СУ ТАСҚЫНЫНА БЕЙІМ АУМАҚТАҒЫ ТОПЫРАҚ ЫЛҒАЛДЫЛЫҒЫН БАҒАЛАУДАҒЫ ҚАШЫҚТЫҚТАН ЗОНДТАУ ТЕХНОЛОГИЯЛАРЫ: ШОЛУ

Топырақ ылғалдылығы жер үсті ағындары мен су тасқынының пайда болуында, әсіресе көктемгі қар мен қатты жауын-шашын жағдайында шешуші рөл атқарады. Шығыс Қазақстан облысы сияқты су тасқыны қаупі жоғары аумақтар үшін топырақ ылғалдылығы мониторингінің өзектілігі артып келеді. Бұл мақалада су тасқынын болжау үшін топырақ ылғалдылығын бағалау үшін қолданылатын заманауи қашықтықтан зондтау әдістеріне (ЖҚЗ) шолу берілген. Гидрологиялық модельдерге спутниктік деректерді (SMAP, Sentinel-1/2, SMOS және т.б.) біріктірудің тиімділігін көрсететін жаһандық және аймақтық зерттеулер қарастырылды. Топырақ ылғалдылығын өлшеудің жер үсті және қашықтықтан әдістеріне салыстырмалы талдау жүргізілді, радиометрлерді, радарларды, мультиспектрлік сенсорларды, сондай-ақ ұшқышсыз ұшу аппараттарын (ҰҰА) қолдану тәсілдері сипатталды. Деректерді өңдеу алгоритмдеріне, соның ішінде спектрлік индекстерге, Машиналық оқыту әдістеріне және нейрондық желі модельдеріне ерекше назар аударылады. Картаға түсіру және бақылау тапсырмаларында бағдарламалық жасақтаманың (Google Earth Engine, Snap, ArcGIS, QGIS) мүмкіндіктері талданады. Шолу су тасқыны туралы ерте ескертудің дәлдігін арттыруда және су тәуекелдерін тұрақты басқару үшін негіздерді қалыптастыруда ЖҚЗ әлеуетін көрсетеді.

1. 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. – Р. 1285766. frontiersin.orgfrontiersin.org.

2. 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.

3. 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.

4. 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.

5. 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.

6. 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.

7. 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.

8. 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.

9. 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.

10. 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.

11. 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.com

12. 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.com

13. https://smap.jpl.nasa.gov/

14. 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.com

15. 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.com

16. Родионова Н.В. Оценка влажности почвы по радарным данным на основе множественной регрессии / Н.В. Радионова // Распространение радиоволн. – 2023. – С. 432-436.

17. https://innoter.com/

18. 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.

19. 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.

20. 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.

21. 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.

22. 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.

23. 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.

24. 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.

25. 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.

26. 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.

27. 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.

28. 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.

29. 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.

30. 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.

31. 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.

32. 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.

33. Kashyap B. Sensing methodologies in agriculture for soil moisture and nutrient monitoring / B. Kashyap, R. Kumar // IEEE Access. – 2021. – Т. 9. – Р. 14095-14121.

34. 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.

35. 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.

36. 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.

37. 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.

38. 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.

39. Initial soil moisture retrievals from the METOP‐A Advanced Scatterometer (ASCAT) / Z. Bartalis et al // Geophysical Research Letters. – 2007. – Т. 34, № 20.

40. Visual sensing for urban flood monitoring / S.W. Lo et al // Sensors. – 2015. – Т. 15, № 8. – Р. 20006-20029.

41. 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.

42. https://developers.google.com/

43. 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.

44. 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.

45. 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.

46. 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.

47. COSMOS: The Cosmic-Ray Soil Moisture Observing System / М. Zreda et al // Hydrology and Earth System Sciences. – 2012. – № 16. – Р. 4079-4099.

48. 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.

49. The Soil Moisture Active Passive (SMAP) Mission / D. Entekhabi et al // Proceedings of the IEEE. – 2010. – № 98(5). – Р. 704-716.

50. 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.

51. Google Earth Engine: Planetary-scale geospatial analysis for everyone / N. Gorelick et al // Remote Sensing of Environment. – 2017. – № 202. – Р. 18-27.sciencedirect.com.

52. 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.

53. 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.

54. 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.

55. 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.

56. 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.

57. 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.

58. 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.

59. 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.

60. 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.

61. 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.

62. 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.

63. 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.

64. 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.

65. 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.

66. 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).