BR 10965172 – Space monitoring and GIS for quantifying soil salinity and degradation of agricultural land in southern Kazakhstan

Term of realization: 2021-2023

Purpose of the study:

The goal of the program is formulated by the Science Committee of the Republic of Kazakhstan as: “Creation of a web-based geoinformation service for operational monitoring of a quantitative assessment of the degree of soil salinity and degradation of agricultural land in the South of Kazakhstan based on data from remote sensing of the Earth.”

According to the technical assignment of the Customer in this program, the object and subject of research are saline agricultural lands of South Kazakhstan, in which the arable soil layer is at the level (0-30 cm). The scale of the required salinity maps is M 1: 300000. The minimum object that must be mapped determines the relationship between the map scale and information saturation (normative documentation: GOST R 51608).

Research methods:

Expert interpretation, standard statistical methods of correlation analysis of remotely recorded parameters (NDVI, LST, etc.) and ground information. Methods for clustering and classifying satellite data.

Scope and implementation of the results: 

Salinization of irrigated arable land and degradation of agricultural lands in the South of Kazakhstan.

Salinization of irrigated arable land and agricultural land degradation in South Kazakhstan is a systemic negative factor affecting four Kazakhstan’s provinces: Turkistan, Almaty, Jambul and Kyzylorda. The main problem is related to water resources, which are formed by the transboundary river outflow (the Syr Darya, the Ile, and the Chu rivers).  Kazakhstan is located in the lower reaches of these river basins and therefore is very vulnerable. An increase in water consumption in upper parts of river basins belonging to territories of neighboring countries (Uzbekistan, China, Kyrgyzstan) and climate change create problems of water availability for irrigated agriculture in the South Kazakhstan. Water supply and food security of South Kazakhstan arouse the task for developing technologies for monitoring soil salinity and agricultural lands degradation. The modern level of solving those problems is based on space monitoring. To solve this problem, the Committee of Science of the Republic of Kazakhstan formulated a special «Technical requirement # 11» as part of the program-targeted funding competition for the period: 05.01.2021 – 31.12.2023. The Institute of Information and Computer Technologies of the National Academy of Sciences of Kazakhstan, together with the Auezov South Kazakhstan University and the Kazakh National Agrarian Research University of the Ministry of Agriculture developed this project proposal.  The project collaborators are: the state USA universities of Maryland and Michigan, with the participation of the CARIN_GOFC-GOLD program (NASA-ESA) as well as institutes of the Russian Academy of Sciences: the Space Research Institute and the named Dokuchaev Soil Science Institute

The purpose of the program

“Creation of web-geoformation service of the quantitative assessment of soil salinity and agricultural land degradation in the South Kazakhstan based on remote sensing data”.

Program tasks

Task 1: «To develop methods and algorithms for quantitative assessment of the degree of agricultural land soil salinity in South Kazakhstan based on ground-based data and remote sensing».

Task 2: “To develop technologies for quantitative assessment of soil salinity in South Kazakhstan using medium and high spatial resolution remote sensing data”.

Task 3: «Analyze non-commercial satellite data and spectral indices»;

Task 4: «To develop a methodology for assessing the degradation of agricultural land in the South of Kazakhstan based on remote sensing data”. The process of degradation of irrigated arable land due to soil salinization will be considered.

Task 5: «Develop a web geoinformation service for rapid monitoring of quantitative assessment of soil salinity and agricultural land degradation in South Kazakhstan on the basis of remote sensing data.

Auxiliary tasks:

Task 6: Prepare documentation and reports for the program.

Task 7: Preparation and publication of articles in scientific journals in accordance with the competition documentation.

Publications:

1. Mukhamediev, R.; Amirgaliyev, Y.; Kuchin, Y.; Aubakirov, M.; Terekhov, A.; Merembayev, T.; Yelis, M.; Zaitceva, E.; Levashenko, V.; Popova, Y.; Symagulov, A.; Tabynbayeva, L. Operational Mapping of Salinization Areas in Agricultural Fields Using Machine Learning Models Based on Low-Altitude Multispectral Images. Drones 2023, 7, 357. https://doi.org/10.3390/drones7060357 (Scopus Quartile: Q1, 91%, JCR Category Quartile: Q2, WoS IF=5.532)
2. Mukhamediev R. I. Yakunin, K., Aubakirov, M., Assanov, I., Kuchin, Y., Symagulov, A., Levashenko V., Zatceva E., Sokolov D., Amirgaliyev, Y. Coverage path planning optimization of heterogeneous UAVs group for precision agriculture //IEEE Access. – 2023. – Т. 11. – №. 15. – С. 5789-5803, doi: 10.1109/ACCESS.2023.3235207, https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10011226 (Scopus Quartile: Q1, 90%, JCR Category Quartile: Q2, WoS IF=3.476)
3. Zaitseva, E., Levashenko, V., Brinzei, N., Kovalenko, A., Yelis, M., Gopejenko, V., & Mukhamediev, R. (2023). Reliability Assessment of UAV Fleets. In Emerging Networking in the Digital Transformation Age: Approaches, Protocols, Platforms, Best Practices, and Energy Efficiency (pp. 335-357). Cham: Springer Nature Switzerland. https://www.researchgate.net/profile/Elena-Zaitseva-2/publication/369409989_Reliability_Assessment_of_UAV_Fleets/links/641d5db6a1b72772e42293f8/Reliability-Assessment-of-UAV-Fleets.pdf
4. Mukhamediev, R. I., Popova, Y., Kuchin, Y., Zaitseva, E., Kalimoldayev, A., Levashenko V., Symagulov, A., Abdoldina F., Gopejenko V., Yakunin K., Muhamedijeva E., Yelis, M. Review of Artificial Intelligence and Machine Learning Technologies: Classification, Restrictions, Opportunities and Challenges //Mathematics. – 2022. – Т. 10. – №. 15. – С. 2552. https://doi.org/10.3390/math10152552 (CiteScore highest quartile = Q1, CiteScore =2.9, CiteScore highest percentile=86%, IF=2.592)
5. Mukhamediev, R. I., Kuchin, Y., Amirgaliyev, Y., Yunicheva, N., Muhamedijeva, E. Estimation of Filtration Properties of Host Rocks in Sandstone-Type Uranium Deposits Using Machine Learning Methods //IEEE Access. – 2022. – Т. 10. – С. 18855-18872. (SCOPUS, Q1, процентиль 87, Web of Science Impact Factor=3,05) https://doi.org/10.1109/ACCESS.2022.3149625
6. Mukhamediev R. I. et al. Review of Some Applications of Unmanned Aerial Vehicles Technology in the Resource-Rich Country //Applied Sciences. – 2021. – Т. 11. – №. 21. – С. 10171. https://doi.org/10.3390/app112110171 (CiteScore highest quartile = Q2, JCR — Q2, CiteScore =3.0, CiteScore highest percentile=71%, Web of Science Impact Factor==2.832) https://www.mdpi.com/2076-3417/11/21/10171.
7. А. Г. Терехов, Н. Н. Абаев, Т. А. Тиллякарим, Н. Т. Серикбай О взаимосвязи между количеством снега и объёмом весеннего половодья в Северном Казахстане // Современные проблемы дистанционного зондирования Земли из космоса. 2023. Т. 20. № 1. С. 323–328, DOI: 10.21046/2070-7401-2023-20-1-323-328 (SCOPUS, Q3, процентиль 45).
8. Терехов А.Г., Сагатдинова Г.Н., Долгих С.А., Абаев Н.Н., Амиргалиев Е.Н. «Многолетние тренды изменений состояния пустынной растительности Приаралья и Прибалхашья в Казахстане по данным NDVI MODIS (2000-2022 гг) // Современные проблемы дистанционного зондирования Земли из космоса». Электронный сборник материалов конференции. Институт космических исследований Российской академии наук. Москва, 2022.DOI 10.21046/20DZZconf-2022a
9. Терехов А.Г., Абаев Н.Н., Маглинец Ю.А., Сагатдинова Г.Н., Амиргалиев Е.Н. «Спутниковый мониторинг зимней промывки пашни от вторичного засоления на примере ирригационного массива «Голодная степь» на Юге Казахстана // Современные проблемы дистанционного зондирования Земли из космоса». Электронный сборник материалов конференции. Институт космических исследований Российской академии наук. Москва, 2022. DOI 10.21046/20DZZconf-2022a
10. Yan Kuchin, Jan Rabcan, A Symagulov, Ravil I. Mukhamediev, Bayangali Abdygalym, Nadiya Yunicheva and Elena Mukhamedieva. Calculation filtration coefficient using regression models//The-20th INTERNATIONAL SCIENTIFIC CONFERENCE INFORMATION TECHNOLOGIES AND MANAGEMENT 2022, April 21-22, 2022, ISMA University of Applied Science, Riga, Latvia.-C.27-28.
11. Symagulov, Y.Kuchin, Jan Rabcan, Ye.Kulakova, I. Assanov , R. Mukhamediev. Pretrained Deep Neural Network Models for Image Change Detection//The 20th INTERNATIONAL SCIENTIFIC CONFERENCE INFORMATION TECHNOLOGIES AND MANAGEMENT 2022, April 21-22, 2022, ISMA University of Applied Science, Riga, Latvia.-C.20-21.
12. Symagulov, I. Assanov, Y.Kuchin, Jan Rabcan, Ye.Kulakova, Bayangali Abdygalym. Video pre-processing for computer vision tasks using UAVs //The 20th INTERNATIONAL SCIENTIFIC CONFERENCE INFORMATION TECHNOLOGIES AND MANAGEMENT 2022, April 21-22, 2022, ISMA University of Applied Science, Riga, Latvia.-C.25-26.
13. Мухамедиев Р.И., Амиргалиев Е.Н. Введение в машинное обучение. Учебник. Свидетельство о внесении сведений в государственный реестр прав на объекты, охраняемые авторским правом № 28386 от 19 августа 2022 года. https://www.dropbox.com/s/xav2wuywqigt2m2/ML_Book_%D0%A1%D0%B2%D0%B8%D0%B4%D0%B5%D1%82%D0%B5%D0%BB%D1%8C%D1%81%D1%82%D0%B2%D0%BE.pdf?dl=0