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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. [ABSTRACT FROM AUTHOR]

The main trends in the development of soil mapping methods are discussed, and the major problems are identified. By the present time, the transition from the paper-based soil maps to digital soil-geographical databases has already been completed. The digital mapping of soils and their properties is now accepted as the main method at all the levels of generalization. The approaches of digital soil mapping, as well as of the traditional one, are based on the ideas of V.V. Dokuchaev about the dependence of soils on soil-forming factors. However, in digital soil mapping, new achievements of mathematical statistics and mathematical modeling are being widely applied. This provides for a greater objectivity and reproducibility of the digital soil maps in comparison with the traditional soil maps. At the same time, all unsolved problems of soil cartography related to the lack of field observation data, scale, soil taxonomy, spatial microheterogeneity, and mapping of individual soil properties are preserved. Partially, these problems can be solved by using remote sensing data. When the soil geographical information is used to assess the quality of soil resources, the interpretation of remote sensing data for mapping purposes seems to be more preferable in comparison with the methods of digital soil mapping. [ABSTRACT FROM AUTHOR]

The analysis of 34 cloudless fragments of Landsat 5, 7, and 8 images (1985-2014) on the territory of Plavsk, Arsen'evsk, and Chern districts of Tula oblast has been performed. It is shown that bare soil surface on the RED-NIR plots derived from the images cannot be described in the form of a sector of spectral plane as it can be done for the NDVI values. The notion of spectral neighborhood of soil line (SNSL) is suggested. It is defined as the sum of points of the RED-NIR spectral space, which are characterized by spectral characteristics of the bare soil applied for constructing soil lines. The way of the SNSL separation along the line of the lowest concentration density of points on the RED-NIR spectral space is suggested. This line separates bare soil surface from vegetating plants. The SNSL has been applied to construct soil line (SL) for each of the 34 images and to delineate bare soil surface on them. Distances from the points with averaged RED-NIR coordinates to the SL have been calculated using the method of moving window. These distances can be referred to as averaged spectral deviations (ASDs). The calculations have been performed strictly for the SNSL areas. As a result, 34 maps of ASDs have been created. These maps contain ASD values for 6036 points of a grid used in the study. Then, the integral map of normalized ASD values has been built with due account for the number of points participating in the calculation (i.e., lying in the SNSL) within the moving window. The integral map of ASD values has been compared with four traditional soil maps on the studied territory. It is shown that this integral map can be interpreted in terms of soil taxa: the areas of seven soil subtypes (soddy moderately podzolic, soddy slightly podzolic, light gray forest. gray forest, dark gray forest, podzolized chernozems, and leached chernozems) belonging to three soil types (soddy-podzolic, gray forest, and chernozemic soils) can be delineated on it. [ABSTRACT FROM AUTHOR]

An approach for the updating of medium-scale soil maps is discussed. It is based on the use of modern remote sensing and geoinformation technologies. The boundaries of soil polygons shown on the old soil maps and their soil contents are corrected using geoinformation analysis of modern topographic data and automated interpretation of vegetation conditions as reflected on satellite imagery. The developed methods are illustrated by the particular examples. [ABSTRACT FROM AUTHOR]

The stochastic modeling of the shift in coordinates of the sampling points was performed for a typical land plot in Bryansk opolie with the purpose to assess the influence of the accuracy of the sampling point positioning on the predicted properties of the plow horizon. It was proved that, for agro-gray soils, an occasional shift of the sampling points by 2-6 m regarding the coordinates on satellite photographs of superhigh resolution may significantly change the correlation between the soil properties and the brightness in the infrared channel. Based on the modeling data, the 95% confidence interval was estimated for the possible correlation coefficients of such properties of the arable agro-gray soil as the density, moisture, specific surface, carbon, and mobile nitrogen content. The use of the brightness in the infrared channel as a covariate for building charts may noticeably improve the map's quality; however, it may unjustifiably complicate the mapping units delineation. [ABSTRACT FROM AUTHOR]