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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]

A digital soil map on a scale of 1 : 50 000 was created for the Pravaya Sokolovka River basin in the south of the Sikhote-Alin Range, Primorsky krai. The main units of the legend were soil subtypes. An attempt was made to adapt the regional nomenclature of soils to the modern Russian soil classification system and to correlate soil names with the nomenclature of the World Reference Base for Soil Resources (WRB). The description of each area included information on the conditions of soil formation. It was shown that vertical zoning is clearly manifested in the soil and vegetation cover. Three small catchments with an area of 3.5–7.6 km2 were chosen as modeling objects. The dynamics of the components of water balance within the catchments were simulated using an open-source SWAT model. Data from published sources, stationary studies, and the observation network of the Russian Hydrometeorological Service were used for the model calibration. Criteria generally accepted in hydrology were used to assess the modeling results. The model reproduced well the flow hydrographs in the average and water-abundant years, but underestimated the peak values of the water discharge during floods caused by intense precipitation by 10–20%. The values of the model parameters and the hydrological characteristics of soils obtained as a result of calibration, and the calculated data on the dynamics of the water balance elements are presented. [ABSTRACT FROM AUTHOR]

For the first time, a soil map of St. Petersburg has been developed on a scale of 1 : 50000 using MicroStation V8i software. The legend to this map contains more than 60 mapping units. The classification of urban soils and information on the soil cover patterns are principally new elements of this legend. New concepts of the urbanized soil space and urbopedocombinations have been suggested for soil mapping of urban territories. The typification of urbopedocombinations in St. Petersburg has been performed on the basis of data on the geometry and composition of the polygons of soils and nonsoil formations. The ratio between the areas of soils and nonsoil formations and their spatial distribution patterns have been used to distinguish between six types of the urbanized soil space. The principles of classification of the soils of urban territories have been specified, and a separate order of pedo-allochthonous soils has been suggested for inclusion into the Classification and Diagnostic System of Russian Soils (2004). Six types of pedo-allochthonous soils have been distinguished on the basis of data on their humus and organic horizons and the character of the underlying mineral substrate. [ABSTRACT FROM AUTHOR]