Study on potential application of brightness temperature models in passive remote sensing

Kseniya Nezhalskaya, Valerii Volosyuk, Kostiantyn Bilousov, Denys Kolesnikov, Glib Cherepnin

Abstract


In the modern science and practice of remote sensing of any surface, including various types of land cover, radio systems are widely used. Due to low energy consumption and high stealth, the most promising direction of technical tools for remote sensing is the development of passive remote sensing systems. To estimate the specific parameters of the surface, information on the relation between the signals of the thermal radiation and these parameters is needed. Therefore, it is relevant to study and analyze the existing types of such relations that connect the brightness temperature and the parameters of the Earth’s surfaces; this is the subject matter of the study in this paper. The goal of this study is to analyze existing relation models and study the conditions of their application in terms of operating frequencies, geometric characteristics, type of surface coverage (with vegetation, without vegetation, etc.), as well as the method of obtaining mathematical relations (electrodynamic models or empirical models). Due to the large number of different types of earth surface coverings and different types of surface models, in order to simplify remote sensing tasks for passive systems, as well as to improve the results of modeling or experiments, the following tasks are formulated: to study and classify existing models of the relation between surface parameters and brightness temperature, and to calculate and analyze the potential accuracy of estimating surface parameters by passive remote sensing systems. The following results were obtained: the impact of the atmosphere on the surface’s brightness temperature, described by the electrodynamic flat model, for the operating frequencies 75–110 GHz of the radiometer was analyzed. The impact of the atmosphere on the brightness temperature appears at sighting angles greater than 50° at horizontal polarization and greater than 80° at vertical polarization. According to the results, it can be said that the estimation accuracy of the imaginary part of the complex permittivity decreases with an operating wavelength decrease, and the estimation accuracy of the real part is the opposite. Conclusions. The results of simulation modeling show that for radiometers with the above-mentioned operating frequencies, it is possible to estimate the real permittivity or only the real component separately in the case of describing the surface with a complex permittivity parameter. It is recommended that the study be conducted at vertical and horizontal polarizations, without and with the impact of the atmosphere. To estimate the surface conductivity (the imaginary part of the complex permittivity of a surface), systems with lower operating frequencies are required. Thus, this study presents an analysis of existing models, calculates the potential accuracy of parameters estimation when using these models, and provides recommendations for practical experiments or modeling.

Keywords


brightness temperature; passive remote sensing; surface model; permittivity; conductivity

Full Text:

PDF

References


Hong, Z., Moreno, H. A., Li, Z., Li, S., Greene, J. S., Hong, Y., & Alvarez, L. V. Triple Collocation of Ground-, Satellite- and Land Surface Model-Based Surface Soil Moisture Products in Oklahoma—Part I: Individual Product Assessment. Remote Sensing, 2022, vol. 14, no. 22, article no. 5641. DOI: 10.3390/rs14225641.

Wu, X. Assessment of Effective Roughness Parameters for Simulating Sentinel-1A Observation and Retrieving Soil Moisture over Sparsely Vegetated Field. Remote Sensing, 2022, vol. 14, no. 23, article no. 6020. DOI: 10.3390/rs14236020.

Duan, S.-B., Han, X.-J., Huang, C., Li, Z.-L., Wu, H., Qian, Y., Gao, M., & Leng, P. Land Surface Temperature Retrieval from Passive Microwave Satellite Observations: State-of-the-Art and Future Directions. Remote Sensing, 2020, vol. 12, no. 16, article no. 2573. DOI: 10.3390/rs12162573.

Xu, R., Pan, Z., Han, Y., Zheng, W., & Wu, S. Surface Properties of Global Land Surface Microwave Emissivity Derived from FY-3D/MWRI Measurements. Sensors, 2023, vol. 23, no. 12, article no. 5534. DOI: 10.3390/s23125534.

Bettenhausen, M. H., & Anguelova, M. D. Brightness Temperature Sensitivity to Whitecap Fraction at Millimeter Wavelengths. Remote Sensing, 2019, vol. 11, no. 17, article no. 2036. DOI: 10.3390/rs11172036.

Sharifnezhad, Z., Norouzi, H., Prakash, S., Blake, R., & Khanbilvardi, R. Diurnal Cycle of Passive Microwave Brightness Temperatures over Land at a Global Scale. Remote Sensing, 2021, vol. 13, no. 4, article no. 817. DOI: 10.3390/rs13040817.

Nambiar, M. K., Ambadan, J. T., Rowlandson, T., Bartlett, P., Tetlock, E., & Berg, A. A. Comparing the Assimilation of SMOS Brightness Temperatures and Soil Moisture Products on Hydrological Simulation in the Canadian Land Surface Scheme. Remote Sensing, 2020, vol. 12, no. 20, article no. 3405. DOI: 10.3390/rs12203405.

Linkova, A. M., & Khlopov, G. I. Vosstanovlenie intensivnosti zhidkikh osadkov s pomoshch'yu mnogo-chastotnogo aktivno-passivnogo zondirovaniya [Reconstruction of liquid precipitation intensity using multi-frequency active-passive sensing]. Radiofizika i elektronika – Radiophysics and electronics, 2014, vol. 5(19), no. 3, pp. 26-31. Available at: http://nbuv.gov.ua/UJRN/rphre_2014_5(19)_3_6 (accessed September 13, 2023). (In Russian)

Schulte, R. M., Kummerow, C. D., Berg, W., Reising, S. C., Brown, S. T., Gaier, T. C., Lim, B. H., & Padmanabhan, S. A Passive Microwave Retrieval Algorithm with Minimal View-Angle Bias: Application to the TEMPEST-D CubeSat Mission. Journal of Atmospheric and Oceanic Technology, 2020, vol. 37, no. 2, pp. 197-210. DOI: 10.1175/JTECH-D-19-0163.1.

McCarthy, S., Crawford, S., Wood, C., Lewis, M. D., Jolliff, J. K., Martinolich, P., Ladner, S., Lawson, A., & Montes, M. Automated Atmospheric Correction of Nanosatellites Using Coincident Ocean Color Radiometer Data. Journal of Marine Science and Engineering, 2023, vol. 11, no. 3, article no. 660. DOI: 10.3390/jmse11030660.

Pathiranage, D. S., Leigh, L., & Pinto, C. T. Evaluation of Low-Cost Radiometer for Surface Reflectance Retrieval and Orbital Sensor’s Validation. Remote Sensing, 2023, vol. 15, no. 9, article no. 2444. DOI: 10.3390/rs15092444.

Mel'nik, Yu. A., Zubkovich, S. G., Stepanenko, V. D., Sokolov, Yu. P., Gubin, V. A., Dulevich, V. E., Pereslegin, S. V., Vertyagin, A. A., Glushkov, V. M., & Yurkov, Yu. A. Radiolokatsionnye metody issledovaniya Zemli [Radar methods of Earth exploration]. Moscow, Sovetskoe radio Publ., 1980. 262 p.

Volosyuk, V. K., & Kravchenko, V. F. Statisticheskaya teoriya radiotekhnicheskikh sistem distantsionnogo zondirovaniya i radiolokatsii [Statistical theory of radio-technical systems of remote sensing and radiolocation]. Moscow, Fiziko-matematicheskaya literatura Publ., 2008. 704 p.

Pandey, P., & Kakar, R. An empirical microwave emissivity model for a foam-covered sea. IEEE Journal of Oceanic Engineering, 1982, vol. 7, no. 3, pp. 135-140. DOI: 10.1109/JOE.1982.1145527.

Wei, E., & Ge, Y. A microwave emissivity model of sea surface under wave breaking. Chinese Physics, 2005, vol. 14, no. 6, article no. 1259. DOI: 10.1088/1009-1963/14/6/036.

Paloscia, S., Macelloni, G., & Santi, E. Soil Moisture Estimates From AMSR-E Brightness Temperatures by Using a Dual-Frequency Algorithm. IEEE Transactions on Geoscience and Remote Sensing, 2006, vol. 44, no. 11, pp. 3135-3144. DOI: 10.1109/TGRS.2006.881714.

Shi, J., Jiang, L., Zhang, L., Chen, K. S., Wigneron, J. P., Chanzy, A., & Jackson, T. J. Physically Based Estimation of Bare-Surface Soil Moisture With the Passive Radiometers. IEEE Transactions on Geoscience and Remote Sensing, 2006, vol. 44, no. 11, pp. 3145-3153. DOI: 10.1109/TGRS.2006.876706.

Munoz-Martin, J. F., Rodriguez-Alvarez, N., Bosch-Lluis, X., & Oudrhiri, K. Effective Surface Roughness Impact in Polarimetric GNSS-R Soil Moisture Retrievals. Remote Sensing, 2023, vol. 15, no. 8, article no. 2013. DOI: 10.3390/rs15082013.

Stepanenko, V. D., Shchukin, G. G., Bobylev, L. P., & Matrosov, S. Yu. Radioteplolokatsiya v meteorologii [Radio-thermal location in meteorology]. Leningrad, Gidrometeoizdat Publ., 1987. 280 p.

Wilheit, T. T., & Chang, A. T. C. An algorithm for retrieval of ocean surface and atmospheric parameters from the observations of the scanning multichannel microwave radiometer. Radio Science, 1980, vol. 15, no. 3, pp. 525-544. DOI: 10.1029/RS015i003p00525.

Order of the Cabinet of Ministers of Ukraine “On approval of the Concept of the National Target Scientific and Technical Space Program of Ukraine for 2021-2025” of January 13, 2021 № 15-р. (In Ukrainian).

Resolution of the Verkhovna Rada of Ukraine “On Adopting as a Basis the Draft Law of Ukraine on Approval of the National Target Scientific and Technical Space Program for 2021-2025” of November 4, 2022 № 2727-IX. (In Ukrainian).




DOI: https://doi.org/10.32620/reks.2024.1.05

Refbacks

  • There are currently no refbacks.