Izvestiya, Atmospheric and Oceanic Physics

, Volume 54, Issue 9, pp 1141–1151 | Cite as

Remote Assessment of Spectral Reflectance of the Surface of Drained Peat Soils of Polesye on the Basis of Satellite Images of Medium Spatial Resolution

  • A. A. YanovskiyEmail author


The dependence of the spectral reflectance (averaged over an area of approximately 0.023 ha) of peat and degraded peat soils of Polesye on the soil organic carbon content has been investigated under actual field conditions for the first time. The dependence is approximated by exponential and power functions, and the confidence intervals are explicitly calculated for each parameter of the approximating functions. The parameter values for the exponential function appear better validated than the parameter values for the power function, since the corresponding confidence intervals for the former are much narrower. The values of AIC and BIC information criteria show that the power model gives a better description of experimental data for bands 1 and 2, and the exponential model gives a better description for the 3N band of the ASTER spectroradiometer.


spectral reflectance satellite remote sensing peat and degraded peat soils soil organic carbon content 



  1. 1.
    Akima, H., A method of univariate interpolation that has the accuracy of a third-degree polynomial, ACM Trans. Math. Software, 1991, vol. 17, no. 3, pp. 341–366.CrossRefGoogle Scholar
  2. 2.
    Akima, H., Gebhardt, A., Petzold, T., and Maechler, M., Akima: Interpolation of irregularly spaced data. R package version 0.5-10, 2013. http://cran.r-project. org/package=akima. Accessed May 15, 2013. Google Scholar
  3. 3.
    Archive of Meteorological Observations, Republican Center on Hydrometeorology, Radioactive Pollution Control, and Environmental Monitoring, Ministry of Natural Resources and Environmental Protection. http://www. Scholar
  4. 4.
    Arinushkina, E.V., Rukovodstvo po khimicheskomu analizu pochv (Guidebook on Chemical Analysis of Soils), Moscow: Moscow State Univ., 1970.Google Scholar
  5. 5.
    ASTER User’s Guide, Pt. 2. Level 1 Data Products (Ver.5.1), Earth Remote Sensing Data Analysis Center, March, 2007.Google Scholar
  6. 6.
    Bambalov, N.N., The limiting value of organic matter content in peat and degraded peat soils, in Innovatsionnye tekhnologii v melioratsii i sel’skokhozyaistvennom ispol’zo-vanii meliorirovannykh zemel’ (Innovation technologies in melioration and agricultural use of meliorated lands), Mat. mezhdunar. nauch.–prakt. konf. (Proceedings of the International Scientific and Practical Conference), Minsk: Belarus: IVC Minfina, 2010, pp. 19–22.Google Scholar
  7. 7.
    Brockmann Consult and contributors, VISAT Ver. 5.0, 2014. Scholar
  8. 8.
    Burnham, K.P. and Anderson, D.R., Model Selection and Multimodel Inference: A Practical Information–Theoretic Approach, New York: Springer, 2002.Google Scholar
  9. 9.
    Chance, K. and Kurucz, R., An improved high-resolution solar reference spectrum for Earth’s atmosphere measurements in the ultraviolet, visible, and near infrared, J. Quant. Spectrosc. Radiat. Transfer, 2010, vol. 111, no. 9, pp. 1289–1295.CrossRefGoogle Scholar
  10. 10.
    Chavez, P.S., An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data, Remote Sens. Environ., 1988, vol. 24, no.  3, pp. 459–479.CrossRefGoogle Scholar
  11. 11.
    Chavez, P.S., Radiometric calibration of Landsat Thematic Mapper Multispectral Images, Photogramm. Eng. Remote Sens., 1989, vol. 55, no. 9, pp. 1285–1294.Google Scholar
  12. 12.
    Cierniewski, J. and Karnieli, A., Virtual surfaces simulating the bidirectional reflectance of semi-arid soils, Int. J. Remote Sens., 2002, vol. 23, no. 19, pp. 4019–4037.CrossRefGoogle Scholar
  13. 13.
    Curcio, J.A., Evaluation of atmospheric aerosol particle size distribution from scattering measurements in the visible and infrared, J. Opt. Soc. Am., 1961, vol. 51, no. 5, pp. 548–551.CrossRefGoogle Scholar
  14. 14.
    Fox, J., The R commander: A basic statistics graphical user interface to R, J. Stat. Software, 2005, vol. 14, no. 9, pp. 1–42.Google Scholar
  15. 15.
    GeodSolve, Online geodesic calculations using the GeodSolve utility. Scholar
  16. 16.
    GRASS Development Team, Geographic Resources Analysis Support System (GRASS) Software, Ver. 6.4.5, Open Source Geospatial Foundation, 2016. http:// Scholar
  17. 17.
    Han, Y., Zhao, N., and Zhao, Y., Study on characteristics of multi-angle polarized reflection of peat, in Proc. SPIE 6752, Geoinformatics 2007, Remotely Sensed Data and Information, Ju, W. and Zhao, S., Eds., 2007, pp. 67520C-1–67520C-10.Google Scholar
  18. 18.
    Hirsch, E., Koren, I., Levin, Z., Altaratz, O., and Agassi, E., On transition-zone water clouds, Atmos. Chem. Phys., 2014, vol. 14, no. 17, pp. 9001–9012.CrossRefGoogle Scholar
  19. 19.
    Iwasaki, A. and Fujisada, H., ASTER geometric performance, IEEE Trans. Geosci. Remote Sens., 2005, vol. 43, no. 12, pp. 2700–2706.CrossRefGoogle Scholar
  20. 20.
    Koren, I., Remer, L.A., Kaufman, Y.J., Rudich, Y., and Martins, J.V., On the twilight zone between clouds and aerosols, Geophys. Res. Lett., 2007, vol. 34, no. 8, L08805.CrossRefGoogle Scholar
  21. 21.
    Levenberg, K.A., Method for the solution of certain non-linear problems in least squares, Quarter. J. Appl. Math., 1944, vol. 2, no. 2, pp. 164–168.Google Scholar
  22. 22.
    Marquardt, D., An algorithm for least-squares estimation of nonlinear parameters, SIAM J. Appl. Math., 1963, vol. 11, no. 2, pp. 431–441.CrossRefGoogle Scholar
  23. 23.
    Medvedev, A.G., Gorbliuk, A.V., Ivanov, N.P., and Shabanova, V.I., Optimization of meliorated peat soils to increase their fertility and prevent degradation, in Problemy Polesya (Problems of Polesia), Minsk: Nauka i technika, 1981, no. 7, pp. 79–86.Google Scholar
  24. 24.
    Moran, M.S., Jackson, R.D., Slater, P.N., and Teillet, P.M., Evaluation of simplified procedures for retrieval of land surface reflectance factors from satellite sensor output, Remote Sens. Environ., 1992, vol. 41, nos. 2–3, pp. 169–184.CrossRefGoogle Scholar
  25. 25.
    Nelder, J.A. and Mead, R., A simplex method for function minimization, Comput. J., 1965, vol. 7, no. 4, pp. 308–313.CrossRefGoogle Scholar
  26. 26.
    Newville, M., Stensitzki, T., Allen, D.B., and Ingargiola, A., LMFIT: Non-linear least-square minimization and curve-fitting for Python, 2014. doi 10.5281/zenodo.11813Google Scholar
  27. 27.
    Nichiporovich, Z.A., Reflectivity of main types of peat soils in Belarus, Torf. Prom-st., 1991, no. 3, pp. 13–16.Google Scholar
  28. 28.
    Orlov, D.S. and Grishina, L.A., Praktikum po himii gumusa (Practicum of Soil Ulmin Chemistry), Moscow: MGU, 1981.Google Scholar
  29. 29.
    Pidoplichko, A.P., Gorbutovich, G.D., Konoiko, M.A., and Dopotko, M.Z., Peat and sapropel deposits, in Problemy Polesya (Problems of Polesia), Minsk: Nauka i technika, 1972, no. 1, pp. 292–313.Google Scholar
  30. 30.
    Ponomareva, V.V. and Nikolaeva, T.A., Methods for the study of organic matter in peat-bog soils, Pochvoved, 1961, no. 5, pp. 88–95.Google Scholar
  31. 31.
    R Core Team, R: A language and environment for scientific computing, Vienna, Austria, 2014. Scholar
  32. 32.
    Slater, P.N., Doyle, F.J., Fritz, N.L., and Welch, R., Photographic systems for remote sensing, Man. Remote Sens. Am. Soc. Photogramm., 1983, vol. 1, no. 6, pp.  231–291.Google Scholar
  33. 33.
    Song, C., Woodcock, C.E., Seto, K.C., Lenney, M.P., and Macomber, S.A., Classification and change detection using Landsat TM data: When and how to correct atmospheric effects?, Remote Sens. Environ., 2001, vol. 75, no. 2, pp. 230–244.CrossRefGoogle Scholar
  34. 34.
    Spiess, A.N. and Neumeyer, N., An evaluation of R2 as an inadequate measure for nonlinear models in pharmacological and biochemical research: A Monte Carlo approach, BMC Pharmacol., 2010, vol. 10, no. 1, pp. 1–11.CrossRefGoogle Scholar
  35. 35.
    Teillet, P.M. and Fedosejevs, G., On the dark target approach to atmospheric correction of remotely sensed data, Can. J. Remote Sens., 1995, vol. 21, pp. 373–387.CrossRefGoogle Scholar
  36. 36.
    Tsytron, G.S., Smeyan, N.I., Bubnova, T.V., Sergeenko, V.T., and Azarenok, T.N., Reflectivity of man-made organic soils of Belorus Polesia and their diagnostics, Pochvoved. Agrokhim., 2007, no. 1, pp. 84–91.Google Scholar
  37. 37.
    Tsytron, G.S., Bubnova, T.V., Sergeenko, V.T., and Azarenok, T.N., Diagnostics of man-made organic soils by their reflectivity, in Tr. V mezhdunar. konf. Evolyutsiya pochvennogo pokrova: istoriya idei i metody, golotsennaya evolyutsuya, prognozy (Proceedings of the V International Conference on Soil Cover Evolution: The History of Ideas and Methods, Holocene Evolution and Forecasts), Ivanov, I.V. and Pesochin, L.S., Eds., Pushchino, 2009, pp. 102–104.Google Scholar
  38. 38.
    Tsytron, G.S., Azarenok, T.N., Kalyuk, V.A., and Bubnova, T.V., On the problem of diagnostics of residually gleyed degro-peat soils], Zemlyarobstva Ahova Raslin, 2011, no. 6, pp. 33–36.Google Scholar
  39. 39.
    Tucker, C.J., Red and photographic infrared linear combinations for monitoring vegetation, Remote Sens. Environ., 1979, vol. 8, no. 2, pp. 127–150.CrossRefGoogle Scholar
  40. 40.
    Tyurin, I.V., Newly modified volumetric method of humus determination using chromic acid, Pochvoved, 1931, no. 6, pp. 36–47.Google Scholar
  41. 41.
    Van der Walt, S., Colbert, S.C., and Varoquaux, G., The NumPy Array: A structure for efficient numerical computation, Comp. Sci. Eng., 2011, vol. 13, pp. 22–30.CrossRefGoogle Scholar
  42. 42.
    Wilson, R.T., Py6S: A Python interface to the 6S radiative transfer model, Comput. Geosci., 2012, vol. 51, pp. 166–171.CrossRefGoogle Scholar
  43. 43.
    Zhumar’, A.Yu., Kovalev, A.A., Kononovich, S.I., Lisitsa, V.D., Pluta, V.E., Smeyan, N.I., Sergeenko, V.T., and Yanovskaya, E.A., Issledovanie opticheskih i fiziko-himicheskih svoystv pochv Belarusi (Study of Optical and Physicochemical Properties of Soils in Belarus), vol. 1: Katalog spektral’nyh i fiziko-himicheskih svoystv pochv Belarusi (Catalog of Spectral and Physicochemical Properties of Soils in Belarus), Minsk, 1992.Google Scholar

Copyright information

© Pleiades Publishing, Ltd. 2018

Authors and Affiliations

  1. 1.Institute for Nature Management, National Academy of Sciences of BelarusMinskBelarus

Personalised recommendations