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Indicative capacity of NDVI in predictive mapping of the properties of plow horizons of soils on slopes in the south of Western Siberia

  • Agricultural Chemistry and Soil Fertility
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Abstract

The informativeness of NDVI for predictive mapping of the physical and chemical properties of plow horizons of soils on different slope positions within the first (280–310 m a.s.l.) and second (240–280 m a.s.l.) altitudinal steps has been examined. This index is uninformative for mapping soil properties in small hollows, whose factual width is less than the Landsat image resolution (30 m). In regression models, NDVI index explains 52% of variance in the content of humus; 35 and 24% of variance in the contents of total and nitrate nitrogen; 19 and 29% of variance in the contents of total and available phosphorus; 25 and 50% of variance in the contents of exchangeable calcium and manganese; and 30 and 29% of variance in the contents of fine silt and soil water, respectively. On the basis of the models obtained, prognostic maps of the soil properties have been developed. Spatial distribution patterns of NDVI calculated from Landsat 8 images (30-m resolution) serve as the cartographic base and the main indicator of the soil properties. The NDVI values and the contents of humus, physical clay (<0.01 mm) and fine silt particles, total and nitrate nitrogen, total phosphorus, and exchangeable calcium and manganese in the soils of the first altitudinal step are higher than those in the soils of the second altitudinal step. An opposite tendency has been found for the available phosphorus content: in the soils of the second altitudinal step and the hollow, its content is higher than that in the soils of the first altitudinal step by 1.8 and 2.4 times, respectively. Differences in the pH of soil water suspensions, easily available phosphorus, and clay in the soils of the compared topographic positions (first and second altitudinal steps and the hollow) are statistically unreliable.

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References

  1. Agrochemical Methods of Soil Studies (Nauka, Moscow, 1975) [in Russian].

  2. V. N. Antonov and L. A. Sladkikh, “Monitoring of crops and prediction of the yield of winter wheat on the basis of remote sensing data,” Geomatika, No. 4 (5), 50–53 (2009).

    Google Scholar 

  3. V. N. Afanas’ev and A. P. Tsypin, Econometrics in STATISTICA Software: Manual for Laboratory Practicum (Orenburg State Univ., Orenburg, 2008) [in Russian].

    Google Scholar 

  4. N. V. Gopp, “Algorithm for compilation of digital soil maps based on field, laboratory, and satellite data,” Issled. Zemli Kosmosa, No. 3, 58–72 (2013).

    Google Scholar 

  5. N. V. Gopp, “Modeling of the reserves of aboveground phytomass of tundra communities using ground and satellite data,” Gorn. Inf.-Anal. Byull. 17 (12), 200–205 (2009).

    Google Scholar 

  6. N. V. Gopp, O. A. Savenkov, T. V. Nechaeva, and V. V. Smirnov, “Influence of morphometric parameters of the relief on spatial variability of the content of exchangeable potassium in a typical agrogray soil,” Agrokhimiya, No. 5, 54–63 (2014).

    Google Scholar 

  7. S. M. Davis, D. A. Landgrebe, T. L. Phillips, P. H. Swain, R. M. Hoffer, J. C. Lindenlaub, and L. F. Silva, Remote Sensing: The Quantitative Approach, Ed. by P. H. Swain and S. M. Davis (McGraw-Hill, New York, 1978; Nedra, Moscow, 1983).

  8. A. J. Gerrard, Soils and Landforms: An Integration of Geomorphology and Pedology (Unwin Hyman, London, 1982; Nedra, Leningrad, 1984).

    Google Scholar 

  9. N. R. Draper and H. Smith, Applied Regression Analysis (Wiley, New York, 1981; Finansy i Statistika, Moscow, 1986).

    Google Scholar 

  10. P. Duchaufour, Précis de Pédologie, L'Évolution des Sols (Masson et Cie, Paris, 1965; Progress, Moscow, 1970).

    Google Scholar 

  11. T. I. Evdokimova, Soil Survey (Moscow State Univ., Moscow, 1987) [in Russian].

    Google Scholar 

  12. T. V. Zvonkova, Applied Geomorphology (Vysshaya Shkola, Moscow, 1970) [in Russian].

    Google Scholar 

  13. A Map of Agroindustrial Soil Groups and Recommendations for Their Use by Rassvet Collective Farm, Toguchinskii District, Novosibirsk Oblast, Scale 1: 25000 (Zapsibgiprozem, Novosibirsk, 1978) [in Russian].

  14. A. N. Kashtanov and V. E. Yavtushenko, Agroecology of Soils on Slopes (Kolos, Moscow, 1997) [in Russian].

    Google Scholar 

  15. Classification and Diagnostics of Soils of the Soviet Union (Kolos, Moscow}, 1977) [in Russian]

  16. L. L. Shishov, V. D. Tonkonogov, I. I. Lebedeva, and M. I. Gerasimova, Classification and Diagnostic System of Russian Soils (Oikumena, Smolensk, 2004) [in Russian].

    Google Scholar 

  17. O. A. Luchitskaya and V. N. Bashkin, “Influence of relief on soil fertility,” Agrokhimiya, No. 5, 109–114 (1995).

    Google Scholar 

  18. Methodology of Multiple Monitoring of Agricultural Land Fertility (Rosinformagrotekh, Moscow, 2003) [in Russian].

  19. V. M. Nazaryuk, O. A. Savenkov, and N. V. Smirnova, “Substantiation and analysis of the fertility parameters of soils and plant productivity for the modeling of nitrogen cycle in agroecosystems,” Sib. Ekol. Zh. 11 (3), 391–401 (2004).

    Google Scholar 

  20. A. D. Orlov, Erosion and Erosion-Prone Lands in Western Siberia (Nauka, Novosibirsk, 1983) [in Russian].

    Google Scholar 

  21. Field Guide for Correlation of Russian Soils (Dokuchaev Soil Science Institute, Moscow, 2008) [in Russian].

  22. Soil-Agrochemical Problems of Agricultural Intensification in Siberia (Siberian Scientific Research Institute of Agriculture, Novosibirsk, 1989) [in Russian].

  23. I. Yu. Savin, E. R. Tanov, and S. Kharzinov, “Use of vegetation index NDVI for evaluation of arable soil quality in Baksan district (Kabardino-Balkaria Republic),” Byull. Pochv. Inst. im. V.V. Dokuchaeva, No. 77, 51–65 (2015).

    Google Scholar 

  24. A. S. Cherepanov, “Vegetation indices,” Geomatika, No. 2, 98–102 (2011).

    Google Scholar 

  25. Econometrics: Methodological Guidelines on the Study Course and on Practical Works using Computer (Vuzovskii Uchebnik, Moscow, 2005) [in Russian].

  26. V. E. Yavtushenko and N. B. Makarov, “Losses of organic matter and nutrients as the result of water erosion,” Agrokhimiya, No. 4, 117–123 (1996).

    Google Scholar 

  27. O. P. Yakutina, T. V. Nechaeva, and N. V. Smirnova, “Regime of major nutrients and plant productivity on eroded soils in the south of Western Siberia,” Probl. Agrokhim. Ekol., No. 1, 16–22 (2011).

    Google Scholar 

  28. C. Ballabio, F. Fava, and A. Rosenmund, “A plant ecology approach to digital soil mapping, improving the prediction of soil organic carbon content in alpine grasslands,” Geoderma 1, 102–116 (2012).

    Article  Google Scholar 

  29. N. V. Gopp, “Soils of the southwestern part of the Dzhulukul Depression in the Altai Republic,” Eurasian Soil Sci. 48, 567–577 (2015).

    Article  Google Scholar 

  30. P. J. Curran, “Multispectral remote sensing of vegetation amount,” Progr. Phys. Geogr. 4, 315–341 (1980).

    Article  Google Scholar 

  31. A. R. Huete and H. Q. Liu, “An error and sensitivity analysis of the atmospheric-and soil-correcting variants of the NDVI for the MODIS-EOS,” IEEE Trans. Geosci. Remote Sens. 32, 897–905 (1994).

    Article  Google Scholar 

  32. IUSS Working Group WRB, World Reference Base for Soil Resources, International Soil Classification System for Naming Soils and Creating Legends for Soil Maps, World Soil Resources Reports No. 106 (Food and Agriculture Organization, Rome, 2014).

  33. P. Kumar, P. C. Pandey, B. K. Singh, S. Katiyar, V. P. Mandal, M. Rani, V. Tomarf, and S. Patairiya, “Estimation of accumulated soil organic carbon stock in tropical forest using geospatial strategy,” Egypt. J. Remote Sens. Space Sci. 19, 109–123 (2016).

    Google Scholar 

  34. S. Lamsal, S. Grunwald, G. L. Bruland, C. M. Bliss, and N. B. Comerford, “Regional hybrid geospatial modeling of soil nitrate–nitrogen in the Santa Fe River watershed,” Geoderma 135, 233–247 (2006).

    Article  Google Scholar 

  35. H. Laurila, M. Karjalainen, J. Kleemola, and J. Hyyppä, “Cereal yield modeling in Finland using optical and radar remote sensing,” Remote Sens. 2, 2185–2239 (2010).

    Article  Google Scholar 

  36. D. F. Lozano-Garcia, R. N. Fernandez, and C. J. Johannsen, “Assessment of regional biomass-soil relationships using vegetation indexes,” IEEE Trans. Geosci. Remote Sens. 29 (2), 331–339 (1991).

    Article  Google Scholar 

  37. N. J. McKenzie and P. J. Ryan, “Spatial prediction of soil properties using environmental correlation,” Geoderma 89, 67–94 (1999).

    Article  Google Scholar 

  38. A. Mondal, D. Khare, S. Kundu, S. Mondal, S. Mukherjee, and A. Mukhopadhyay, “Spatial soil organic carbon (SOC) prediction by regression kriging using remote sensing data,” Egypt. J. Remote Sens. Space Sci., (2016). http://dx.doi.org/ doi 10.1016/ j.ejrs.2016.06.004

    Google Scholar 

  39. Precision Agriculture, Ed. by J. V. Stafford (Wageningen Akademic, Wageningen, 2005).

  40. R. G. Rivero, S. Grunwald, S. Newman, T. Z. Osborne, and K. R. Reddy, “Incorporation of ASTER satellite imagery into multivariate geostatistical models to predict soil phosphorus,” Biannual Meeting of Commission 1.5 Pedometrics (International Union of Soil Science Naples, Fl, 2005), pp. 75–76.

    Google Scholar 

  41. J. W. Rouse, R. H. Haas, J. A. Schell, and D. W. Deering, “Monitoring vegetation systems in the Great Plains with ERTS,” Third Earth Resources Technology Satellite-1 Symposium, NASA SP-351 I (Washington, 1973), 309–317.

    Google Scholar 

  42. P. A. Shary, L. S. Sharaya, and A. V. Mitusov, “Fundamental quantitative methods of land surface analysis,” Geoderma 107 (1–2), 1–32 (2002).

    Article  Google Scholar 

  43. K. Sumfleth and R. Duttmann, “Prediction of soil property distribution in paddy soil landscapes using terrain data and satellite information as indicators,” Ecol. Indic. 8 (5), 485–501 (2008). http://dx.doi.org/. doi 10.1016/j.ecolind.2007.05.005

    Article  Google Scholar 

  44. O. P. Yakutina, T. V. Nechaeva, and N. V. Smirnova, “Consequences of snowmelt erosion: soil fertility, productivity, and quality of wheat on greyzemic phaeozem in the south of West Siberia,” Agric., Ecosyst. Environ. 200, 88–93 (2015).

    Article  Google Scholar 

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Authors and Affiliations

  1. Institute of Soil Science and Agricultural Chemistry, Siberian Branch of the Russian Academy of Sciences, prosp. Akad. Lavrent’eva 8/2, Novosibirsk, 630090, Russia

    N. V. Gopp, T. V. Nechaeva, O. A. Savenkov & N. V. Smirnova

  2. Institute of Computational Technologies, Siberian Branch of the Russian Academy of Sciences, prosp. Akad. Lavrent’eva 6, Novosibirsk, 630090, Russia

    V. V. Smirnov

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  1. N. V. Gopp
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  2. T. V. Nechaeva
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  3. O. A. Savenkov
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  4. N. V. Smirnova
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Correspondence to N. V. Gopp.

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Original Russian Text © N.V. Gopp, T.V. Nechaeva, O.A. Savenkov, N.V. Smirnova, V.V. Smirnov, 2017, published in Pochvovedenie, 2017, No. 11, pp. 1377–1389.

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Gopp, N.V., Nechaeva, T.V., Savenkov, O.A. et al. Indicative capacity of NDVI in predictive mapping of the properties of plow horizons of soils on slopes in the south of Western Siberia. Eurasian Soil Sc. 50, 1332–1343 (2017). https://doi.org/10.1134/S1064229317110060

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