Features of the Geoinformation Monitoring System Using Microwave and Optical Tools


  • Ferdenant A. Mkrtchyan

    Fryazino branch of the Kotelnikov's Institute of Radioengineering and Electronics, RAS, Fryazino, Moscow region, 141190, Russia


Received: 2 April 2024 | Revised: 24 April 2024 | Accepted: 28 April 2024 | Published Online: 14 May 2024


The article discusses the issues of GIMS-technology that combines the methodology of GIS-technology and simulation modeling, giving it predictive functions when solving problems of environmental monitoring of the environment. The relationships between experimental data, algorithms and models of environmental processes are analyzed to implement effective operational control and diagnostics of the environment. Particular attention is paid to remote microwave sensing sensors, which ensure the implementation of the functions of GIMS-technology when solving specific problems of monitoring natural systems. For example, the use of microwave technology makes it possible to quickly obtain data on the state of soil moisture, and salinity water bodies, assess the possibility of critical hydrological situations and monitor the condition of hydraulic structures in regions with increased hydrological hazard. It is noted that GIMS-technology ensures the restoration of the spatial distribution of geoecosystem characteristics based on the data of route and ground measurements, which are characterized by fragmentation in space and episodicity in time. GIMS-technology makes it possible to overcome situations of irreducible information uncertainty, using the evolutionary modeling technique for this. The use of optical sensors and spectroellipsometry and spectrophotometry technologies makes it possible to calculate indicators of the quality of water resources, assessing the content of chemical elements in water and the presence of pollutant stains on the water surface.


GIMS-technology, Remote sensing, Monitoring, Environment, Brightness temperature, Ecosystem, Spectroellipsometry, Spectrophotometry


[1] Kondratyev, K.Ya., Krapivin V.F., 2005. Monitoring and prediction of natural disasters. Il Nuovo Cimento. 27(6), 657. DOI: https://doi.org/10.1393/ncc/i2005-10003-y

[2] Kondratyev, K.Ya., Krapivin, V.F., Savinykh, V.P., et al., 2004. Global Ecodynamics: A multidimensional analysis. Springer-Prazis: Chichester, UK.

[3] Krapivin, V.F., Phillips, G.W., 2001. A remote sensing based expert system to study the Aral-Caspian aquageosystem water regime. Remote Sensing and Environment. 75(2), 201–215.

[4] Mkrtchyan, F.A., Krapivin, V.F., Kovalev, V.I., et al., 2009. An adaptive spectroellipsometer for ecological monitoring. Microwave and Optical Technology Letters. 51(11), 2792–2795. DOI: https://doi.org/10.1002/mop.24730

[5] Kovalev, V.I., Rukovishnikov, A.I., Kovalev, S.V., et al., 2014. An LED multichannel spectral ellipsometer with binary modulation of the polarization state. Instruments аnd Experimental Techniques. 57, 607–610. DOI: https://doi.org/10.1134/S002044121405008X

[6] Krapivin, V.F., Varotsos, C.A., Soldatov, V.Yu., 2015. New ecoinformatics tools in environmental science: Applications and decision-making. Springer: London, UK. pp. 1–903.

[7] Mkrtchyan, F.A., Krapivin, V.F., Shapovalov, S.M. (editors), 2019. About capabilities of GIMS-technology to the study of the marine ecosystems. Proceedings of the Photonics & Electromagnetics Research Symposium (PIERS 2019); 2019 Jun 17–20; Rome, Italy. p. 3393–3397.

[8] Mkrtchyan, F.A., Soldatov, V.Yu., Mkrtchyan, M.A., 2024. About some aspects of use of optical sensors for monitoring the aquatic environment. Journal of Environmental & Earth Sciences. 6(1), 1–10. DOI: https://doi.org/10.30564/jees.v6i1.6006

[9] Mkrtchyan. F.A. (editor), 2019. About optimal algorithms for making statistical decisions for small volume samples and with a-priori parametric uncertainty. Proceedings of the Photonics & Electromagnetics Research Symposium (PIERS 2019); 2019 Jun 17–20; Rome, Italy. p. 3398–3404.

[10] Nitu, C., Krapivin, V.F., Mkrtchyan, F.A., 2020. Advanced ecology. Monitoring, diagnostics, prognosis. Vol. 1. Matrix Rom: Bucharesti. pp. 1–396.

[11] Nitu, C., Krapivin, V.F., Mkrtchyan., 2021. Advanced Ecology. Monitoring, diagnostics, prognosis.Vol. 2. Matrix Rom: Bucharesti. pp. 1–374.

[12] Varotsos, C.A., Krapivin, V.F., Mkrtchyan, F.A., et al., 2023. Constructive processing of microwave and optical data for hydrogeochemical applications. Springer Nature: USA. pp. 1–513. DOI: https://doi.org/10.1007/978-3-031-28877-7

[13] Goody, R.M., Yung, Y.L., 1989. Atmospheric radiation. Oxford University Press: New York, USA. pp. 1–519.

[14] Shifrin, K.S., 1998. Physical Optics and Ocean Water. American Institute of Physics Melville, NY: New York, USA. pp. 1–285.

[15] Solomon, H., 1977. Data dependent clustering techniques. Classification and Clustering. 155–173. DOI: https://doi.org/10.1016/B978-0-12-714250-0.50011-5


How to Cite

Mkrtchyan, F. A. (2024). Features of the Geoinformation Monitoring System Using Microwave and Optical Tools. Journal of Environmental & Earth Sciences, 6(2), 17–28. https://doi.org/10.30564/jees.v6i2.6325