, posvyashchennaya 132-i godovshchine so dnya rozhdeniya akademika N. I. Vavilova (Vavilov Readings  — 2019: Intern. Scientific and Practical Conf. dedicated to the 132nd Anniversary of the Birth of Academician N. I. Vavilov), Saratov: OOO “Amirit”, 2019, pp. 228–230 (in Russian).
  7. Fedulov Yu. P., Podushkin Yu. V., Fotosintez i dykhanie rastenii: uchebnoe posobie dlya bakalavrov, izuchayushchikh distsiplinu “Fiziologiya i biokhimiya rastenii” (Photosynthesis and respiration of plants: A tutorial for bachelors studying the discipline “Plant Physiology and Biochemistry”), Krasnodar: Kuban State Agrarian University named after I. T. Trubilin, 2019, 101 p. (in Russian).
  8. Stirbet A., Riznichenko G. Yu., Rubin A. B., Govindjee Z., Modeling chlorophyll a fluorescence transient: Relation to photosynthesis, Biochemistry (Moscow), 2014, V. 79, No. 4, pp. 291–323, DOI: 10.1134/S0006297914040014.
  9. Ayudhya T. I., Posey F. T., Tyus J. C., Dingra N. N., Using a microscale approach to rapidly separate and characterize three photosynthetic pigment species from fern, J. Chemical Education, 2015, V. 92, Iss. 5, pp. 920–923, DOI: 10.1021/ed500344c.
  10. Berger M., Moreno J., Johannessen J. A. et al., ESA’s sentinel missions in support of Earth system science, Remote Sensing of Environment, 2012, V. 120, pp. 84–90, DOI: 10.1016/j.rse.2011.07.023.
  11. Cannon A. J., Sobie S. R., Murdock T. Q., Bias correction of GCM precipitation by quantile mapping: How well do methods preserve changes in quantiles and extremes?, J. Climate, 2015, V. 28, pp. 6938–6959, DOI: 10.1175/JCLI-D-14-00754.1.
  12. Elber G., Lee I.-K., Kim M.-S., Comparing offset curve approximation methods, IEEE Computer Graphics and Applications, 1997, V. 17, No. 3, pp. 62–71, DOI: 10.1109/38.586019.
  13. Fang J., Li X., Xiao J. et al., Vegetation photosynthetic phenology dataset in northern terrestrial ecosystems, Scientific Data, 2023, V. 10, Article 300, DOI: 10.1038/s41597-023-02224-w.
  14. Friedl M., Sulla-Menashe D., MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500m SIN Grid V061, NASA Land Processes Distributed Active Archive Center, 2022, DOI: 10.5067/MODIS/MCD12Q1.061.
  15. Gao S., Zhong R., Yan K. et al., Evaluating the saturation effect of vegetation indices in forests using 3D radiative transfer simulations and satellite observations, Remote Sensing of Environment, 2023, V. 295, Article 113665, DOI: 10.1016/j.rse.2023.113665.
  16. Guanter L., Bacour C., Schneider A. et al., The TROPOSIF global sun-induced fluorescence dataset from the Sentinel-5P TROPOMI mission, Earth System Science Data, 2021, V. 13, Iss. 11, pp. 5423–5440, DOI: 10.5194/essd-13-5423-2021.
  17. Johnson S. J., Stockdale T. N., Ferranti L. et al., SEAS5: the new ECMWF seasonal forecast system, Geoscientific Model Development, 2019, V. 12, Iss. 3, pp. 1087–1117, DOI: 10.5194/gmd-12-1087-2019.
  18. Li X., Xiao J., TROPOMI observations allow for robust exploration of the relationship between solar-induced chlorophyll fluorescence and terrestrial gross primary production, Remote Sensing of Environment, 2022, V. 268, Article 112748, DOI: 10.1016/j.rse.2021.112748.
  19. Liu Z., Zhao F., Liu X. et al., Direct estimation of photosynthetic CO2 assimilation from solar-induced chlorophyll fluorescence (SIF), Remote Sensing of Environment, 2022, V. 271, Article 112893, DOI: 10.1016/j.rse.2022.112893.
  20. Lungu O. N., Chabala L. M., Shepande C., Satellite-based crop monitoring and yield estimation — a review, J. Agricultural Science, 2020, V.13, Iss. 1, pp. 180–194, DOI: 10.5539/jas.v13n1p180.
  21. Magney T. S., Bowlingc D. R., Logan B. A., Mechanistic evidence for tracking the seasonality of photosynthesis with solar-induced fluorescence, PNAS, 2019, V. 116, No. 24, pp. 11640–11645, DOI: 10.1073/pnas.1900278116.
  22. Marshall M., Tu K., Brown J., Optimizing a remote sensing production efficiency model for macro-scale GPP and yield estimation in agroecosystems, Remote Sensing of Environment, 2018, V. 217, pp. 258–271, DOI: 10.1016/j.rse.2018.08.001.
  23. Mordvin E. Yu., Pochemin N. M., Volkov N. V. et al., Changes in moisture supply in the steppe zone of the southern part of Western Siberia for the period 1980–2050 according to scenario forecasts based on global CMIP6 models, Arid Ecosystems, 2024, V. 14, pp. 259–268, DOI: 10.1134/S2079096124700203.
  24. Muñoz-Sabater J., Dutra E., Agustí-Panareda A. et al., ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth System Science Data, 2021, V. 13, Iss. 9, pp. 4349–4383, DOI: 10.5194/essd-13-4349-2021.
  25. Peng B., Guan K., Pan M., Li Y., Benefits of seasonal climate prediction and satellite data for forecasting U. S. maize yield, Geophysical Research Letters, 2018, V. 45, pp. 9662–9671, DOI: 10.1029/2018GL079291.
  26. Proctor J., Zeppetello L. V., Chan D., Huybers P., Climate change increases the interannual variance of summer crop yields globally through changes in temperature and water supply, Science Advances, 2025, V. 11, Iss. 36, Article eady3575, DOI: 10.1126/sciadv.ady3575.
  27. Reeves M. C., Zhao M., Running S. W., Usefulness and limits of MODIS GPP for estimating wheat yield, Intern. J. Remote Sensing, 2005, V. 26, pp. 1403–1421, DOI:10.1080/01431160512331326567.
  28. Rouse J. W., Jr., Haas R. H., Scheel J. A., Deering D. W., Monitoring vegetation systems in the great plains with ERTS, Proc. 3 rd Earth Resource Technology Satellite-1 (ERTS-1) Symp., NASA SP-351, 1974, V. 1, pp. 309–317.
  29. Tucker C. J., Red and photographic infrared linear combinations for monitoring vegetation, Remote Sensing of Environment, 1979, V. 8, pp. 127–150, DOI: 10.1016/0034-4257(79)90013-0.
  30. Tucker C. J., Fung I. Y., Keeling C. D., Gammon R. H., Relationship between atmospheric CO2 variations and a satellite-derived vegetation index, Nature, 1986, V. 319, pp. 195–199, DOI: 10.1038/319195a0.
  31. Veefkind J. P., Aben I., McMullan K. 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 Sensing of Environment, 2012, V. 120, pp. 70–83, DOI: 10.1016/j.rse.2011.09.027.
  32. Xiao J., Fisher J. B., Hashimoto H. et al., Emerging satellite observations for diurnal cycling of ecosystem processes, Nature Plants, 2021, No. 7, pp. 877–887, DOI: 10.1038/s41477-021-00952-8.
  33. Zhou Z., Ding Y., Liu S. et al., Estimating the applicability of NDVI and SIF to gross primary productivity and grain-yield monitoring in China, Remote Sensing, 2022, No. 14, Article 3237, DOI: 10.3390/rs14133237.