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. 27-48

Development of the precision farming concept for winter wheat crops: from multispectral satellite data to hyperspectral standards

A.I. Novikov 1 , O.I. Sokolova 2 , T.P. Novikova 3 
1 Agrophysical Research Institute, Saint Petersburg, Россия
2 Rostov State Transport University, Rostov-on-Don, Russia
3 Saint Petersburg State Forest Technical University, Saint Petersburg, Russia
Accepted: 25.05.2026
DOI: 10.21046/2070-7401-2026-23-4-27-48
Improving the efficiency of managing the production process of winter wheat is a key task for ensuring food security and sustainable agricultural development. The article analyzes and develops the concept of an integrated management system combining Earth remote sensing data, climatic indicators, soil characteristics, flexible algorithms for estimating state parameters, and advanced machine learning methods for monitoring the growth and predicting the yield of winter wheat. On the basis of analysis of modern research, including IGWO-CNN (Improved Gray Wolf Optimization — Convolutional Neural Network), LSTM (Long Short-Term Memory), RF (Random Forest), SVM (Support Vector Machine), WOFOST (WOrld FOod STudies) and other models, an integrated approach is considered that allows for accurate and timely yield forecasting at the regional level. Special attention is paid to the problem of integrating hyperspectral data as a reference for calibration and verification of satellite models. A multi-perspective analysis of the effectiveness of various sensors and methods is presented, including analysis at the sensor level, modeling methodologies, predictors, and problem areas, which made it possible to identify the most effective approaches for integration with hyperspectral standards. An improvement of the algorithm for estimating the state of an agrocenosis based on the Kalman filter for an extended state vector, including parameters of biomass and the soil environment, is proposed. The results show that the integration of multispectral data (especially Sentinel-2 with red-edge channels) and radar data (Sentinel-1) with hyperspectral standards and interpretable deep learning models significantly increases the reliability of forecasts and will facilitate informed decision-making in agricultural production.
Keywords: precision farming, winter wheat, remote sensing, hyperspectral data, machine learning, state estimation, production process, MODIS, LSTM, IGWO-CNN, Sentinel-2, data integration, Kalman filter
Full text

References:

  1. Galitsky V. V., Komarov A. S., On modeling plant growth, Izvestiya Akademii Nauk SSSR. Ser. biologicheskaya, 1979, No. 5, pp. 714–723 (in Russian).
  2. Mikhailenko I. M., Upravlenie sistemami tochnogo zemledeliya (Management of precision farming systems), Saint Petersburg: Saint Petersburg University Press, 2005, 234 p. (in Russian).
  3. Mikhailenko I. M., Timoshin V. N., Estimation of the parameters of the state of biomass of root crops using remote sensing data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2025, V. 22, No. 3, pp. 161–170 (in Russian), DOI: 10.21046/2070-7401-2025-22-3-161-170.
  4. Novikova T. P., Evdokimova S. A., Novikov A. I., R&D of the pert basic model for project planning, Modeling of systems and processes, 2021, V. 14, No. 4, pp. 75–81 (in Russian), DOI: 10.12737/2219-0767-2021-14-4-75-81.
  5. Ali A., Hussain T., Tantashutikun N. et al., Application of smart techniques, internet of things and data mining for resource use efficient and sustainable crop production, Agriculture, 2023, V. 13, No. 2, Article 397, DOI: 10.3390/agriculture13020397.
  6. Bernardes R. C., De Medeiros A., da Silva L. et al., Deep-learning approach for Fusarium head blight detection in wheat seeds using low-cost imaging technology, Agriculture, 2022, V. 12, No. 11, Article 1801, DOI: 10.3390/agriculture12111801.
  7. Bolton D. K., Friedl M. A., Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics, Agricultural and Forest Meteorology, 2013, V. 173, pp. 74–84, DOI: 10.1016/j.agrformet.2013.01.007.
  8. Cai Y., Guan K., Lobell D. et al., Integrating satellite and climate data to predict wheat yield in Australia using machine learning approaches, Agricultural and Forest Meteorology, 2019, V. 274, pp. 144–159, DOI: 10.1016/j.agrformet.2019.03.010.
  9. Chahbi-Bellakanji A., Shil E., Khlif M. et al., Early cereal yield prediction using machine learning and Sentinel-1 and Sentinel-2 satellite data, European J. Remote Sensing, 2025, V. 58, No. 1, Article 2558614, DOI: 10.1080/22797254.2025.2558614.
  10. Chen P., Li Y., Liu X. et al., Improving yield prediction based on spatio-temporal deep learning approaches for winter wheat: A case study in Jiangsu Province, China, Computers and Electronics in Agriculture, 2023, V. 213, Article 108201, DOI: 10.1016/j.compag.2023.108201.
  11. Cui X., Guan Z., Farnsworth D., Advancing specialty crop management: A review of recent developments in robotics, remote sensing, and machine learning systems, Intern. Food and Agribusiness Management Review, 2025, V. 28, No. 2, pp. 423–440, DOI: 10.22434/IFAMR1104.
  12. Delécolle R., Maas S. J., Guérif M., Baret F., Remote sensing and crop production models: present trends, ISPRS J. Photogrammetry and Remote Sensing, 1992, V. 47, pp. 145–161, DOI: 10.1016/0924-2716(92)90030-D.
  13. Dhaliwal D. S., Williams M. M., II, Sweet corn yield prediction using machine learning models and field-level data, Precision Agriculture, 2024, V. 25, pp. 51–64, DOI: 10.1007/s11119-023-10057-1.
  14. Elbeltagi A., Srivastava A., Cao X. et al., An interpretable machine learning approach based on SHAP, Sobol and LIME values for precise estimation of daily soybean crop coefficients, Scientific Reports, 2025, V. 15, Article 36594, DOI: 10.1038/s41598-025-20386-y.
  15. Filippi P., Han S. Y., Bishop T. F. A., On crop yield modelling, predicting, and forecasting and addressing the common issues in published studies, Precision Agriculture, 2024, V. 26, Article 8, DOI: 10.1007/s11119-024-10212-2.
  16. Fu H., Lu J., Li J. et al., Winter wheat yield prediction using satellite remote sensing data and deep learning models, Agronomy, 2025, V. 15, No. 1, Article 205, DOI: 10.3390/agronomy15010205.
  17. Gavasso-Rita Y. L., Papalexiou S. M., Li Y. et al., Crop models and their use in assessing crop production and food security: A review, Food and Energy Security, 2024, V. 13, No. 1, Article e503, DOI: 10.1002/fes3.503.
  18. Guanter L., Zhang Y., Jung M. et al., Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence, PNAS, 2014, V. 111, No. 14, pp. E1327–E1333, DOI: 10.1073/pnas.1320008111.
  19. Guérif M., Duke C. L., Adjustment procedures of a crop model to the site-specific characteristics of soil and crop using remote sensing data assimilation, Agriculture, Ecosystems and Environment, 2000, V. 81, pp. 57–69, DOI: 10.1016/S0167-8809(00)00168-7.
  20. Guo J., Lu W., Zhang G. et al., Incorporating remote sensing data in crop model to monitor crop growth and predict yield in regional area, Proc. SPIE, 2006, V. 6411, Article 64111C, DOI: 10.1117/12.692756.
  21. Han J., Zhang Z., Cao J. et al., Prediction of winter wheat yield based on multi-source data and machine learning in China, Remote Sensing, 2020, V. 12, No. 2, Article 236, DOI: 10.3390/rs12020236.
  22. Huete A., Didan K., Miura T. et al., Overview of the radiometric and biophysical performance of the MODIS vegetation indices, Remote Sensing of Environment, 2002, V. 83, pp. 195–213, DOI: 10.1016/S0034-4257(02)00096-2.
  23. Jennewein J. S., Lamb B. T., Hively W. D. et al., Integration of satellite-based optical and synthetic aperture radar imagery to estimate winter cover crop performance in cereal grasses, Remote Sensing, 2022, V. 14, No. 9, Article 2077, DOI: 10.3390/rs14092077.
  24. Jiang J., Shu Y., Wang J., Long M., Transferability in deep learning: A survey, https://arxiv.org/, arXiv:2201.05867, 2022, 64 p., https://arxiv.org/abs/2201.05867.
  25. Jin X., Li Z., Feng H. et al., Estimation of maize yield by assimilating biomass and canopy cover derived from hyperspectral data into the AquaCrop model, Agricultural Water Management, 2020, V. 227, Article 105846, DOI: 10.1016/j.agwat.2019.105846.
  26. Jones J. W., Hoogenboom G., Porter C. H. et al., The DSSAT cropping system model, European J. Agronomy, 2003, V. 18, pp. 235–265, DOI: 10.1016/S1161-0301(02)00107-7.
  27. Khaki S., Wang L., Crop yield prediction using deep neural networks, Frontiers in Plant Science, 2019, V. 10, Article 621, 10 p., DOI: 10.3389/fpls.2019.00621.
  28. Lee W. S., Alchanatis V., Yang C. et al., Sensing technologies for precision specialty crop production, Computers and Electronics in Agriculture, 2010, V. 74, No. 1, pp. 2–33, DOI: 10.1016/j.compag.2010.08.005.
  29. Liu L., Cao R., Chen J. et al., Detecting crop phenology from vegetation index time-series data by improved shape model fitting in each phenological stage, Remote Sensing of Environment, 2022, V. 277, Article 113060, DOI: 10.1016/j.rse.2022.113060.
  30. Liu F., Jiang X., Wu Z. et al., Attention mechanism-combined LSTM for grain yield prediction in China using multi-source satellite imagery, Sustainability, 2023, V. 15, No. 12, Article 9210, DOI: 10.3390/su15129210.
  31. Liu X., Yang H., Ata-Ul-Karim S. T. et al., Screening drought-resistant and water-saving winter wheat varieties by predicting yields with multi-source UAV remote sensing data, Computers and Electronics in Agriculture, 2025, V. 234, Article 110213, DOI: 10.1016/j.compag.2025.110213.
  32. Lobell D. B., Burke M. B., On the use of statistical models to predict crop yield responses to climate change, Agricultural and Forest Meteorology, 2010, V. 150, pp. 1443–1452, DOI: 10.1016/j.agrformet.2010.07.008.
  33. Lu B., Dao P. D., Liu J. et al., Recent advances of hyperspectral imaging technology and applications in agriculture, Remote Sensing, 2020, V. 12, No. 16, Article 2659, DOI: 10.3390/rs12162659.
  34. Lyu Y., Wang P., Bai X. et al., Machine learning techniques and interpretability for maize yield estimation using time-series images of MODIS and multi-source data, Computers and Electronics in Agriculture, 2024, V. 222, Article 109063, DOI: 10.1016/j.compag.2024.109063.
  35. Ma X., Zhu X., Xie Q. et al., Monitoring nature’s calendar from space: Emerging topics in land surface phenology and associated opportunities for science applications, Global Change Biology, 2022, V. 28, No. 24, pp. 7186–7204, DOI: 10.1111/gcb.16436.
  36. Ma Y., Liang S. Z., Myers D. B. et al., Subfield-level crop yield mapping without ground truth data: A scale transfer framework, Remote Sensing of Environment, 2024, V. 315, Article 114427, DOI: 10.1016/j.rse.2024.114427.
  37. Manin A. A., Sokolov S. V., Novikov A. I. et al., Kalman filter adaptation to disturbances of the observer’s parameters, Inventions, 2021, V. 6, No. 4, Article 80, DOI: 10.3390/inventions6040080.
  38. Manin A. A., Sukhanov A. V., Sokolov S. V. et al., Kalman filter adaptive to constant perturbations of the observable object parameters, Proc. 5 th Intern. Scientific Conf. “Intelligent Information Technologies for Industry” (IITI’21), Lecture Notes in Networks and Systems, 2022, V. 330, pp. 527–538, DOI: 10.1007/978-3-030-87178-9_52.
  39. Marais Sicre C., Fieuzal R., Baup F., Contribution of multispectral (optical and radar) satellite images to the classification of agricultural surfaces, Intern. J. Applied Earth Observation and Geoinformation, 2020, V. 84, Article 101972, DOI: 10.1016/j.jag.2019.101972.
  40. Muruganantham P., Wibowo S., Grandhi S. et al., A systematic literature review on crop yield prediction with deep learning and remote sensing, Remote Sensing, 2022, V. 14, No. 9, Article 1990, DOI: 10.3390/rs14091990.
  41. Novikov A. I., Vovchenko N. G., Sokolov S. V. et al., Improving the quality of automated VIS-grading of Scots pine seeds using fuzzy logic algorithm, IOP Conf. Series: Earth and Environmental Science, 2021, V. 875, Article 012032, DOI: 10.1088/1755-1315/875/1/012032.
  42. Novikova T. P., Tylek P., Petrishchev E. P., Novikov A. I. et al., Higher plant seed container germination success predicted by smart farming optical RGB approach, Scientific Reports, 2026, V. 16, Article 13021, DOI: 10.1038/s41598-026-42258-9.
  43. Pei J., Tan S., Zou Y. et al., The role of phenology in crop yield prediction: Comparison of ground-based phenology and remotely sensed phenology, Agricultural and Forest Meteorology, 2025, V. 361, Article 110340, DOI: 10.1016/j.agrformet.2024.110340.
  44. Peng B., Guan K., Tang J. et al., Towards a multiscale crop modelling framework for climate change adaptation assessment, Nature Plants, 2020, V. 6, No. 4, pp. 338–348, DOI: 10.1038/s41477-020-0625-3.
  45. Radočaj D., Plaščak I., Jurišić M. (2025a), Fusion of Sentinel-2 phenology metrics and saturation-resistant vegetation indices for improved correlation with maize yield maps, Agronomy, 2025, V. 15, No. 6, Article 1329, DOI: 10.3390/agronomy15061329.
  46. Radočaj D., Plaščak I., Jurišić M. (2025b), A comparative assessment of regular and spatial cross-validation in subfield machine learning prediction of maize yield from Sentinel-2 phenology, Eng, 2025, V. 6, No. 10, Article 270, DOI: 10.3390/eng6100270.
  47. Radočaj D., Jurišić M., Plaščak I., Galić L., Satellite remote sensing for crop yield prediction: A review, Agriculture, 2026, V. 16, No. 4, Article 417, DOI: 10.3390/agriculture16040417.
  48. Segarra J., Buchaillot M. L., Araus J. L., Kefauver S. C., Remote sensing for precision agriculture: Sentinel-2 improved features and applications, Agronomy, 2020, V. 10, No. 5, Article 641, DOI: 10.3390/agronomy10050641.
  49. Shahhosseini M., Hu G., Huber I., Archontoulis S. V., Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt, Scientific Reports, 2021, V. 11, No. 1, Article 1606, DOI: 10.1038/s41598-020-80820-1.
  50. Sokolov S. V., Novikov A. I., Adaptive estimation of UVs navigation parameters by irregular inertial-satellite measurements, Intern. J. Intelligent Unmanned Systems, 2021, V. 9, No. 4, pp. 274–282, DOI: 10.1108/IJIUS-10-2019-0056.
  51. Sokolov S., Novikov A., Polyakova M., Adaptive stochastic filtration based on the estimation of the covariance matrix of measurement noises using irregular accurate observations, Inventions, 2021, V. 6, No. 1, Article 10, DOI: 10.3390/inventions6010010.
  52. Sokolov S., Khatlamadzhiyan A., Novikov A. et al., Terminal stochastic filtering of nonlinear dynamic processes: The case of invariant immersion, Proc. 6 th Intern. Scientific Conf. “Intelligent Information Technologies for Industry” (IITI’22), Lecture Notes in Networks and Systems, 2023, V. 566, pp. 369–379, DOI: 10.1007/978-3-031-19620-1_35.
  53. Trevisiol F., Mandanici E., Pagliarani A., Bitelli G., Evaluation of Landsat-9 interoperability with Sentinel-2 and Landsat-8 over Europe and local comparison with field surveys, ISPRS J. Photogrammetry and Remote Sensing, 2024, V. 210, pp. 55–68, DOI: 10.1016/j.isprsjprs.2024.02.021.
  54. van Klompenburg T., Kassahun A., Catal C., Crop yield prediction using machine learning: A systematic literature review, Computers and Electronics in Agriculture, 2020, V. 177, Article 105709, DOI: 10.1016/j.compag.2020.105709.
  55. Wang X., Huang J., Feng Q., Yin D., Winter wheat yield prediction at county level and uncertainty analysis in main wheat-producing regions of China with deep learning approaches, Remote Sensing, 2020, V. 12, No. 11, Article 1744, DOI: 10.3390/rs12111744.
  56. Zhang Z., Yang J., Sun H. et al., MCVI-SANet: A lightweight semi-supervised model for LAI and SPAD estimation of winter wheat under vegetation index saturation, https://arxiv.org/, arXiv:2512.18344, 2025, 33 p., https://arxiv.org/abs/2512.18344.
  57. Zhou W., Liu Y., Ata-Ul-Karim S. T. et al., Integrating climate and satellite remote sensing data for predicting county-level wheat yield in China using machine learning methods, Intern. J. Applied Earth Observation and Geoinformation, 2022, V. 111, Article 102861, DOI: 10.1016/j.jag.2022.102861.