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Izvestiya, Atmospheric and Oceanic Physics

, Volume 54, Issue 9, pp 1272–1281 | Cite as

2015–2016 Seasonal Variations of Backscattering from Natural Coverage in the Moscow Region Based on Radar Data from the Sentinel 1A Satellite

  • N. V. RodionovaEmail author
PHYSICAL PRINCIPLES OF EARTH STUDIES FROM SPACE

Abstract

In this paper, we discuss the time series of the coefficient of backscattering from forest cover and unused land in the Moscow region based on 29 C-band sessions of the Sentinel 1A satellite over a one-year period from March 10, 2015 to March 4, 2016. The data were interpreted using the MIMICS model of the change in the permittivity of the elements of the forest cover and the model of the change in the permittivity of the soil. The seasonal features of the change in backscattering from the forest using two polarizations of VV and VH are shown for the Losinyi Ostrov forest. A strong positive correlation was found between values ​​of the backscattering coefficient of the forest and values of air temperature. Using the MIMICS model, we were also able to relate the annual changes in the backscattering coefficient of the forest to changes in the permittivity of the trees dependent on temperature. The soil moisture content of unused land in the vicinity of Fryazino, Moscow Region was estimated based on the radar data for 2015–2016. Data on soil moisture can be extracted using a single equation of Dubois et al., 1995 for consistent polarization and regression of Rao et al., 2003 under a number of conditions. The time series of the values ​​of the equivalent water layer for the studied field is shown for the snow period.

Keywords:

radar image multi-time data backscattering coefficient permittivity soil moisture air temperature Pearson correlation coefficient 

Notes

REFERENCES

  1. 1.
    Arslan, A.N., Pulliainen, J., and Hallikainen, M., Observations of L-band and C-band backscatter and a semi-empirical backscattering model approach from a forest–snow–ground system, Prog. Electromagn. Res., 2006, vol. 56, pp. 263–281.CrossRefGoogle Scholar
  2. 2.
    Besic, N., Vasile, G., Chanussot, J., Stankovic, S., and Ovarlez, J.-P., D’urso, G., Boldo, D., Dedieu, J.-P., Stochastically based wet snow mapping with SAR data, in IEEE Int. Grs Symp. (IGARSS’2012), Munich: Germany, 2012, pp. 4859–5862.Google Scholar
  3. 3.
    Bourgeau-Chavez, L.L., Kasischke, E.S., and Ruther-ford, M.D., Evaluation of ERS SAR data for prediction of fire danger in a boreal region, Int. J. Wildland Fire, 1999, vol. 9, no. 3, pp. 183–194.CrossRefGoogle Scholar
  4. 4.
    Dubois, P.C., van Zyl, J.J., and Engman, T., Measuring soil moisture with imaging radars, IEEE Trans. Geosci. Remote Sens., 1995, vol. 33, no. 4, pp. 916–926.Google Scholar
  5. 5.
    Fung, A.K., Comparison of model predictions with backscattering and emission measurements from snow and ice, in Microwave Scattering and Emission Models and Their Applications, Boston: Artech, 1994, pp. 425–450.Google Scholar
  6. 6.
    Gareth Rees, W., Remote Sensing of Snow and Ice, CRC Press, 2006.Google Scholar
  7. 7.
    Kalinkevich, A.A., Krylova, M.S., Masyuk, V.M., Kakovkina, A.Yu., and Khromets, E.A., History of the measurement of “living” wood permittivity), in Proc. of the Third All-Russian Conference “Ultra-Wideband Signals in Radiolocation and Communication”, Murom, 2010, pp. 169–174.Google Scholar
  8. 8.
    Koskinen, J.T., Pulliainen, J.T., Luojus, K.P., and Takala, M., Monitoring of snow-cover properties during the spring melting period in forested areas, IEEE Trans. Geosci. Remote Sens., 2010, vol. 48, pp. 50–58.CrossRefGoogle Scholar
  9. 9.
    Magagi, R., Bernier, M., and Bouchard, M.C., Use of ground observations to simulate the seasonal changes in the backscattering coefficient of the subarctic forest, IEEE Trans. Geosci. Remote Sens., 2002, vol. 40, pp. 281–297.CrossRefGoogle Scholar
  10. 10.
    Mätzler, C., Applications of the interaction of microwaves with the natural snow cover, Remote Sens. Rev., 1987, vol. 2, pp. 259–387.CrossRefGoogle Scholar
  11. 11.
    Mätzler, C., Microwave permittivity of dry snow, IEEE Trans. Geosci. Remote Sens., 1996, vol. 34, no. 2, pp. 573–581.CrossRefGoogle Scholar
  12. 12.
    Mironov, V.L., Kosolapova, L.G., and Savin, I.V., Dielectric model of thawed and frozen organic soil at the AMSR radiometer frequency, Issled. Zemli Kosmosa, 2015, no. 5, pp. 9–15.Google Scholar
  13. 13.
    Natsional’nye parki Rossii (National Parks in Russia), Moscow: Tsentr Okhrany Dikoi Prirody, 1996.Google Scholar
  14. 14.
    Nagler, T. and Rott, H., Retrieval of wet snow by means of multitemporal SAR data, IEEE Trans. Geosci. Remote Sens., 2000, vol. 38, no. 2, pp. 754–765.CrossRefGoogle Scholar
  15. 15.
    Pettinato, S., Poggi, P., Macelloni, G., Paloscia, S., Pampaloni, P., and Crepaz, A., Mapping snow cover in Alpine areas with ENVISAT/SAR images, in Proc. Envisat and ERS Symp., September 6–10, Salzburg, Austria, 2004.Google Scholar
  16. 16.
    Rao, S.S., Kumar, S.D., Das, S.N., Nagaraju, M.S.S., Venugopal, N.V., Rajankar, P., Laghate, P., Reddy, M.S., Joshi, A.K., and Sharma, J.R., Modified Dubois model for estimating soil moisture with dual polarized SAR data, J. Indian Soc. Remote Sens., 2013, vol. 41, no. 4, pp. 865–872. doi 10.1007/s12524-013-0274-3CrossRefGoogle Scholar
  17. 17.
    Rignot, E., Way, J.B., McDonald, K., Vierck, L., Williams, C., Adams, P., Payne, C., Wood, W., and Shi, J., Monitoring of environmental conditions in taiga using ERS-1 SAR, Remote Sens. Environ., 1994, vol. 49, pp. 145–154.CrossRefGoogle Scholar
  18. 18.
    Rott, H. and Matzler, C., Possibilities and limits of synthetic aperture radar for snow and glacier surveying, Ann. Glaciol., 1987, vol. 9, pp. 195–199.CrossRefGoogle Scholar
  19. 19.
    Rykhus, R. and Lu, Z., Monitoring a boreal wildfire using multitemporal Radarsat-1 intensity and coherence images, Geomatics, Nat. Hazards Risk, 2011, vol. 2, no. 1, pp. 15–32.CrossRefGoogle Scholar
  20. 20.
    Salcedo, A.P., Estimation of snow parameters (SWE and SCA) and sea ice monitoring using SAR data, 2010. http://aulavirtual.ig.conae.gov.ar/moodle/pluginfile.php/ 513/mod_page/content/71/Seminario_final_Salcedo.pdf.Google Scholar
  21. 21.
    Tadono, T., Fukami, K., and Shi, J., Estimation of snow hydrological parameters using single-parameter, multi-temporal SAR images, Geosci. Remote Sens. Symp. (IGARSS’01), 2001.Google Scholar
  22. 22.
    Thiel, Ch., Thiel, Ca., Reiche, J., Leiterer, R., and Schmullius, C., Analysis of ASAR and PALSAR data for optimising forest cover mapping—A GSE forest monitoring study, in Conf. ForestSat2007, 5–7 November, 2007. Montpellier, 2007Google Scholar
  23. 23.
    Topp, G.C., Davis, J.L., and Annan, A.P., Electromagnetic determination of soil water content: Measurements in coaxial transmission lines, Water Resour. Res., 1980, vol. 16, no. 3, pp. 574–582.CrossRefGoogle Scholar
  24. 24.
    Ulaby, F.T., Moore, R.K., and Fung, A.K., Active microwave sensing of land, in Microwave Remote Sensing, Active and Passive: From Theory to Applications, Boston: Artech, 1986.Google Scholar
  25. 25.
    Ulaby, F.T., Sarabandi, K., McDonald, K., Whitt, M., and Dobson, D.C., Michigan microwave canopy scattering model, Int. J. Remote Sens., 1990, vol. 11, pp. 1223–1254.CrossRefGoogle Scholar
  26. 26.
    Van Doninck, J., Peters, J., Lievens, H., De Baets, B., and Verhoest, N.E.C., Accounting for seasonality in a soil moisture change detection algorithm for ASAR Wide Swath time series, Hydrol. Earth Syst. Sci., 2012, vol. 16, pp. 773–786. doi 10.5194/hess‑16–773–2012CrossRefGoogle Scholar
  27. 27.
    Walker, J.P., Panciera, R., and Monerris, A., Basis of an Australian radar soil moisture algorithm theoretical baseline document, Melbourne: Monash Univ., 2013. http://users.monash.edu.au/~jpwalker/reports/ GARADA_Deliverable%232.pdf.Google Scholar
  28. 28.
    Wegmüller, U., The effect of freezing and thawing on the microwave signatures of bare soil, Remote Sens. Environ., 1990, vol. 33, pp. 123–135.CrossRefGoogle Scholar

Copyright information

© Pleiades Publishing, Ltd. 2018

Authors and Affiliations

  1. 1.Institute of Radioengineering and Electronics, Russian Academy of SciencesFryazinoRussia

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