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ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2023, Vol. 20, No. 6, pp. 222-233

Towards the development of a remote microwave indicator of hydrological drought (case study of seasonal drying of a hyperhaline lake)

A.N. Romanov 1 , I.V. Khvostov 1 , I.V. Ryabinin 1 , D.A. Romanov 1 
1 Institute for Water and Environmental Problems SB RAS, Barnaul, Russia
Accepted: 31.10.2023
DOI: 10.21046/2070-7401-2023-20-6-222-233
The paper presents the results of a 10-year study of seasonal variations in brightness temperatures of the hyperhaline Kulunda Lake located on the territory of the Kulunda Plain (southern Western Siberia) characterized by an arid climate with low annual precipitation. On their basis, a new approach to the development of a remote microwave index of hydrological drought is proposed. The analysis of the seasonal dynamics of brightness (Tb) and thermodynamic temperatures of the lake water surface was carried out using the L1C SMOS (Soil Moisture and Ocean Salinity) and MOD11A1 MODIS (Moderate Resolution Imaging Spectroradiometer) products. The L1C product contains Tb values obtained at a sounding angle of 42.5° on horizontal and vertical polarizations and is linked to the discrete geodetic grid DGG ISEA 4H9 (Icosahedral Snyder Equal Area). On the basis of the results of laboratory measurements of dielectric characteristics of water from Lake Kulunda and bottom soil, experimental dependences of the emissivity coefficients of the water surface and the dried bottom on temperature were established. Using satellite optical data, the patterns of seasonal drying out of the lake were studied and periodic drying cycles were identified. The developed algorithm can be used for an area with several drying lakes that lie within a pixel of satellite radiometer.
Keywords: salinity, mineral lake, emissivity, radio brightness temperature, SMOS satellite, Western Siberia
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References:

  1. Dobrovolsky S. G., Droughts of the world and their evolution over time: agricultural, meteorological and hydrological aspects, Water resources, 2015, Vol. 42, No. 2, pp. 119–132 (in Russian), DOI: 10.7868/S0321059615020042.
  2. Doganovsky A. M., Myakisheva N. V., Prokofieva T. I., Water balance of lakes in the zone of insufficient moisture, In: Problemy sovremennoi gidrologii, 2004, pp. 66–76 (in Russian).
  3. Vladimirov A. M., Classification of hydrological droughts, Scientific notes of the Russian State Hydrometeorological University, 2012, No. 23, pp. 5–12 (in Russian).
  4. Vladimirov A. M., Malysheva N. G., Estimation of the probability of occurrence of hydrological drought, Scientific notes of the Russian State Hydrometeorological University, 2012, No. 24, pp. 5–17 (in Russian).
  5. Lebedeva (Verba) M. P., Lopukhina O. V., Kalinina N. V., Specificity of the Chemical and Mineralogical Composition of Salts in Solonchak Playas and Lakes of the Kulunda Steppe, Pochvovedenie, 2008, No. 4. pp. 467–480 (in Russian).
  6. Niyazov Dj. B., Kalashnikova O. Yu., Use of MODIS imagery in assessing environmental risks associated with associated with water-related hazards, News of Universities of Kyrgyzstan, 2018, No. 11, pp. 55–59 (in Russian).
  7. Rahmouni A., Meddi M., Hamoudi Saaed A., Hydrological drought response to meteorological drought propagation and basin characteristics: a case study for northwest of Algeria, Meteorology and Hydrology, 2022, No. 9, pp. 94–107 (in Russian), DOI: 10.52002/0130-2906-2022-9-94-107.
  8. Romanov A. N., Influence of water content and temperature on the dielectric and radio-emitting properties of the salt crust of puffy solonchak, Eurasian Soil Science, 2019, Vol. 52, No. 2, pp. 171–179, DOI: 10.1134/S1064229319020121.
  9. Romanov A. N., Khvostov I. V., Kosmicheskii mikrovolnovyi monitoring zasolennykh pochv i solenykh ozer Kulundinskoi ravniny (Space microwave monitoring of saline soils and salt lakes of the Kulunda Plain), Barnaul: OOO “Pyat’ plyus”, 2017, 88 p. (in Russian).
  10. De Natale F., Alilla R., Parisse B., Nardi P., A bibliometric analysis on drought and heat indices in agriculture, Agricultural and Forest Meteorology, 2023, Vol. 341, Article 109626, DOI: 10.1016/j.agrformet.2023.109626.
  11. Enguehard P., Frappart F., Zeiger P. et al., Contribution of automatically generated radar altimetry water levels from unsupervised classification to study hydrological connectivity within Amazon floodplains, J. Hydrology: Regional Studies, 2023, Vol. 47, Article 101397, DOI: 10.1016/j.ejrh.2023.101397.
  12. Feng L., Hu C., Chen X., Satellites Capture the Drought Severity Around China’s Largest Freshwater Lake, J. Selected Topics in Applied Earth Observations and Remote Sensing,2012, Vol. 5, No. 4, pp. 1266–1271, DOI: 10.1109/JSTARS.2012.2188885.
  13. Foroumandi E., Nourani V., Kantoush S. A., Investigating the main reasons for the tragedy of large saline lakes: Drought, climate change, or anthropogenic activities? A call to action, J. Arid Environments, 2022, Vol. 196, Article 104652, DOI: 10.1016/j.jaridenv.2021.104652.
  14. Gutierrez A., Castro R., Vieira P. et al., SMOS L1 Processor L1c Data Processing Model, Lisboa: DEIMOS Engenharia, 2017, 83 p., https://earth.esa.int/eogateway/documents/20142/37627/SMOS-L1c-Data-Processing-Models.pdf.
  15. Haxhismajli B., Hajrizi E., Qehaja B. et al., Image Analysis of Water Level using Remote Sensing, 29 th Intern. Conf. Systems, Signals and Image Processing, Sofia, Bulgaria, 2022, pp. 1–6, DOI: 10.1109/IWSSIP55020.2022.9854490.
  16. Isikdogan F., Bovik A. C., Passalacqua P., Surface Water Mapping by Deep Learning, J. Selected Topics in Applied Earth Observations and Remote Sensing, 2017, Vol. 10, No. 11, pp. 4909–4918, DOI: 10.1109/JSTARS.2017.2735443.
  17. Keller A. A., Garner K., Rao N. et al., Hydrological models for climate-based assessments at the watershed scale: A critical review of existing hydrologic and water quality models, Science of the Total Environment, 2023, Vol. 867, Article 161209, DOI: 10.1016/j.scitotenv.2022.161209.
  18. Lai C., Zhong R., Wang Z. et al., Monitoring hydrological drought using long-term satellite-based precipitation data, Science of the Total Environment, 2019, Vol. 649, pp. 1198–1208, DOI: 10.1016/j.scitotenv.2018.08.245.
  19. Sahr K., White D., Kimerling A. J., Geodesic Discrete Global Grid Systems, Cartography and Geographic Information Science, 2003, Vol. 30, pp. 121–134.
  20. Schaepman M. E., Spectrodirectional remote sensing: From pixels to processes, Intern. J. Applied Earth Observation and Geoinformation, 2007, Vol. 9, No. 2, pp. 204–223.
  21. Su Z., Roebeling R. A., Schulz J. et al., Observation of Hydrological Processes Using Remote Sensing, Treatise on Water Science, 2011, Vol. 2, pp. 351–399.
  22. Sur C., Park S.-Y., Kim J.-S., Lee J.-H., Prognostic and diagnostic assessment of hydrological drought using water and energy budget-based indices, J. Hydrology, 2020, Vol. 591, Article 125549, DOI: 10.1016/j.jhydrol.2020.125549.
  23. Svoboda M., Fuchs B. A., Handbook of Drought Indicators and Indices, Geneva, 2016, 45 p., https://www.droughtmanagement.info/literature/GWP_Handbook_of_Drought_Indicators_and_Indices_2016.pdf
  24. Yang P., Xia J., Chen Y. et al., Dynamic evolution of recent droughts in Central Asia based on microwave remote sensing satellite products, J. Hydrology, 2023, Vol. 620, Pt. B, Article 129497, DOI: 10.1016/j.jhydrol.2023.129497.
  25. Zhang L., Jiao W., Zhang H. et al., Studying drought phenomena in the Continental United States in 2011 and 2012 using various drought indices, Remote Sensing of Environment, 2017, Vol. 190, pp. 96–106, DOI: 10.1016/j.rse.2016.12.010.
  26. Zhang Q., Shi R., Xu C.-Y. et al., Multisource data-based integrated drought monitoring index: Model development and application, J. Hydrology, 2022, Vol. 615, Pt. A, Article 128644, DOI: 10.1016/j.jhydrol.2022.128644.
  27. Zhang X., Hao Z., Singh V. P. et al., Drought propagation under global warming: Characteristics, approaches, processes, and controlling factors, Science of the Total Environment, 2022, Vol. 838, Pt. 2, Article 156021, DOI: 10.1016/j.scitotenv.2022.156021.