Open Access
Issue |
E3S Web Conf.
Volume 623, 2025
IV International Conference on Ensuring Sustainable Development: Ecology, Earth Science, Energy and Agriculture (AEES2024)
|
|
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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 |
- 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]
- 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]
- W.G. Hopkins, N.P. Hüner, Introduction to plant physiology, 4th ed, Wiley & Sons, New York (2009) [Google Scholar]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- V.M. Pasov, Variability of yields and assessment of expected productivity of grain crops, Hydrometeoizdat, Leningrad, 152 (1980) [Google Scholar]
- 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]
- V.M. Pasov, Climatic variability of winter wheat harvests, Meteorology and Hydrology, 2, 94–103 (1973) [Google Scholar]
- Methodology of the MARS Crop Yield Forecasting System, Eur Rep 21291 EN/1-4. (2008) [Google Scholar]
- 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]
- E.S. Ulanova, V.A. Moiseychik, A.N. Polevoy, A guide to agrometeorological forecasts, Hydrometeoizdat, Leningrad, 1–2 (1984) [Google Scholar]
- The method of the main components, http://www.machmeleammg.ru/wiki/mdex.php?title=MeTOd_raaeHbix_ĸoMnoHeHT// [Google Scholar]
- 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]
- 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]
- 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]
- Meteoblue, https://www.meteoblue.com/ru/climate-change [Google Scholar]
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