Eurasian Soil Science

, Volume 50, Issue 4, pp 387–395 | Cite as

The application of the piecewise linear approximation to the spectral neighborhood of soil line for the analysis of the quality of normalization of remote sensing materials

  • A. L. Kulyanitsa
  • A. D. Rukhovich
  • D. D. Rukhovich
  • P. V. Koroleva
  • D. I. Rukhovich
  • M. S. Simakova
Genesis and Geography of Soils
  • 19 Downloads

Abstract

The concept of soil line can be to describe the temporal distribution of spectral characteristics of the bare soil surface. In this case, the soil line can be referred to as the multi-temporal soil line, or simply temporal soil line (TSL). In order to create TSL for 8000 regular lattice points for the territory of three regions of Tula oblast, we used 34 Landsat images obtained in the period from 1985 to 2014 after their certain transformation. As Landsat images are the matrices of the values of spectral brightness, this transformation is the normalization of matrices. There are several methods of normalization that move, rotate, and scale the spectral plane. In our study, we applied the method of piecewise linear approximation to the spectral neighborhood of soil line in order to assess the quality of normalization mathematically. This approach allowed us to range normalization methods according to their quality as follows: classic normalization > successive application of the turn and shift > successive application of the atmospheric correction and shift > atmospheric correction > shift > turn > raw data. The normalized data allowed us to create the maps of the distribution of a and b coefficients of the TSL. The map of b coefficient is characterized by the high correlation with the ground-truth data obtained from 1899 soil pits described during the soil surveys performed by the local institute for land management (GIPROZEM).

Keywords

soil line soil science problem-oriented systems geoinformatics digital terrain models soil maps remote sensing data 

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References

  1. 1.
    A. V. Bryzzhev, D. I. Rukhovich, P. V. Koroleva, N. V. Kalinina, E. V. Vil’chevskaya, E. A. Dolinina, and S. V. Rukhovich, “Organization of retrospective monitoring of the soil cover in Azov district of Rostov oblast,” Eurasian Soil Sci. 48, 1029–1049 (2015). doi 10.1134/S1064229315100014CrossRefGoogle Scholar
  2. 2.
    M. Dubinin, Vegetation indices, GIS-Lab, 2006. http://gis-lab.info/qa/vi.html.Google Scholar
  3. 3.
    M. Dubinin, Regressive normalization of remote data survey in R, GIS-Lab, 2009. http://gis-lab.info/qa/ regress-r.html.Google Scholar
  4. 4.
    A. Kostikova, Conversion of TM and ETM+ data to radiation parameters on sensor, GIS-Lab, 2004. http://gis-lab.info/qa/dn2radiance.html.Google Scholar
  5. 5.
    A. Kostikova, Atmospheric correction of Landsat/ETM+ data (COST method), GIS-Lab, 2006. http://gis-lab.info/qa/atcor.html.Google Scholar
  6. 6.
    A. L. Kulyanitsa, P. V. Koroleva, D. I. Rukhovich, A. D. Rukhovich, D. D. Rukhovich, and M. S. Simakova, “Creation of the maps of coefficients a and b of soil line calculated for 34 Landsat images obtained on different dates,” Inf. Kosmos, No. 1, 100–114 (2016).Google Scholar
  7. 7.
    D. I. Rukhovich, M. S. Simakova, A. L. Kulyanitsa, A. V. Bryzzhev, P. V. Koroleva, N. V. Kalinina, E. V. Vil’chevskaya, E. A. Dolina, and S. V. Rukhovich, “Analysis of the application of soil maps in the system of retrospective monitoring of the state of soil cover and land use,” Pochvovedenie, No. 5, 605–625 (2015).Google Scholar
  8. 8.
    D. I. Rukhovich, A. D. Rukhovich, D. D. Rukhovich, E. V. Vil’chevskaya, G. A. Suleiman, and N. V. Kalinina, “Application of soil line for map compilation of averaged spectral deviations and their soil interpretation,” Inf. Kosmos, No. 3, 125–142 (2015).Google Scholar
  9. 9.
    D. I. Rukhovich, A. D. Rukhovich, D. D. Rukhovich, M. S. Simakova, A. L. Kulyanitsa, A. V. Bryzzhev, and P. V. Koroleva, “Maps of averaged spectral deviations from soil lines and their comparison with traditional soil maps,” Eurasian Soil Sci. 49, 739–756 (2016). doi 10.1134/S1064229316070085CrossRefGoogle Scholar
  10. 10.
    D. I. Rukhovich, A. D. Rukhovich, D. D. Rukhovich, M. S. Simakova, A. L. Kulyanitsa, A. V. Bryzzhev, and P. V. Koroleva, “The informativeness of coefficients a and b of the soil line for the analysis of remote sensing materials,” Eurasian Soil Sci. 49, 831–845 (2016). doi 10.1134/S1064229316080123CrossRefGoogle Scholar
  11. 11.
    A FAQ on vegetation in remote sensing. http://www.yale.edu/ceo/Documentation/rsvegfaq.html.Google Scholar
  12. 12.
    F. Baret, G. Guyot, and D. Major, “TSAVI: a vegetation index which minimizes soil brightness effects on LAI or APAR estimation,” 12th Canadian Symposium on Remote Sensing and IGARSS’89 (Vancouver, 1990).Google Scholar
  13. 13.
    F. Baret and G. Guyot, “Potentials and limits of vegetation indices for LAI and APAR assessment,” Remote Sens. Environ. 35, 161–173 (1991).CrossRefGoogle Scholar
  14. 14.
    R. J. Kauth and G. S. Thomas, “The tasseled cap—a graphic description of the spectral-temporal development of agricultural crops as seen by LANDSAT,” Proceedings of the Symposium on Machine Processing of Remotely Sensed Data (Purdue University of West Lafayette, Indiana, 1976), pp. 4B-41–4B-51.Google Scholar

Copyright information

© Pleiades Publishing, Ltd. 2017

Authors and Affiliations

  • A. L. Kulyanitsa
    • 1
  • A. D. Rukhovich
    • 2
  • D. D. Rukhovich
    • 2
  • P. V. Koroleva
    • 3
  • D. I. Rukhovich
    • 3
  • M. S. Simakova
    • 3
  1. 1.Natinal University of Science and Technology MISiSMoscowRussia
  2. 2.Lomonosov Moscow State UniversityMoscowRussia
  3. 3.Dokuchaev Soil Science InstituteMoscowRussia

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