Abstract
In the well-known conceptual model SCORPAN, a given soil property is considered as dependent on the following environmental factors: soil, climate, organisms, topography, time, and space. Predictive mapping of soils in digital soil mapping is based on similar ideas, but environmental factors may include not only factors of soil formation, but also, for example, remote sensing data. At present, predictive mapping has found wide application not only in soil science but also in ecology, agriculture, and geomorphology. This paper provides a review of environmental factors that are used in predictive mapping with a special attention to situations, when wide sets of environmental factors may be used and a part of them are not quantitative, such as vegetation types. The systems of quantitative variables for topography and climate description are most developed, so special attention is paid to them. Land surface description is performed using both local and non-local variables that need integration. In climate description, variables, which give estimates of dry or wet terrain features, such as moisture ratio or water deficit, are essential. They need evaluation of potential evapotranspiration that is not measured by weather stations, but can be calculated. The possibilities of accounting for these and other environmental factors, including non-quantitative ones, in quantitative statistical models of predictive mapping are described together with principles of their construction, verification, comparison, and choice of appropriate models. Examples of predictive soil mapping applications are given for various scales, and their specificity for different scales is outlined. Some aspects of remote sensing data usage are discussed.
REFERENCES
A. O. Alekseev, G. V. Mitenko, and P. A. Sharyi, “Quantitative estimates of paleoenvironmental changes in the Late Holocene in the south of the East European Plain as recorded in the magnetic properties of soils,” Eurasian Soil Sci. 53 (12), 1677–1686 (2020). https://doi.org/10.1134/S1064229320120029
S. A. Bartalev, V. A. Egorov, V. O. Zharko, E. A. Lupyan, D. E. Plotnikov, S. A. Khvostikov, and N. V. Shabanov, Satellite Mapping of the Vegetation Cover of Russia (Inst. Kosm. Issled. Ross. Akad. Nauk, Moscow, 2016) [in Russian].
M. I. Budyko, Thermal Balance of the Earth’s Surface (Gidrometeoizdat, Leningrad, 1956) [in Russian].
K. F. Gauss, “General studies about curved surfaces,” in On the Foundations of Geometry (Gos. Izd. Tekh.-Teor. Lit., Moscow, 1956), pp. 123–161 [in Russian].
V. V. Dokuchaev, “Russian chernozem,” in Selected Writings (Sel’khozgiz, Moscow, 1948), Vol. 1 [in Russian].
N. N. Ivanov, “Landscape and climatic regions of the globe,” in Papers of the Geographical Society (Moscow–Leningrad, 1948), Vol. 1 [in Russian].
A. G. Isachenko, Landscape Science and Physical-Geographical Zoning (Vysshaya Shkola, Moscow, 1991) [in Russian].
F. I. Kozlovskii, “Soil individual and methods for its determination,” in Regularities of Spatial Variation of Soil Properties and Information-Statistical Methods for Their Study (Nauka, Moscow, 1970), pp. 42–59 [in Russian].
I. F. Kuzyakova, V. A. Romanenkov, and Ya. V. Kuzyakov, “Method of geostatistics in soil-agrochemical studies,” Pochvovedenie, No. 9, 1132–1139 (2001).
L. T. Matveev, Course of General Meteorology. Atmospheric Physics (Gidrometeoizdat, Leningrad, 1984) [in Russian].
P. S. Pogrebnyak, General Forestry (Kolos, Moscow, 1968) [in Russian].
I. A. Ryl’skii, “Laser scanning and digital aerial photography: a new level of detail,” Geomatics, No. 4, 53–56 (2015).
I. N. Stepanov, I. V. Florinskii, and P. A. Sharyi, “On the conceptual scheme of landscape studies,” in Geometry of Earth Surface Structures (Pushchino, 1991), pp. 9–15 [in Russian].
P. A. Sharyi, “Topographic method of second derivatives,” in Geometry of Earth Surface Structures (Pushchino, 1991), pp. 28–58 [in Russian].
P. A. Sharyi, O. V. Rukhovich, and L. S. Sharaya, “Methodology for the analysis of spatial variability of wheat yield characteristics depending on the conditions of the agricultural landscape,” Agrokhimiya, No. 2, 57–81 (2011).
P. A. Sharyi, O. V. Rukhovich, and L. S. Sharaya, “Predictive modeling of winter wheat yield characteristics,” in Digital Soil Cartography: Theoretical and Experimental Studies (Pochv. Inst. im. V. V. Dokuchaeva, Moscow, 2012), pp. 310–326 [in Russian].
P. A. Shary and D. L. Pinskii, “Statistical evaluation of the relationships between spatial variability in the organic carbon content in gray forest soils, soil density, concentrations of heavy metals, and topography,” Eurasian Soil Sci. 46 (11), 1076–1087 (2013). https://doi.org/10.1134/S1064229313090044
P. A. Shary and N. S. Smirnov, “Mechanisms of the effects of solar radiation and terrain anisotropy on the vegetation of dark conifer forests in the Pechora-Ilych state biosphere reserve,” Russ. J. Ecol. 44 (1), 9–17 (2013). https://doi.org/10.1134/S1067413613010116
P. A. Sharyi, L. S. Sharaya, and L. V. Sidyakina, “Relationship between NDVI forests and climate characteristics of the Volga basin,” Sovrem. Probl. Distantsionnogo Zondirovaniya Zemli Kosmosa 17 (4), 154–163 (2020). https://doi.org/10.21046/2070-7401-2020-17-4-154-163
D. G. Schepaschenko, L. V. Mukhortova, A. Z. Shvidenko, and E. F. Vedrova, “The pool of organic carbon in the soils of Russia,” Eurasian Soil Sci. 46 (2), 107–116 (2013). https://doi.org/10.1134/S1064229313020129
R. G. Allen, L. S. Pereira, D. Raes, and M. Smith, Crop Evapotranspiration (Quides for Computing Crop Water Requirements). FAO Irrigation and Drainage Paper No. 56 (1998).
A. Baltensweiler, L. Walthert, C. Ginzler, F. Sutter, R. S. Purves, and M. Hanewinkel, “Terrestrial laser scanning improves digital elevation models and topsoil pH modelling in regions with complex topography and dense vegetation,” Environ. Modell. Software 95, 13–21 (2017).
C. M. Beale, J. J. Lennon, J. M. Yearsley, M. J. Brewer, and D. A. Elston, “Regression analysis of spatial data,” Ecol. Lett. 13 (2), 246–264 (2010).
T. W. Beers, P. E. Dress, and L. C. Wensel, “Aspect transformation in site productivity research,” J. For. 64, 691–692 (1966).
J. Besag, “Spatial interaction and the statistical analysis of lattice systems,” J. R. Stat. Soc. Ser. B. 36 (2), 192–236 (1974).
T. Behrens, H. Forster, T. Scholten, U. Steinrucken, E.-D. Spies, and M. Goldschmitt, “Digital soil mapping using artificial neural networks,” J. Plant Nutr. Soil Sci. 168 (1), 21–33 (2005).
K. J. Beven and M. J. Kirkby, “A physically based, variable contributing area model of basin hydrology,” Hydrol. Sci. Bull. 24 (1), 43–69 (1979).
M. P. Bishop, L. A. James, Jr. J. F. Shroder, and S. J. Walsh, “Geospatial technologies and digital geomorphological mapping: concepts, issues and research,” Geomorphology 137, 5–26 (2012).
C. F. Dormann, J. M. McPherson, M. B. Araujo, R. Bivand, J. Bolliger, G. Carl, R. G. Davies, A. Hirzel, W. Jetz, W. D. Kissling, I. Kuhn, R. Ohlemuller, P. R. Peres-Neto, B. Reineking, B. Schroder, F. M. Schurr, and R. Wilson, “Methods to account for spatial autocorrelation in the analysis of species distributional data: a review,” Ecography 30 (5), 609–628 (2007).
I. S. Evans, “General geomorphometry, derivatives of altitude, and descriptive statistics,” in Spatial Analysis in Geomorphology (Methuen & Co., Ltd., London, 1972), Ch. 2, pp. 17–90.
J. Franklin, “Predictive vegetation mapping: geographic modelling of biospatial patterns in relation to environmental gradients,” Prog. Phys. Geogr. 19 (4), 474–499 (1995).
T. G. Freeman, “Calculating catchment area with divergent flow based on a regular grid,” Comput. Geosci. 17 (3), 413–422 (1991).
R. Grimm, T. Behrens, M. Märker, and H. Elsenbeer, “Soil organic carbon concentrations and stocks on Barro Colorado Island – Digital soil mapping using Random Forests analysis,” Geoderma 146, 102–113 (2008).
S. Yu. Grishin, “The boreal forests of north-eastern Eurasia,” Vegetatio 121, 11–21 (1995).
A. Guisan and N. E. Zimmermann, “Predictive habitat distribution models in ecology,” Ecol. Modell. 135 (2–3), 147–186 (2000).
B. A. Hawkins, M. A. Rodriguez, and S. G. Weller, “Global angiosperm family richness revisited: linking ecology and evolution to climate,” J. Biogeogr. 38, 1253–1266 (2011).
R. J. Hijmans, S. E. Cameron, J. L. Parra, P. J. Jones, and A. Jarvis, “Very high resolution interpolated climate surfaces for global land areas,” Int. J. Climatol. 25 (15), 1965–1978 (2005).
J. Hjort and M. Marmion, “Effects of sample size on the accuracy of geomorphological models,” Geomorphology 102, 341–350 (2008).
D. W. Hosmer and S. Lemeshow, Applied Logistic Regression (John Wiley & Sons, Inc., New York, 2000).
T. Hwang, C. Song, J. M. Vose, and L. E. Band, “Topography-mediated controls on local vegetation phenology estimated from MODIS vegetation index,” Landscape Ecol. 26, 541–556 (2011).
D. King, H. Bourennane, M. Isampert, and J. J. Macaire, “Relationship of the presence of a non-calcareous clay-loam horizon to DEM attributes in a gently sloping area,” Geoderma 89 (1–2), 95–111 (1999).
P. V. Krestov, A. M. Omelko, and Y. Nakamura, “Phytogeography of higher units of forests and krummholz in North Asia and formation of vegetation complex in the Holocene,” Phytocoenologia 40, 41–56 (2010).
K. Lim, P. Treitz, M. Wulder, B. St-Onge, and M. Flood, “LiDAR remote sensing of forest structure,” Prog. Phys. Geogr. 27, 88–106 (2003).
H. Lischke, A. Guisan, A. Fischlin, and H. Bugmann, “Vegetation responses to climate change in the Alps - Modeling studies,” in A View from the Alps: Regional Perspectives on Climate Change (MIT Press, Bostonm 1998), Ch. 6, pp. 309–350.
J. A. Lutz, J. W. van Wagtendonk, and J. F. Franklin, “Climatic water deficit, tree species ranges, and climate change in Yosemite National Park,” J. Biogeogr. 37, 936–950 (2010).
R. A. MacMillan and P. A. Shary, “Landforms and landform elements in geomorphometry,” in Geomorphometry: Concepts, Software, Applications. Developments in Soil Science (Elsevier, Amsterdam, 2009), Vol. 33, Ch. 9, pp. 227–254.
L. W. Martz and E. de Jong, “CATCH: a Fortran program for measuring catchment area from digital elevation models,” Comput. Geosci. 14 (5), 627–640 (1988).
A. B. McBratney, I. O. A. Odeh, T. F. A. Bishop, M. S. Dunbar, and T. M. Shatar, “An overview of pedometric techniques for use in soil survey,” Geoderma 97 (3–4), 293–327 (2000).
A. B. McBratney, M. L. Mendonca Santos, and B. Minasny, “On digital soil mapping,” Geoderma 117 (1–2), 3–52 (2003).
N. J. McKenzie and P. J. Ryan, “Spatial prediction of soil properties using environmental correlation,” Geoderma 89 (1–2), 67–94 (1999).
H. Mitašová and J. Hofierka, “Interpolation by regularized spline with tension. II. Application to terrain modeling and surface geometry analysis,” Math. Geol. 25 (6), 657–669 (1993).
D. C. Montgomery and E. A. Peck, Introduction to Linear Regression Analysis (John Wiley & Sons, New York, 1982).
D. Moser, S. Dullinger, T. Englisch, H. Niklfeld, C. Plutzar, N. Sauberer, H. G. Zechmeister, and G. Grabherr, “Environmental determinants of vascular plant species richness in the Austrian Alps,” J. Biogeogr. 32, 1117–1127 (2005).
R. J. Pike, I. S. Evans, and T. Hengl, “Geomorphometry: a brief guide,” in Geomorphometry: Concepts, Software, Applications. Developments in Soil Science (Elsevier, Amsterdam, 2009), Vol. 33, Ch. 1, pp. 3–30.
K. B. Pierce Jr., T. Lookingbill, and D. Urban, “A simple method for estimating potential relative radiation (PRR) for landscape-scale vegetation analysis,” Landscape Ecol. 20 (2), 137–147 (2005).
P. Scull, J. Franklin, O. A. Chadwick, and D. McArthur, “Predictive soil mapping: a review,” Prog. Phys. Geogr. 27 (2), 171–197 (2003).
P. A. Shary, “Land surface in gravity points classification by a complete system of curvatures,” Math. Geol. 27 (3), 373–390 (1995).
P. A. Shary, L. S. Sharaya, and A. V. Mitusov, “Fundamental quantitative methods of land surface analysis,” Geoderma 107 (1–2), 1–32 (2002).
P. A. Shary, L. S. Sharaya, and O. V. Rukhovich, “Model-based forecasting winter wheat yields using landscape and climate data,” in Landscape Modelling and Decision Support (Springer Nature Switzerland AG, Switzerland, 2020), Ch. 20, pp. 383–396.
N. L. Stephenson, “Actual evapotranspiration and deficit: biologically meaningful correlates of vegetation distribution across spatial scales,” J. Biogeogr. 25, 855–870 (1998).
A. L. Thomas, D. King, E. Dambrine, A. Couturier, and J. Roque, “Predicting soil classes with parameters derived from relief and geologic materials in a sandstone region of the Vosges mountains (Northeastern France),” Geoderma 90 (3–4), 291–305 (1999).
J. A. Thompson, J. C. Bell, and C. A. Butler, “Digital elevation model resolution: effects on terrain attribute calculation and quantitative soil-landscape modeling,” Geoderma 100, 67–89 (2001).
C. W. Thornthwaite, “An approach toward a rational classification of climate,” Geogr. Rev. 38 (1), 55–94 (1948).
F. R. Troeh, “Landform parameters correlated to soil drainage,” Soil Sci. Soc. Am. Proc. 28 (6), 808–812 (1964).
R. A. Viscarra Rossel and E. N. Bui, “A new detailed map of total phosphorus stocks in Australian soil,” Sci. Total Environ. 542, 1040–1049 (2016).
R. A. Viscarra Rossel, D. J. Brus, C. Lobsey, Z. Shi, and G. McLachlan, “Baseline estimates of soil organic carbon by proximal sensing: comparing design-based, model-assisted and model-based inference,” Geoderma 265, 152–163 (2016).
Author information
Authors and Affiliations
Corresponding author
Additional information
Translated by T. Chicheva
Rights and permissions
About this article
Cite this article
Shary, P.A. Environmental Variables in Predictive Soil Mapping: A Review. Eurasian Soil Sc. 56, 247–259 (2023). https://doi.org/10.1134/S1064229322602384
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1134/S1064229322602384