kaz44Вестник Университета Шакарима. Серия технические наукиBulletin of Shakarim University. Technical Sciences2788-79953006-0524«Шәкәрім университеті» КеАҚ10.53360/2788-7995-2025-3(19)-12kaz44-1978Research ArticleАВТОМАТИЗАЦИЯ И ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИ (ОРИГИНАЛЬНАЯ СТАТЬЯ)AUTOMATION AND INFORMATION TECHNOLOGY (ORIGINAL ARTICLE)ТЕХНОЛОГИИ ДИСТАНЦИОННОГО ЗОНДИРОВАНИЯ В ОЦЕНКЕ ПОЧВЕННОЙ ВЛАЖНОСТИ НА ТЕРРИТОРИИ, ПОДВЕРЖЕННОЙ ПАВОДКАМ: ОБЗОРREMOTE SENSING TECHNOLOGIES IN ASSESSING SOIL MOISTURE IN FLOOD-PRONE AREAS: REVIEWhttps://orcid.org/0000-0001-7161-2686БондаровичА. А.BondarovichA. A.

Андрей Александрович Бондарович – кандидат географических наук, доцент кафедры экономической географии и картографии

Российская Федерация, Алтайский край, г. Барнаул, пр. Ленина, 61

Andrey Bondarovich – candidate of Geographical Sciences; Associate Professor of the Department of economic geography and cartography

61 Lenin Ave., Barnaul, Altai Territory 

a9130262571@gmail.comМаулитА.MaulitA.

Алмасбек Маулит – докторант

071412, Республика Казахстан, г. Семей, ул. Глинки, 20 А

Almasbek Maulit – doctoral student of Shakarim University; head of the Technopark «Shygys Bastau»

071412, Republic of Kazakhstan, Semey, Glinka str., 20 A

maulit.almas@gmail.comОчередькоИ. А.OcheredkoI. A.

Игорь Александрович Очередько – Научный сотрудник технопарка «Shygys Bastau»

Республика Казахстан, г. Усть-Каменогорск, ул. Казахстан 55

Igor Alexandrovich Ocheredko – researcher of the Technopark «Shygys Bastau»

Ust-Kamenogorsk, 55 Kazakhstan Street

egor007kz@mail.ruЖантасоваЖ. З.ZhantasovaJ. Z.

Женискуль Зейнешовна Жантасова – доцент кафедры компьютерного моделирования и информационных технологий

 Республика Казахстан, г. Усть-Каменогорск, ул. Казахстан 55 

Zheniskul Zeyneshovna Zhantasova – associate professor of the Department of computer modeling and information technologies

Ust-Kamenogorsk, 55 Kazakhstan Street

Zheniskul_z@mail.ruАлтайский государственный университетРоссияAltai State UniversityRussian FederationУниверситета им. ШакаримаКазахстанSemey Shakarim UniversityKazakhstanВосточно-Казахстанкий университет им. С. АманжоловаКазахстанS.Amanzholov East Kazakhstan UniversityKazakhstan20250311202503(19)98116Copyright © Бондарович А.А., Маулит А., Очередько И.А., Жантасова Ж.З., 20252025Бондарович А.А., Маулит А., Очередько И.А., Жантасова Ж.З.Bondarovich A.A., Maulit A., Ocheredko I.A., Zhantasova J.Z.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://tech.vestnik.shakarim.kz/jour/article/view/1978

Почвенная влажность является одним из ключевых гидрологических параметров, напрямую влияющих на формирование поверхностного стока, паводков и оползней. Особенно важную роль она играет в периоды весеннего снеготаяния и экстремальных метеоосадков, когда насыщенность почвы влагой достигает критических уровней. В условиях глобального изменения климата и участившихся экстремальных погодных явлений возрастает потребность в оперативном и точном мониторинге влажности почвы, особенно для регионов, подверженных паводкам, таких как Восточно-Казахстанская область. В статье представлен комплексный обзор современных методов дистанционного зондирования (ДЗЗ), применяемых для оценки почвенной влаги с целью повышения эффективности прогнозирования паводков. Рассматриваются возможности пассивных радиометров (например, SMAP, SMOS), активных радарных сенсоров (Sentinel-1, RADARSAT) и оптических систем (Sentinel-2, Landsat-8) в извлечении информации о влажностных характеристиках верхнего слоя почвы. Кроме того, освещены локальные методы с использованием мультиспектральных камер, установленных на беспилотных летательных аппаратах (БПЛА), что особенно актуально при необходимости локального мониторинга с высоким разрешением. Проведен сравнительный анализ дистанционных и наземных методов измерения почвенной влаги. Особое внимание уделено алгоритмическим подходам обработки спутниковых данных: спектральным и радиометрическим индексам (NDVI, NDMI, LST), методам машинного обучения и нейросетевым архитектурам для улучшения оценки пространственно-временных вариаций почвенной влаги. Рассматриваются также программные решения и платформы, такие как Google Earth Engine, SNAP, ArcGIS и QGIS, обеспечивающие доступ и автоматизированную обработку больших объёмов спутниковых данных. Таким образом, обзор демонстрирует значительный потенциал интеграции ДЗЗ в гидрологические модели для формирования эффективной системы раннего предупреждения о паводках и устойчивого водохозяйственного планирования на региональном уровне.

Soil moisture plays a key role in the formation of surface runoff and floods, especially in conditions of spring snowmelt and extreme precipitation. The relevance of soil moisture monitoring is increasing for areas with high flood risk, such as the East Kazakhstan region. This article provides an overview of modern remote sensing (remote sensing) methods used to assess soil moisture in order to predict floods. Global and regional studies demonstrating the effectiveness of integrating satellite data (SMAP, Sentinel-1/2, SMOS, etc.) into hydrological models are considered. A comparative analysis of ground-based and remote methods of measuring soil moisture is carried out, approaches using radiometers, radars, multispectral sensors, as well as unmanned aerial vehicles (UAVs) are described. Special attention is paid to data processing algorithms, including spectral indexes, machine learning methods, and neural network models. The capabilities of the software (Google Earth Engine, SNAP, ArcGIS, QGIS) in mapping and monitoring tasks are analyzed. The review highlights the potential of remote sensing in improving the accuracy of early flood warnings and laying the foundations for sustainable water risk management.

почвенная влажностьдистанционное зондированиепаводкиспутниковый мониторингмашинное обучениенейросетимультиспектральные данныеsoil moistureremote sensingfloodssatellite monitoringEast Kazakhstan regionmachine learningneural networksGISmultispectral dataSMAPSentinelРабота выполнена при финансовой поддержке Комитета науки Министерства науки и высшего образования Республики Казахстан в рамках программно-целевого финансирования по научным, научно-техническим программам на 2024-2026гг по теме «Разработка системы прогнозирования катастрофических паводков в ВосточноКазахстанской области с применением данных ДЗЗ, ГИС-технологий и машинного обучения» (ИРН BR24992899).ReferencesThe impacts of rainfall and soil moisture on flood hazards in a humid mountainous catchment: a modeling investigation / T. Yu et al // Frontiers in Earth Science. – 2023. – № 11. – Р. 1285766. frontiersin.orgfrontiersin.org.The impacts of rainfall and soil moisture on flood hazards in a humid mountainous catchment: a modeling investigation / T. Yu et al // Frontiers in Earth Science. – 2023. – № 11. – R. 1285766.frontiersin.orgfrontiersin.org. (In English).The relative importance of antecedent soil moisture and precipitation in flood generation in the Yangtze River basin / Q. Ran et al // Hydrology and Earth System Sciences. – 2022. – № 26. – Р. 4919-4936. hess.copernicus.org.The relative importance of antecedent soil moisture and precipitation in flood generation in the Yangtze River basin / Q. Ran et al // Hydrology and Earth System Sciences. – 2022. – № 26. – R. 4919-4936. hess.copernicus.org. (In English).Initial Soil Moisture Effects on Flash Flood Generation – A Comparison Between Basins of Contrasting Hydro-Climatic Conditions / M.G. Grillakis et al // J. Hydrol. – 2016. – № 541. – Р. 206-217. https://doi.org/10.1016/j.jhydrol.2016.03.007.Initial Soil Moisture Effects on Flash Flood Generation – A Comparison Between Basins of Contrasting Hydro-Climatic Conditions / M.G. Grillakis et al // J. Hydrol. – 2016. – № 541. – R. 206-217. https://doi.org/10.1016/j.jhydrol.2016.03.007. (In English).The Relative Importance of Different Flood-Generating Mechanisms across Europe / W.R. Berghuijs // J.W. Water Resour. Res. – 2019. – № 55. – Р. 4582-4593. https://doi.org/10.1029/2019WR024841.The Relative Importance of Different Flood-Generating Mechanisms across Europe / W.R. Berghuijs // J.W. Water Resour. Res. – 2019. – № 55. – R. 4582-4593. https://doi.org/10.1029/2019WR024841. (In English).Orth R. Drought Reduces Blue-Water Fluxes More Strongly than Green-Water Fluxes in Europe / R. Orth, G. Destouni // Nat. Commun. – 2018. – № 9. – Р. 3602. https://doi.org/10.1038/s41467-018-06013-7.Orth R. Drought Reduces Blue-Water Fluxes More Strongly than Green-Water Fluxes in Europe / R. Orth, G. Destouni // Nat. Commun. – 2018. – № 9. – R. 3602. https://doi.org/10.1038/s41467-018-06013-7. (In English).Yang W. Classifying Floods by Quantifying Driver Contributions in the Eastern Monsoon Region of China / W. Yang, H. Yang D. Yang // J. Hydrol. – 2020. – № 585. – Р. 124767. https://doi.org/10.1016/j.jhydrol.2020.124767.Yang W. Classifying Floods by Quantifying Driver Contributions in the Eastern Monsoon Region of China / W. Yang, H. Yang D. Yang // J. Hydrol. – 2020. – № 585. – R. 124767. https://doi.org/10.1016/j.jhydrol.2020.124767. (In English).Study on the Dominant Mechanism of Extreme Flow Events in the Middle and Lower Reaches of the Yangtze River / J. Wang et al // China Rural Water Hydropower. – 2022. – № 06. – Р. 119-124.Study on the Dominant Mechanism of Extreme Flow Events in the Middle and Lower Reaches of the Yangtze River / J. Wang et al // China Rural Water Hydropower. – 2022. – № 06. – R. 119-124. (In English).Sharma S. Baseflow Significantly Contributes to River Floods in Peninsular India / S. Sharma, P.P. Mujumdar // Sci. Rep. – 2024. – № 14. – Р. 1251. https://doi.org/10.1038/s41598-024-51850-w.Sharma S. Baseflow Significantly Contributes to River Floods in Peninsular India / S. Sharma, P.P. Mujumdar // Sci. Rep. – 2024. – № 14. – R. 1251. https://doi.org/10.1038/s41598-024-51850-w. (In English).Effects in Flood Drivers Challenge Estimates of Extreme River Floods / S. Jiang et al // J. Compounding Sci. Adv. – 2024. – № 10. – Р. eadl4005. https://doi.org/10.1126/sciadv.adl4005.Effects in Flood Drivers Challenge Estimates of Extreme River Floods / S. Jiang et al // J. Compounding Sci. Adv. – 2024. – № 10. – R. eadl4005. https://doi.org/10.1126/sciadv.adl4005. (In English).Close Co-Variation Between Soil Moisture and Runoff Emerging from Multi-Catchment Data Across Europe / N. Ghajarnia et al // G. Sci. Rep. – 2020. – № 10. – Р. 4817. https://doi.org/10.1038/s41598-020-61621-y.Close Co-Variation Between Soil Moisture and Runoff Emerging from Multi-Catchment Data Across Europe / N. Ghajarnia et al // G. Sci. Rep. – 2020. – № 10. – R. 4817. https://doi.org/10.1038/s41598-020-61621-y. (In English).Assimilation of surface- and root-zone ASCAT soil moisture products into rainfall–runoff modeling / L. Brocca et al // IEEE Trans. Geosci. Remote Sens. – 2012. – № 50(7). – Р. 2542-2555.mdpi.commdpi.comAssimilation of surface- and root-zone ASCAT soil moisture products into rainfall–runoff modeling / L. Brocca et al // IEEE Trans. Geosci. Remote Sens. – 2012. – № 50(7). – R. 2542-2555.mdpi.commdpi.com. (In English).Munawar H.S. Remote Sensing Methods for Flood Prediction: A Review / H.S. Munawar, A.W.A. Hammad, S.T. Waller // Sensors. – 2022. – № 22(3). – Р. 960.mdpi.commdpi.comMunawar H.S. Remote Sensing Methods for Flood Prediction: A Review / H.S. Munawar, A.W.A. Hammad, S.T. Waller // Sensors. – 2022. – № 22(3). – R. 960.mdpi.commdpi.com. (In English).https://smap.jpl.nasa.gov/https://smap.jpl.nasa.gov/. (In English).Application of Soil Moisture Data Assimilation in Flood Forecasting of Xun River in Hanjiang River Basin / J. Bai et al // Water. – 2022. – № 14(24). – Р. 4061.mdpi.commdpi.comApplication of Soil Moisture Data Assimilation in Flood Forecasting of Xun River in Hanjiang River Basin / J. Bai et al // Water. – 2022. – № 14(24). – R. 4061.mdpi.commdpi.com. (In English).Exploring Sentinel-1 and Sentinel-2 diversity for flood inundation mapping using deep learning / G. Konapala et al // ISPRS Journal of Photogrammetry and Remote Sensing. – 2021. – № 180. – Р. 163-173.mdpi.comExploring Sentinel-1 and Sentinel-2 diversity for flood inundation mapping using deep learning / G. Konapala et al // ISPRS Journal of Photogrammetry and Remote Sensing. – 2021. – № 180. – R. 163-173.mdpi.com. (In English).Родионова Н.В. Оценка влажности почвы по радарным данным на основе множественной регрессии / Н.В. Радионова // Распространение радиоволн. – 2023. – С. 432-436.Rodionova N.V. Otsenka vlazhnosti pochvy po radarnym dannym na osnove mnozhestvennoi regressii / N.V. Radionova // Rasprostranenie radiovoln. – 2023. – S. 432-436. (In Russian).https://innoter.com/https://innoter.com/. (In English).Волчек А.А. Источники данных глобального мониторинга влажности почвы средствами дистанционного зондирования поверхности земли / А.А. Волчек, Д.О. Петров // Гидрометеорология и экология. – 2021. – № 1(100). – С. 38-43.Volchek A.A. Istochniki dannykh global'nogo monitoringa vlazhnosti pochvy sredstvami distantsionnogo zondirovaniya poverkhnosti zemli / A.A. Volchek, D.O. Petrov // Gidrometeorologiya i ehkologiya. – 2021. – № 1(100). – S. 38-43. (In Russian).Investigating Soil Moisture–Climate Interactions in a Changing Climate: A Review / S.I. Seneviratne et al // Earth-Sci. Rev. – 2010. – № 99. – Р. 125-161. https://doi.org/10.1016/j.earscirev.2010.02.004.Investigating Soil Moisture–Climate Interactions in a Changing Climate: A Review / S.I. Seneviratne et al // Earth-Sci. Rev. – 2010. – № 99. – R. 125-161. https://doi.org/10.1016/j.earscirev.2010.02.004. (In English).Ground, Proximal, and Satellite Remote Sensing of Soil Moisture / E. Babaeian et al // M. Rev. Geophys. – 2019. – № 57. – Р. 530-616. https://doi.org/10.1029/2018RG000618.Ground, Proximal, and Satellite Remote Sensing of Soil Moisture / E. Babaeian et al // M. Rev. Geophys. – 2019. – № 57. – R. 530-616. https://doi.org/10.1029/2018RG000618. (In English).Accuracy Calibration and Evaluation of Capacitance-Based Soil Moisture Sensors for a Variety of Soil Properties / В. Li et al // Agric. Water Manag. – 2022. – № 273. – Р. 107913. https://doi.org/10.1016/j.agwat.2022.107913.Accuracy Calibration and Evaluation of Capacitance-Based Soil Moisture Sensors for a Variety of Soil Properties / V. Li et al // Agric. Water Manag. – 2022. – № 273. – R. 107913. https://doi.org/10.1016/j.agwat.2022.107913. (In English).Soil Moisture Measurement for Ecological and Hydrological Watershed-Scale Observatories: A Review / D.A. Robinson et al // Vadose Zone J. – 2008. – № 7. – Р. 358-389. https://doi.org/10.2136/vzj2007.0143.Soil Moisture Measurement for Ecological and Hydrological Watershed-Scale Observatories: A Review / D.A. Robinson et al // Vadose Zone J. – 2008. – № 7. – R. 358-389. https://doi.org/10.2136/vzj2007.0143. (In English).Validation Analysis of SMAP and AMSR2 Soil Moisture Products over the United States Using Ground-Based Measurements / X. Zhang et al // S. Remote Sens. – 2017. – № 9. – Р. 104. https://doi.org/10.3390/rs9020104.Validation Analysis of SMAP and AMSR2 Soil Moisture Products over the United States Using Ground-Based Measurements / X. Zhang et al // S. Remote Sens. – 2017. – № 9. – R. 104. https://doi.org/10.3390/rs9020104. (In English).Assessing In-Field Soil Moisture Variability in the Active Root Zone Using Granular Matrix Sensors / В. Hodges et al // Agric. Water Manag. – 2023. – № 282. – Р. 108268. https://doi.org/10.1016/j.agwat.2023.108268.Assessing In-Field Soil Moisture Variability in the Active Root Zone Using Granular Matrix Sensors / V. Hodges et al // Agric. Water Manag. – 2023. – № 282. – R. 108268. https://doi.org/10.1016/j.agwat.2023.108268. (In English).The International Soil Moisture Network: Serving Earth System Science for over a Decade / W. Dorigo et al // Hydrol. Earth Syst. Sci. – 2021. – № 25. – Р. 5749-5804. https://doi.org/10.5194/hess-25-5749-2021.The International Soil Moisture Network: Serving Earth System Science for over a Decade / W. Dorigo et al // Hydrol. Earth Syst. Sci. – 2021. – № 25. – R. 5749-5804. https://doi.org/10.5194/hess-25-5749-2021. (In English).QLB-NET: A Dense Soil Moisture and Freeze–Thaw Monitoring Network in the Qinghai Lake Basin on the Qinghai–Tibetan Plateau / L. Chai et al // Bull. Am. Meteorol. Soc. – 2024. – № 105. – Р. E584-E604. https://doi.org/10.1175/bams-d-23-0186.1.QLB-NET: A Dense Soil Moisture and Freeze–Thaw Monitoring Network in the Qinghai Lake Basin on the Qinghai–Tibetan Plateau / L. Chai et al // Bull. Am. Meteorol. Soc. – 2024. – № 105. – R. E584-E604. https://doi.org/10.1175/bams-d-23-0186.1. (In English).Forschungsarbeiten zum Bodenwasserhaushalt in der kasachischen Steppe / L. Haselow et al // Wasserwirtsch. – 2020. – № 110. – Р. 34-40. https://doi.org/10.1007/s35147-020-0366-2.Forschungsarbeiten zum Bodenwasserhaushalt in der kasachischen Steppe / L. Haselow et al // Wasserwirtsch. – 2020. – № 110. – R. 34-40. https://doi.org/10.1007/s35147-020-0366-2. (In English).A Review of Satellite-Derived Soil Moisture and Its Usage for Flood Estimation / S. Kim et al // Remote Sens. Earth Syst. Sci. – 2019. – № 2. – Р. 225-246. https://doi.org/10.1007/s41976-019-00025-7.A Review of Satellite-Derived Soil Moisture and Its Usage for Flood Estimation / S. Kim et al // Remote Sens. Earth Syst. Sci. – 2019. – № 2. – R. 225-246. https://doi.org/10.1007/s41976-019-00025-7. (In English).Satellite Remote Sensing of Soil Moisture for Hydrological Applications: A Review of Issues to Be Solved. In ICT for Smart Water Systems: Measurements and Data Science / L. Zhuo et al // The Handbook of Environmental Chemistry; Springer: Cham, Switzerland. – 2019. – Vol. 102. https://doi.org/10.1007/698_2019_394.Satellite Remote Sensing of Soil Moisture for Hydrological Applications: A Review of Issues to Be Solved. In ICT for Smart Water Systems: Measurements and Data Science / L. Zhuo et al // The Handbook of Environmental Chemistry; Springer: Cham, Switzerland. – 2019. – Vol. 102. https://doi.org/10.1007/698_2019_394. (In English).Chen Y. An Improved Global Remote-Sensing-Based Surface Soil Moisture (RSSSM) Dataset Covering 2003-2018 / Y. Chen, X. Feng, B. Fu // Earth Syst. Sci. – 2021. – № 13. – Р. 1-31. https://doi.org/10.5194/essd-13-1-2021.Chen Y. An Improved Global Remote-Sensing-Based Surface Soil Moisture (RSSSM) Dataset Covering 2003-2018 / Y. Chen, X. Feng, B. Fu // Earth Syst. Sci. – 2021. – № 13. – R. 1-31. https://doi.org/10.5194/essd-13-1-2021. (In English).Global Soil Moisture Patterns Observed by Spaceborne Microwave Radiometers and Scatterometers / R.A. De Jeu et al // Surv. Geophys. – 2008. – № 29. – Р. 399-420. https://doi.org/10.1007/s10712-008-9044-0.Global Soil Moisture Patterns Observed by Spaceborne Microwave Radiometers and Scatterometers / R.A. De Jeu et al // Surv. Geophys. – 2008. – № 29. – R. 399-420. https://doi.org/10.1007/s10712-008-9044-0. (In English).Monitoring Hydrological Changes with Satellite Data: Spring Thaw's Effect on Soil Moisture and Groundwater in Seasonal Freezing–Thawing Zones / J. Wang et al // J. Hydrol. – 2023. – № 626. – Р. 130365. https://doi.org/10.1016/j.jhydrol.2023.130365.Monitoring Hydrological Changes with Satellite Data: Spring Thaw's Effect on Soil Moisture and Groundwater in Seasonal Freezing–Thawing Zones / J. Wang et al // J. Hydrol. – 2023. – № 626. – R. 130365. https://doi.org/10.1016/j.jhydrol.2023.130365. (In English).Kashyap B. Sensing methodologies in agriculture for soil moisture and nutrient monitoring / B. Kashyap, R. Kumar // IEEE Access. – 2021. – Т. 9. – Р. 14095-14121.Kashyap B. Sensing methodologies in agriculture for soil moisture and nutrient monitoring / B. Kashyap, R. Kumar // IEEE Access. – 2021. – T. 9. – R. 14095-14121. (In English).Noborio K. Measurement of soil water content and electrical conductivity by time domain reflectometry: a review / K. Noborio // Computers and electronics in agriculture. – 2001. – Т. 31, № 3. – Р. 213-237.Noborio K. Measurement of soil water content and electrical conductivity by time domain reflectometry: a review / K. Noborio // Computers and electronics in agriculture. – 2001. – T. 31, № 3. – R. 213-237. (In English).A generalized frequency domain reflectometry modeling technique for soil electrical properties determination / J. Minet et al // Vadose zone journal. – 2010. – Т. 9, № 4. – Р. 1063-1072.A generalized frequency domain reflectometry modeling technique for soil electrical properties determination / J. Minet et al // Vadose zone journal. – 2010. – T. 9, № 4. – R. 1063-1072. (In English).SU S.L. A critical review of soil moisture measurement / S.L. SU, D.N. Singh, M.S. Baghini // Measurement. – 2014. – Т. 54. – Р. 92-105.SU S.L. A critical review of soil moisture measurement / S.L. SU, D.N. Singh, M.S. Baghini // Measurement. – 2014. – T. 54. – R. 92-105. (In English).State of the art in large‐scale soil moisture monitoring / T.E. Ochsner et al // Soil Science Society of America Journal. – 2013. – Т. 77, № 6. – Р. 1888-1919.State of the art in large‐scale soil moisture monitoring / T.E. Ochsner et al // Soil Science Society of America Journal. – 2013. – T. 77, № 6. – R. 1888-1919. (In English).Soil moisture measurement for ecological and hydrological watershed-scale observatories: A review / D.A. Robinson et al // Vadose zone journal. – 2008. – Т. 7, № 1. – Р. 358-389.Soil moisture measurement for ecological and hydrological watershed-scale observatories: A review / D.A. Robinson et al // Vadose zone journal. – 2008. – T. 7, № 1. – R. 358-389. (In English).Initial soil moisture retrievals from the METOP‐A Advanced Scatterometer (ASCAT) / Z. Bartalis et al // Geophysical Research Letters. – 2007. – Т. 34, № 20.Initial soil moisture retrievals from the METOP‐A Advanced Scatterometer (ASCAT) / Z. Bartalis et al // Geophysical Research Letters. – 2007. – T. 34, № 20. (In English).Visual sensing for urban flood monitoring / S.W. Lo et al // Sensors. – 2015. – Т. 15, № 8. – Р. 20006-20029.Visual sensing for urban flood monitoring / S.W. Lo et al // Sensors. – 2015. – T. 15, № 8. – R. 20006-20029. (In English).Kaku K. Satellite remote sensing for disaster management support: A holistic and staged approach based on case studies in Sentinel Asia / K. Kaku // International Journal of Disaster Risk Reduction. – 2019. – Т. 33. – Р. 417-432.Kaku K. Satellite remote sensing for disaster management support: A holistic and staged approach based on case studies in Sentinel Asia / K. Kaku // International Journal of Disaster Risk Reduction. – 2019. – T. 33. – R. 417-432. (In English).https://developers.google.com/https://developers.google.com/ (In English).Soil Moisture Measuring Techniques and Factors Affecting the Moisture Dynamics: A Comprehensive Review / A. Sarwar et al // Sustainability. – 2022. – № 14(18). – Р. 11538.mdpi.commdpi.com.Soil Moisture Measuring Techniques and Factors Affecting the Moisture Dynamics: A Comprehensive Review / A. Sarwar et al // Sustainability. – 2022. – № 14(18). – R. 11538.mdpi.commdpi.com. (In English).Deep Learning-Based Framework for Soil Moisture Content Retrieval of Bare Soil from Satellite Data / М. Dabboor et al // Remote Sensing. – 2023. – № 15(7). – Р. 1916.mdpi.commdpi.com.Deep Learning-Based Framework for Soil Moisture Content Retrieval of Bare Soil from Satellite Data / M. Dabboor et al // Remote Sensing. – 2023. – № 15(7). – R. 1916.mdpi.commdpi.com. (In English).Assessing the Potential of UAV-Based Multispectral and Thermal Data to Estimate Soil Water Content Using Geophysical Methods / Y. Guan et al // Remote Sensing. – 2024. – № 16(1). – Р. 61. (Published 22 Dec 2023) mdpi.commdpi.com.Assessing the Potential of UAV-Based Multispectral and Thermal Data to Estimate Soil Water Content Using Geophysical Methods / Y. Guan et al // Remote Sensing. – 2024. – № 16(1). – R. 61. (Published 22 Dec 2023) mdpi.commdpi.com. (In English).Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data / I. Ali et al // Remote Sensing. – 2015. – № 7. – Р. 16398-16421.mdpi.com.Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data / I. Ali et al // Remote Sensing. – 2015. – № 7. – R. 16398-16421.mdpi.com. (In English).COSMOS: The Cosmic-Ray Soil Moisture Observing System / М. Zreda et al // Hydrology and Earth System Sciences. – 2012. – № 16. – Р. 4079-4099.COSMOS: The Cosmic-Ray Soil Moisture Observing System / M. Zreda et al // Hydrology and Earth System Sciences. – 2012. – № 16. – R. 4079-4099. (In English).Soil moisture measurement for ecological and hydrological watershed-scale observatories: A review / D.A. Robinson et al // Vadose Zone Journal. – 2008. – № 7(1). – Р. 358-389.Soil moisture measurement for ecological and hydrological watershed-scale observatories: A review / D.A. Robinson et al // Vadose Zone Journal. – 2008. – № 7(1). – R. 358-389. (In English).The Soil Moisture Active Passive (SMAP) Mission / D. Entekhabi et al // Proceedings of the IEEE. – 2010. – № 98(5). – Р. 704-716.The Soil Moisture Active Passive (SMAP) Mission / D. Entekhabi et al // Proceedings of the IEEE. – 2010. – № 98(5). – R. 704-716. (In English).The SMOS Mission: New Tool for Monitoring Key Elements of the Global Water Cycle / Y.H. Kerr et al // Proceedings of the IEEE. – 2010. – № 98(5). – Р. 666-687.The SMOS Mission: New Tool for Monitoring Key Elements of the Global Water Cycle / Y.H. Kerr et al // Proceedings of the IEEE. – 2010. – № 98(5). – R. 666-687. (In English).Google Earth Engine: Planetary-scale geospatial analysis for everyone / N. Gorelick et al // Remote Sensing of Environment. – 2017. – № 202. – Р. 18-27.sciencedirect.com.Google Earth Engine: Planetary-scale geospatial analysis for everyone / N. Gorelick et al // Remote Sensing of Environment. – 2017. – № 202. – R. 18-27.sciencedirect.com. (In English).ESA CCI Soil Moisture for improved Earth system monitoring: Evaluation of the Climate Data Record / W. Dorigo et al // Remote Sensing of Environment. – 2017. – № 203. – Р. 185-201.ESA CCI Soil Moisture for improved Earth system monitoring: Evaluation of the Climate Data Record / W. Dorigo et al // Remote Sensing of Environment. – 2017. – № 203. – R. 185-201. (In English).Soil moisture estimation through ASCAT and AMSR-E sensors: An intercomparison and validation study across Europe / L. Brocca et al // Remote Sensing of Environment. – 2011. – № 115(12). – Р. 3390-3408.Soil moisture estimation through ASCAT and AMSR-E sensors: An intercomparison and validation study across Europe / L. Brocca et al // Remote Sensing of Environment. – 2011. – № 115(12). – R. 3390-3408. (In English).Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach (Bagging-KNN) / H. Shahabi et al // Remote Sensing. – 2020. – № 12(2). – Р. 266.Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach (Bagging-KNN) / H. Shahabi et al // Remote Sensing. – 2020. – № 12(2). – R. 266. (In English).Influence of changes in rainfall and soil moisture on trends in flooding (study in Mediterranean region) / Y. Tramblay et al // Journal of Hydrology. – 2018. – № 560. – Р. 245-258.Influence of changes in rainfall and soil moisture on trends in flooding (study in Mediterranean region) / Y. Tramblay et al // Journal of Hydrology. – 2018. – № 560. – R. 245-258. (In English).Anusha N. Flood detection and flood mapping using multi-temporal synthetic aperture radar and optical data / N. Anusha, B. Bharathi // Egyptian Journal of Remote Sensing and Space Science. – 2020. – № 23(2). – Р. 207-219.Anusha N. Flood detection and flood mapping using multi-temporal synthetic aperture radar and optical data / N. Anusha, B. Bharathi // Egyptian Journal of Remote Sensing and Space Science. – 2020. – № 23(2). – R. 207-219. (In English).Atar M. Retrieval of Soil Moisture Using Time Series of Radar and Optical Remote Sensing Data at 10 m Resolution / M. Atar, R. Shah-Hosseini, O. Ghaffari // Environ. Sci. Proc. – 2024. – № 29. – Р. 75. https://doi.org/10.3390/ECRS2023-16861.Atar M. Retrieval of Soil Moisture Using Time Series of Radar and Optical Remote Sensing Data at 10 m Resolution / M. Atar, R. Shah-Hosseini, O. Ghaffari // Environ. Sci. Proc. – 2024. – № 29. – R. 75. https://doi.org/10.3390/ECRS2023-16861. (In English).Chen, Y. An Improved Remote Sensing-Based Global Surface Soil Moisture Dataset (RSSSM, 2003-2020) [Dataset]. PANGAEA 2022. https://doi.org/10.1594/PANGAEA.940004.Chen, Y. An Improved Remote Sensing-Based Global Surface Soil Moisture Dataset (RSSSM, 2003-2020) [Dataset]. PANGAEA 2022. https://doi.org/10.1594/PANGAEA.940004. (In English).Dodin A. Principal Characteristics of the Geological Structure and Genesis of the Eastern Part of the Altai-Sayan Structural Zone / A. Dodin // In Natural Conditions of the Krasnoyarsk Region; Nauka: Moskva, Russia. – 1961. – р. 99-125.Dodin A. Principal Characteristics of the Geological Structure and Genesis of the Eastern Part of the Altai-Sayan Structural Zone / A. Dodin // In Natural Conditions of the Krasnoyarsk Region; Nauka: Moskva, Russia. – 1961. – r. 99-125. (In English).A Sentinel-1 SAR-Based Global 1-km Resolution Soil Moisture Data Product: Algorithm and Preliminary Assessment. Remote Sens / D. Fan et al // Environ. – 2025. – № 318. – Р. 114579. https://doi.org/10.1016/j.rse.2024.114579.A Sentinel-1 SAR-Based Global 1-km Resolution Soil Moisture Data Product: Algorithm and Preliminary Assessment. Remote Sens / D. Fan et al // Environ. – 2025. – № 318. – R. 114579. https://doi.org/10.1016/j.rse.2024.114579. (In English).Gao Y. Light Thinning Can Improve Soil Water Availability and Water Holding Capacity of Plantations in Alpine Mountains / Y. Gao et al // Front. Plant Sci. – 2022. – № 13. – Р. 1032057. https://doi.org/10.3389/fpls.2022.1032057.Gao Y. Light Thinning Can Improve Soil Water Availability and Water Holding Capacity of Plantations in Alpine Mountains / Y. Gao et al // Front. Plant Sci. – 2022. – № 13. – R. 1032057. https://doi.org/10.3389/fpls.2022.1032057. (In English).Ensemble of Optimised Machine Learning Algorithms for Predicting Surface Soil Moisture Content at a Global Scale / Q. Han et al // Geosci. Model Dev. – 2023. – № 16. – Р. 5825-5845. https://doi.org/10.5194/gmd-16-5825-2023.Ensemble of Optimised Machine Learning Algorithms for Predicting Surface Soil Moisture Content at a Global Scale / Q. Han et al // Geosci. Model Dev. – 2023. – № 16. – R. 5825-5845. https://doi.org/10.5194/gmd-16-5825-2023. (In English).Understanding the Impacts of Predecessor Rain Events on Flood Hazard in a Changing Climate / A. Khatun et al // Hydrol. Process. – 2022. – № 36. – Р. e14500. https://doi.org/10.1002/hyp.14500.Understanding the Impacts of Predecessor Rain Events on Flood Hazard in a Changing Climate / A. Khatun et al // Hydrol. Process. – 2022. – № 36. – R. e14500. https://doi.org/10.1002/hyp.14500. (In English).Augmenting Daily MODIS LST with AIRS Surface Temperature Retrievals to Estimate Ground Temperature and Permafrost Extent in High Mountain Asia / K.Y. Kim et al // Remote Sens. Environ. – 2024. – № 305. – Р. 114075. https://doi.org/10.1016/j.rse.2024.114075.Augmenting Daily MODIS LST with AIRS Surface Temperature Retrievals to Estimate Ground Temperature and Permafrost Extent in High Mountain Asia / K.Y. Kim et al // Remote Sens. Environ. – 2024. – № 305. – R. 114075. https://doi.org/10.1016/j.rse.2024.114075. (In English).Assessment of ERA5-Land Volumetric Soil Water Layer Product Using In Situ and SMAP Soil Moisture Observations / Р. Lal et al // IEEE Geosci. Remote Sens. Lett. – 2022. – № 19. – Р. 2508305. https://doi.org/10.1109/LGRS.2022.3223985.Assessment of ERA5-Land Volumetric Soil Water Layer Product Using In Situ and SMAP Soil Moisture Observations / R. Lal et al // IEEE Geosci. Remote Sens. Lett. – 2022. – № 19. – R. 2508305. https://doi.org/10.1109/LGRS.2022.3223985. (In English).Кабжанова Г.Р. Рахимжанов, Б.К.; Тулеукулова, Д.Т. Оценка возможностей дистанционного мониторинга влажности почвы на территории Северного Казахстана / Г.Р. Кабжанова, Б.К. Рахимжанов, Д.Т. Тулеукулова // Вестник науки КазАТУ им. С.Сейфуллина. – 2024. – № 4(123).Kabzhanova G.R. Rakhimzhanov, B.K.; Tuleukulova, D.T. Otsenka vozmozhnostei distantsionnogo monitoringa vlazhnosti pochvy na territorii Severnogo Kazakhstana / G.R. Kabzhanova, B.K. Rakhimzhanov, D.T. Tuleukulova // Vestnik nauki KaZATU im. S.Seifullina. – 2024. – № 4(123). (In Russian).

The authors declare that there are no conflicts of interest present.