Features of Creating a System of Space Monitoring of Water-Supplied Territories for Irrigation in the South of Kazakhstan

• Autorzy: Malakhov Dmitry V. , Tskhay Mikhail , Kalashnikov Alexander A. , Bekmukhamedov Nurlan E. , Kalashnikov Pavel A. , Baizakova Aigul

Identyfikatory

DOI

10.12911/22998993/153398

• Języki publikacji: EN

Abstrakty

EN

The location of a significant part of the agricultural territories of Kazakhstan in the risk agriculture zone implies the development and further application of an objective monitoring system for irrigated territories. The purpose of the study was to develop methods for on-the-spot and long-term recognition of irrigated massifs and verification of methods in the conditions of the territories of southern Kazakhstan. The paper describes the methods of on-the-spot recognition of irrigated fields, the general assessment of irrigated areas for the growing season, as well as the method of recognizing promising areas for irrigation. The on-the-spot recognition of the fields is based on the use of such spectral indices as the Global Vegetation Moisture Index, Green Normalized Difference Vegetation Index, Normalized Difference Vegetation Index, and the xanthophyll index, combined into a single system by the Decision Tree algorithm. The assessment of irrigated areas is based on differences in the physiological state of plants in conditions of normal water supply and plants experiencing a lack of moisture. The evaluation system includes the calculation of the temperature difference according to the corresponding satellite data and the calculation of the difference in vegetation indices for the same period. The difference in vegetation indices in irrigated fields has positive values due to a steady increase in green biomass, and the temperature difference, on the contrary, is negative or zero, since healthy plants, with normal water supply, actively evaporate moisture to maintain optimal temperatures of biochemical processes. To develop these methods, ground data from 2017–2021 were used. Verification of the methods with ground data demonstrated acceptable accuracy (87% in the on-the-spot assessment of irrigated fields; 60–90% in the general assessment of irrigated areas), while the methods have significant potential for further improvement.

Słowa kluczowe

EN

irrigation; spectral indices; decision tree; monitoring; evaluation

• Wydawca: Polskie Towarzystwo Inżynierii Ekologicznej ; Lublin University of Technology
• Czasopismo: Journal of Ecological Engineering
• Rocznik: 2022
• Tom: Vol. 23, nr 11
• Strony: 202--216
• Opis fizyczny: Bibliogr. 32 poz., rys., tab.

Twórcy

autor

Malakhov Dmitry V.

• National Center for Space Research and Technology, 15 Shevchenko str., 050010, Almaty, Kazakhstan

• https://orcid.org/0000-0002-7844-6569

autor

Tskhay Mikhail

• Kazakh Scientific Research Institute of Water Economy, 12 Koigeldy str., 080003, Taraz, Kazakhstan

autor

Kalashnikov Alexander A.

• Kazakh Scientific Research Institute of Water Economy, 12 Koigeldy str., 080003, Taraz, Kazakhstan

autor

Bekmukhamedov Nurlan E.

• National Center for Space Research and Technology, 15 Shevchenko str., 050010, Almaty, Kazakhstan

autor

Kalashnikov Pavel A.

• Kazakh Scientific Research Institute of Water Economy, 12 Koigeldy str., 080003, Taraz, Kazakhstan

autor

Baizakova Aigul

• Kazakh Scientific Research Institute of Water Economy, 12 Koigeldy str., 080003, Taraz, Kazakhstan

Bibliografia

• 1. Bastiaanssen, W.G.M.. 1998. Remote sensing in water resources management: The state of art. International Water Management Institute, Colombo, 118.
• 2. Bastiaanssen, W.G.M., Menenti, M., Feddes, R.A., Holtslag, A.A.M. 1998. A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation. Journal of Hydrology, 212–213, 198–212. https://doi.org/10.1016/s0022-1694(98)00253-4
• 3. Beven, K.J., Kirkby, M.J. 1979. A physically based, variable contributing area model of basin hydrology. Hydrological Science Bulletin, 24(1), 43–69. https://doi.org/10.1080/02626667909491834
• 4. Ceccato, P., Gobron, N., Flasse, S., Pinty, B., Tarantola, S. 2002. Designing a spectral index to estimate vegetation water content from remote sensing data: Part 1: Theoretical approach. Remote Sensing of Environment, 82(2–3), 188–197. https://doi.org/10.1016/s0034-4257(02)00037-8
• 5. El-Magd, I.A., Tanton, T.W. 2005. Remote sensing and GIS for estimation of irrigation crop water demand. International Journal of Remote Sensing, 26(11), 2359–2370.
• 6. El-Shirbeny, M.A., Alsersy, M.A.M., Nasser, H.S., Khaled, A.A.T. 2015. Changes in irrigation water consumption in the Nile Delta of Egypt assessed by remote sensing. Arabian Journal of Geosciences, 8, 10509–10519. https://doi.org/10.1007/s12517-015-2005-2
• 7. French, A.N., Hunsaker, D.J., Thorp, K.R. 2015. Remote sensing of evapotranspiration over cotton using the TSEB and METRIC energy balance models. Remote Sensing of Environment, 158, 281–294.
• 8. Gitelson, A., Merzlyak, M. 1998. Remote sensing of chlorophyll concentration in higher plant leaves. Advances in Space Research, 22(5), 689–692.
• 9. Government of the Republic of Kazakhstan. 2016. Decree of the Government of the Republic of Kazakhstan dated April 8, 2016 No. 200 “On approval of the General scheme for the integrated use and protection of water resources”. Available at: https://adilet.zan.kz/rus/docs/P1600000200 (access date: January 20, 2022).
• 10. Huang, Y., Fipps, G., Maas, S.J., Fletcher, R.S. 2010. Airborne remote sensing for detection of irrigation canal leakage. Irrigation and Drainage, 59(5), 524–534.
• 11. Johnson, T.D., Belitz, K. 2012. A remote sensing approach for estimating the location and rate of urban irrigation in semi-arid climates Journal of Hydrology, 414–415, 86–98.
• 12. Karimi, P., Bongani, B., Blatchford, M., de Fraiture, C. 2019. Global satellite-based ET products for the local level irrigation management: An application of irrigation performance assessment in the sugarbelt of Swaziland. Remote Sensing, 11(6), 705. https://doi.org/10.3390/rs11060705
• 13. Kottek, M., Grieser, J., Beck, C., Rudolf, B., Rubel, F. 2006. World map of the Köppen-Geiger climate classification updated. Meteorologische Zeitschrift, 15(3), 259–260.
• 14. Lu, L., Zhihao, Q., Jingjing, L. 2008. Mapping the irrigation area of winter wheat farmland in North China plain using MODIS remote sensing data. Proceedings of SPIE 7104, Remote Sensing for Agriculture, Ecosystems, and Hydrology, 10, 71040P. https://doi.org/10.1117/12.800167
• 15. Mandal, S.M. 2012. Remote sensing and GIS based ground water potential mapping of Kangshabati irrigation command area, West Bengal. Journal of Geography and Natural Disasters, 1(1), 1-8. https://doi.org/10.4172/2167-0587.1000104
• 16. Medeu, A.R. (Ed.). 2010. Natsionalnyi atlas Respubliki Kazakhstan [National Atlas of the Republic of Kazakhstan]. Vol. 1. Natsionalnyi nauchnotekhnologicheskii kholding Parasat, Almaty, 158.
• 17. Medeu, A.R., Malkovskii, I.M., Toleubaeva, L.S., Alimkulov, S.K. 2015. Vodnaya bezopasnost Respubliki Kazakhstan: Problemy ustoichivogo vodoobespecheniya [Water security of the Republic of Kazakhstan: Problems of sustainable water supply]. Institute of Geography, Almaty, 582. [in Russian].
• 18. Medvedev, S.S. 2004. Fiziologiya rastenii [Plant physiology]. St. Petersburg University Press, St. Petersburg, 336. [in Russian]
• 19. Moore, I.D., Gessler, P.E., Nielsen, G.A., Petersen, G.A. 1993. Terrain attributes: Estimation methods and scale effects. In: A.J. Jakeman, M.B. Beck, M. McAleer (Eds.), Modelling change in environmental systems. Wiley, London, 189–214.
• 20. Moran, M.S., Maas, S.J., Vanderbilt, V.C., Barnes, E.M., Miller, S.N., Clarke, T.R. 2004. Application of image-based remote sensing to irrigated agriculture. In: S. Ustin (Ed.), Remote sensing for natural resources management and environmental monitoring: Manual of remote sensing, 3 ed. John Wiley&Sons, New York, 4, 617–676.
• 21. Ozdogan, M., Yang, Y., Allez, G., Cervantes, C. 2010. Remote sensing of irrigated agriculture: Opportunities and challenges. Remote Sensing, 2, 2274–2304. https://doi.org/10.3390/rs2092274
• 22. Peñuelas, J., Gamon, J.A., Fredeen, A.L., Merino, J., Field, C.B. 1994. Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower leaves. Remote Sensing of Environment, 48, 135–146.
• 23. President of the Republic of Kazakhstan. 2006. Decree of the President of the Republic of Kazakhstan dated November 14, 2006 No. 216. On the concept of the transition of the Republic of Kazakhstan to sustainable development for 2007–2024. Available at: https://adilet.zan.kz/rus/docs/U060000216_/info (access date: January 20, 2022).
• 24. Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W. 1974. Monitoring vegetation systems in the Great Plains with ERTS. Third ERTS Symposium, NASA SP-351, Washington DC, December 10–14, 1973. Vol. 1, 309–317. Available at: https://ntrs.nasa.gov/citations/19740022614
• 25. Santos, C., Lorite, I.J., Tasumi, M., Allen, R.G., Fereres, E. 2008. Integrated satellite-based evapotranspiration with simulation models for irrigation at the scheme level. Irrigation Science, 26, 277–288. https://doi.org/10.1007/s00271-007-0093-9
• 26. Sørensen, R., Zinko, U., Seibert, J. 2006. On the calculation of the topographic wetness index: evaluation of different methods based on field observations. Hydrology and Earth System Sciences, 10(1), 101–112.
• 27. Stagakis, S., González-Dugo, V., Cid, P., Guillén-Climent, M.L., Zarco-Tejad, P.J. 2012. Monitoring water stress and fruit quality in an orange orchard under regulated deficit irrigation using narrow-band structural and physiological remote sensing indices. ISPRS Journal of Photogrammetry and Remote Sensing, 71, 47–61.
• 28. Taghvaeian, S., Chávez, J.L., Hansen, N.C. 2012. Infrared thermometry to estimate crop water stress index and water use of irrigated maize in Northeastern Colorado. Remote Sensing, 4(11), 3619–3637. https://doi.org/10.3390/rs4113619
• 29. Taghvaeian, S., Chávez, J.L., Hattendorf, M.J., Crookston, M.A. 2013. Optical and thermal remote sensing of turfgrass quality, water stress, and water use under different soil and irrigation treatments. Remote Sensing, 5, 2327–2347. https://doi.org/10.3390/rs5052327
• 30. Trezza, R. 2006. Evapotranspiration from a remote sensing model for water managementin an irrigation system in Venezuela. Interciencia, 31(6), 417–423.
• 31. Tsychueva, N.Y., Malakhov, D.V. 2016. Irrigated land detection technique using satellite data and GIS. In: Water resources of Central Asia and their use. Materials of the international research and practice conference dedicated to summarizing the results of the decade for action “Water for Life” declared by the UN. LLP „Nurai Print Sersis”, Almaty, 182–188.
• 32. Yousaf, W., Awan, W.K., Kamran, M., Ahmad, S.R., Bodla, H.U., Riaz, M., Umar, M., Chohan, K. 2021. A paradigm of GIS and remote sensing for crop water deficit assessment in near real time to improve irrigation distribution plan. Agricultural Water Management, 243, 106443. https://doi.org/10.1016/j.agwat.2020.106443

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).