Enhancing the Spatial Resolution of the AMSR2 C-Band Radiometer Channels for Monitoring the Arctic Seas Using the 36.5 Ghz Measurement Channels

E. V. Zabolotskikh1, ✉, B. Chapron2

1 Russian State Hydrometeorological University, Saint Petersburg, Russian Federation

2 Institut Français de Recherche pour l’Exploitation de la Mer, Ifremer, Plouzané, France

e-mail: liza@rshu.ru

Abstract

Purpose. The purpose of the work is to study the potential for improving the spatial resolution of the Advanced Microwave Scanning Radiometer 2 (AMSR2) on C-band channels using the measurements at 36.5 GHz frequency to analyze the underlying surface parameters of the Arctic seas.

Methods and Results. The results of numerical modeling of the brightness temperature (Тb) of microwave radiation from the sea ice-ocean-atmosphere system under conditions of the non-scattering Arctic atmosphere, as well as the calculations of Тb for the atmospheric and oceanic parameters based on the ERA5 reanalysis data were used. The values of sea ice effective emission coefficients previously calculated for the entire Arctic region were applied in calculations. The Тb sensitivity to atmospheric radiation, surface temperature and underlying surface emission coefficients at the 6.9 and 36.5 GHz measurement channels at the horizontal and vertical polarizations, and also variability of the above parameters in the Arctic were assessed. Depending on the surface type and based on the modeling results, proposed are the formulas for calculating the Тb fields at the 6.9 GHz frequency at the enhanced spatial resolution, in which Тb at the 6.9 GHz frequency of the original resolution and Тb at the 36.5 GHz frequency of the Level 1R satellite product were used.

Conclusions. The developed approach expands the possibilities of using the AMSR2 data for a detailed study of the parameters of Arctic seas partially or completely covered with first-year ice of various types and concentration. For the areas of continuous multi-year consolidate ice, application of the described method to improving the spatial resolution of measurements at 6.9 GHz, especially in vertical polarization, seems impossible. The errors and applicability of the above approach are conditioned by the variability of atmospheric water content parameters in a low spatial resolution pixel of measurements at the 6.9 GHz frequency.

Keywords

AMSR2, brightness temperature, spatial resolution, Arctic, sea ice-ocean-atmosphere system, physical modeling, sea ice

Acknowledgements

The study was carried out within the framework of state assignment of the Ministry of Science and Higher Education of RF No. FSZU-2025-0005 “The Arctic Sea ice-ocean-atmosphere system: Development of satellite methods and models”.

Original russian text

Original Russian Text © E. V. Zabolotskikh, B. Chapron, 2025, published in MORSKOY GIDROFIZICHESKIY ZHURNAL, Vol. 41, Iss. 5, pp. 694–714 (2025)

For citation

Zabolotskikh, E.V. and Chapron, B., 2025. Enhancing the Spatial Resolution of the AMSR2 C-Band Radiometer Channels for Monitoring the Arctic Seas Using the 36.5 Ghz Measurement Channels. Physical Oceanography, 32(5), pp. 703-722.

References

  1. Zabolotskikh, E.V. and Chapron, B., 2017. Atmospheric Total Water Vapor Content Retrieval Using Satellite Microwave Radiometer Measurements of AMSR2. Sovremenniye Problemy Distantsionnogo Zondirovaniya Zemli iz Kosmosa, 14(1), pp. 207-215. https://doi.org/10.21046/2070-7401-2017-14-1-207-215 (in Russian).
  2. Greenwald, T.J., Stephens, G.L., Vonder Haar, T.H. and Jackson, D.L., 1993. A Physical Retrieval of Cloud Liquid Water over the Global Oceans Using Special Sensor Microwave/Imager (SSM/I) Observations. Journal of Geophysical Research: Atmospheres, 98(D10), pp. 18471-18488. https://doi.org/10.1029/93JD00339
  3. Wentz, F.J., Gentemann, C., Smith, D. and Chelton, D., 2000. Satellite Measurements of Sea Surface Temperature through Clouds. Science, 288(5467), pp. 847-850. https://doi.org/10.1126/science.288.5467.847
  4. Comiso, J.C., 2009. Enhanced Sea Ice Concentrations and Ice Extents from AMSR-E Data. Journal of the Remote Sensing Society of Japan, 29(1), pp. 199-215. https://doi.org/10.11440/rssj.29.199
  5. Xing, D., Hou, J., Huang, C. and Zhang, W., 2022. Estimation of Snow Depth from AMSR2 and MODIS Data Based on Deep Residual Learning Network. Remote Sensing, 14(20), 5089. https://doi.org/10.3390/rs14205089
  6. Lu, M., Hoang, K.O. and Kumarasiri, A.D.T.N., 2024. Temperature Effects in AMSR2 Soil Moisture Products and Development of a Removal Method Using Data at Ascending and Descending Overpasses. Remote Sensing, 16(9), 1606. https://doi.org/10.3390/rs16091606
  7. Lenti, F., Nunziata, F., Estatico, C. and Migliaccio, M., 2013. On the Spatial Resolution Enhancement of Microwave Radiometer Data in Banach Spaces. IEEE Transactions on Geoscience and Remote Sensing, 52(3), pp. 1834-1842. https://doi.org/10.1109/TGRS.2013.2255614
  8. Long, D.G., Brodzik, M.J. and Hardman, M.A., 2019. Enhanced-Resolution SMAP Brightness Temperature Image Products. IEEE Transactions on Geoscience and Remote Sensing, 57(7), pp. 4151-4163. https://doi.org/10.1109/TGRS.2018.2889427
  9. Sethmann, R., Burns, B.A. and Heygster, G.C., 1994. Spatial Resolution Improvement of SSM/I Data with Image Restoration Techniques. IEEE Transactions on Geoscience and Remote Sensing, 32(6), pp. 1144-1151. https://doi.org/10.1109/36.338362
  10. Imaoka, K., Maeda, T., Kachi, M., Kasahara, M., Ito, N. and Nakagawa, K., 2012. Status of AMSR2 Instrument on GCOM-W1. In: SPIE, 2012. Proceedings of SPIE. Bellingham, USA: SPIE. Vol. 8528, Earth Observing Missions and Sensors: Development, Implementation, and Characterization II. Kyoto, Japan, 852815. https://doi.org/10.1117/12.977774
  11. Wang, Y., Shi, J., Jiang, L., Du, J. and Tian, B., 2011. The Development of an Algorithm to Enhance and Match the Resolution of Satellite Measurements from AMSR-E. Science China Earth Sciences, 54(3), pp. 410-419. https://doi.org/10.1007/s11430-010-4074-0
  12. Maeda, T., Tomii, N., Seki, M., Sekiya, K., Taniguchi, Y. and Shibata, A., 2021. Validation of Hi-Resolution Sea Surface Temperature Algorithm toward the Satellite-Borne Microwave Radiometer AMSR3 Mission. IEEE Geoscience and Remote Sensing Letters, 19, 4500305. https://doi.org/10.1109/LGRS.2021.3066534
  13. Nunziata, F., Alparone, M., Camps, A., Park, H., Zurita, A.M., Estatico, C. and Migliaccio, M., 2021. An Enhanced Resolution Brightness Temperature Product for Future Conical Scanning Microwave Radiometers. IEEE Transactions on Geoscience and Remote Sensing, 60, 5301812. https://doi.org/10.1109/TGRS.2021.3109376
  14. Choi, M. and Hur, Y., 2012. A Microwave-Optical/Infrared Disaggregation for Improving Spatial Representation of Soil Moisture Using AMSR-E and MODIS Products. Remote Sensing of Environment, 124, pp. 259-269. https://doi.org/10.1016/j.rse.2012.05.009
  15. Santi, E., 2010. An Application of the SFIM Technique to Enhance the Spatial Resolution of Spaceborne Microwave Radiometers. International Journal of Remote Sensing, 31(9), pp. 2419-2428. https://doi.org/10.1080/01431160903005725
  16. Long, D.G., Hardin, P.J. and Whiting, P.T., 1993. Resolution Enhancement of Spaceborne Scatterometer Data. IEEE Transactions on Geoscience and Remote Sensing, 31(3), pp. 700-715. https://doi.org/10.1109/36.225536
  17. Hu, W., Yao, Z., Chen, S., Xu, Z., Liu, Y., Feng, Z. and Ligthart, L., 2022. Spatial Resolution and Data Integrity Enhancement of Microwave Radiometer Measurements Using Total Variation Deconvolution and Bilateral Fusion Technique. Remote Sensing, 14(14), 3502. https://doi.org/10.3390/rs14143502
  18. Li, Y., Hu, W., Chen, S., Zhang, W., Guo, R., He, J. and Ligthart L., 2019. Spatial Resolution Matching of Microwave Radiometer Data with Convolutional Neural Network. Remote Sensing, 11(20), 2432. https://doi.org/10.3390/rs11202432
  19. Liu, J.G., 2000. Smoothing Filter-Based Intensity Modulation: A Spectral Preserve Image Fusion Technique for Improving Spatial Details. International Journal of Remote Sensing, 21(18), pp. 3461-3472. https://doi.org/10.1080/014311600750037499
  20. Santi, E., Brogioni, M., Macelloni, G., Paloscia, S., Pampaloni, P. and Pettinato, S., 2008. A Simple Technique to Improve the AMSR-E Spatial Resolution at C-Band. In: IEEE, 2008. 2008 Microwave Radiometry and Remote Sensing of the Environment. Florence, Italy, pp. 1-4. https://doi.org/10.1109/MICRAD.2008.4579458
  21. Meissner, T. and Wentz, F.J., 2012. The Emissivity of the Ocean Surface between 6 and 90 GHz over a Large Range of Wind Speeds and Earth Incidence Angles. IEEE Transactions on Geoscience and Remote Sensing, 50(8), pp. 3004-3026. https://doi.org/10.1109/TGRS.2011.2179662
  22. Matrosov, S.Yu. and Shulgina, E.M., 1981. Scattering and Attenuation of Microwave Radiation by Precipitation. Trudy Glavnoi Goefizicheskoi Observatorii im. A.I. Voeikova, 448, pp. 85-94 (in Russian).
  23. Svendsen, E., Matzler, C. and Grenfell, T.C., 1987. A Model for Retrieving Total Sea Ice Concentration from a Spaceborne Dual-Polarized Passive Microwave Instrument Operating near 90 GHz. International Journal of Remote Sensing, 8(10), pp. 1479-1487. https://doi.org/10.1080/01431168708954790
  24. Zabolotskikh, E.V. and Chapron, B., 2024. Estimation of Atmospheric Microwave Radiation Parameters over the Arctic Sea Ice from the AMSR2 Data. IEEE Transactions on Geoscience and Remote Sensing, 62, 4104211. https://doi.org/10.1109/TGRS.2024.3392369
  25. Ulaby, F.T., Moore, R.K. and Fung, A.K., 1981. Microwave Remote Sensing: Active and Passive. Volume 1. Microwave Remote Sensing Fundamentals and Radiometry. Reading, MA: Addison-Wesley Publishing Co., 456 p.
  26. Zabolotskikh, E. and Azarov, S., 2022. Wintertime Emissivities of the Arctic Sea Ice Types at the AMSR2 Frequencies. Remote Sensing, 14(23), 5927. https://doi.org/10.3390/rs14235927
  27. Zabolotskikh, E.V., Balashova, E.A. and Chapron B., 2019. Advanced Method for Sea Ice Concentration Retrieval from Satellite Microwave Radiometer Measurements at Frequencies near 90 GHz. Sovremenniye Problemy Distantsionnogo Zondirovaniya Zemli iz Kosmosa, 16(4), pp. 233-243. https://doi.org/10.21046/2070-7401-2019-16-4-233-243 (in Russian).
  28. Zabolotskikh, E.V., Zhivotovskaia, M.A., Lvova, E.V. and Yarusov, K.I., 2024. Arctic Sea Ice Classification Using AMSR2 Data. Sovremenniye Problemy Distantsionnogo Zondirovaniya Zemli iz Kosmosa, 21(5), pp. 263-274. https://doi.org/10.21046/2070-7401-2024-21-5-263-274 (in Russian).

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