EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 623 (2025) E3S Web Conf., 623 (2025) 01027 References
Open Access
Issue
E3S Web Conf.
Volume 623, 2025
IV International Conference on Ensuring Sustainable Development: Ecology, Earth Science, Energy and Agriculture (AEES2024)
Article Number 01027
Number of page(s) 12
Section Ecology, Biodiversity and Ways of its Conservation
DOI https://doi.org/10.1051/e3sconf/202562301027
Published online 08 April 2025
  1. M.H. Khamidov, K.S. Khamraev, ISSN 0869-8155, Agrarian science, 10 (2019) https://doi.org/10.32634/0869-8155-2019-332-9-76-79 [Google Scholar]
  2. Recommendations of the Ministry of Agriculture and Water Resources of the Republic of Uzbekistan on the “Procedure for irrigation of agricultural crops” (Tashkent, 2006) [Google Scholar]
  3. W.G. Hopkins, N.P. Hüner, Introduction to plant physiology, 4th ed, Wiley & Sons, New York (2009) [Google Scholar]
  4. I.Yu. Savin, S.A. Bartalev, E.A. Lupyan, V.A. Tolpin, S.A. Khvostikov, Forecasting crop yields based on satellite data: opportunities and prospects, Modern problems of remote sensing of the Earth from space, 7, 3, 275–285 (2010) [Google Scholar]
  5. J. Liu, E. Pattey, J. R. Miller, H. McNairn, A. Smith, B. Hu, Estimating crop stresses, aboveground dry biomass and yield of corn using multi-temporal optical data combined with a radiation use efficiency model, Remote Sens. Environ, 114, 6, 11671177 (2010) DOI: 10.1016/j.rse.2010.01.004 [Google Scholar]
  6. B. Duchemin, P. Maisongrande, G. Boulet, I.A. Benhadj, Simple algorithm for yield estimates: Evaluation for semiarid irrigated winter wheat monitored with green leaf area index, Environ, Modell. Softw, 23, 7, 876–892 (2008) DOI: 10.1016/j.envsoft.2007.10.003 [CrossRef] [Google Scholar]
  7. F. Rembold, C. Atzberger, I. Savin, O. Rojas, Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection, Remote Sens, 5, 4, 1704–1733 (2013) DOI: 10.3390/rs5041704 [CrossRef] [Google Scholar]
  8. X. Xiao, C. Jin, J. Dong, Gross Primary Production of Terrestrial Vegetation, J.M. Hanes (ed.). Biophysical Applications of Satellite Remote Sensing. Berlin: SpringerVerlag Berlin Heidelberg, 127–148 (2014) DOI: 10.1007/978-3-642-25047-7_5 [Google Scholar]
  9. J.L. Monteith, Solar radiation and productivity in tropical ecosystems, J. Appl. Ecol., 9, 3, 747–766 (1972) DOI: 10.2307/2401901 [CrossRef] [Google Scholar]
  10. H. Medrano, J.M. Escalona, J. Cifre, J. Bota, J. Flexas, A ten-year study on the physiology of two Spanish grapevine cultivars under field conditions: effects of water availability from leaf photosynthesis to grape yield and quality, Functional Plant Biology, 30 (6), 607–619 (2003) [CrossRef] [PubMed] [Google Scholar]
  11. R.D. Jackson, P.J. Pinter, R.J. Reginato, S.B. Idso, Detection and evaluation of plant stresses for crop management decisions, IEEE Transactions on Geoscience and Remote Sensing, GE, 24 (1), 99–106 (1986) [CrossRef] [Google Scholar]
  12. W.C. Snyder, Z. Wan, Y. Zhang, Y.Z. Feng, Classification-based emissivity for land surface temperature measurement from space, International Journal of Remote Sensing, 19 (14), 2753–2774 (1998) [CrossRef] [Google Scholar]
  13. M.S. Jayalakshmy, J. Philip, Thermophysical properties of plant leaves and their influence on the environment temperature, International Journal of Thermophysics, 31 (11-12), 2295–2304 (2010) [CrossRef] [Google Scholar]
  14. O.V. Batyreva, Calculation of the significance of the multiple correlation coefficient and selection of the optimal number of predictors, Meteorology and Hydrology, 3, 4957 (1969) [Google Scholar]
  15. V.M. Pasov, Variability of yields and assessment of expected productivity of grain crops, Hydrometeoizdat, Leningrad, 152 (1980) [Google Scholar]
  16. V.M. Pasov, Variability of the harvest of spring grain crops in various climatic zones of the USSR, Meteorology and Hydrology, 7, 82–86 (1973) [Google Scholar]
  17. V.M. Pasov, Climatic variability of winter wheat harvests, Meteorology and Hydrology, 2, 94–103 (1973) [Google Scholar]
  18. Methodology of the MARS Crop Yield Forecasting System, Eur Rep 21291 EN/1-4. (2008) [Google Scholar]
  19. O.D. Sirotenko, E.A. Abashina, On the assessment of the use of dynamic models to assess the agrometeorological conditions of crop formation, Meteorology and hydrology, 8, 95–101 (1982) [Google Scholar]
  20. E.S. Ulanova, V.A. Moiseychik, A.N. Polevoy, A guide to agrometeorological forecasts, Hydrometeoizdat, Leningrad, 1–2 (1984) [Google Scholar]
  21. The method of the main components, http://www.machmeleammg.ru/wiki/mdex.php?title=MeTOd_raaeHbix_ĸoMnoHeHT// [Google Scholar]
  22. J.W. Boardman, F.A. Kruse, Automated Spectral Analysis: A Geological Example Using AVIRIS Data, North Grapevine Mountains, Nevada, In: ERIM, Ed., Proc. 10th Thematic Conference on Geological Remote Sensing, San Antonio, 9-12 May 1994, 407–418 (1994) [Google Scholar]
  23. Giles M. Foody, Global and Local Assessment of Image Classification Quality on an Overall and Per-Class Basis without Ground Reference Data. Remote Sens, 14, 5380 (2022) https://doi.org/10.3390/rs14215380 [CrossRef] [Google Scholar]
  24. Zhou Wang, Alan C. Bovik, Hamid R. Sheikh, P. Simoncelli, Image quality assessment: from error visibility to structural similarity, IEEE Transactions on image processing, 13, 4 (2004) [Google Scholar]
  25. Meteoblue, https://www.meteoblue.com/ru/climate-change [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.