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The presence of the petrogypsic horizon is an impediment to developing agriculture in the Sahara. It hinders the soil's ability to store water and root development of crops. The petrogypsic horizon is commonly difficult to map due to its location either on the surface or at depth. This study used logistic regression-kriging and logistic regression models to map the petrogypsic horizon occurrence probability using 466 observations over an area of 22 573 ha in the Sahara Desert of Algeria. The models included remote sensing indices and topographic variables as environmental covariates. The accuracy of models was verified by the area under the curve (AUC). A binary map was produced by applying a threshold of 0.7 on the most performant probability map. Our results showed that logistic regression-kriging performed the best (AUC = 0.88), due to the consideration of residual spatial correlation in the model. The grain size index covariate was the most relevant compared to topographic variables, which showed the usefulness of spectral indices. Based on the binary map, the risk associated with the presence of the petrogypsic horizon was limited, representing 26% of the study area. In the Sahara Desert, though the petrogypsic horizon was weakly correlated with the tested environmental covariates, the use of satellite images and residual autocorrelation in a predictive modelling approach improved the mapping and thus risk assessment of the petrogypsic horizon. [ABSTRACT FROM AUTHOR]

Topographic variables derived from digital elevation models (DEM) are the most commonly used predictor in soil organic carbon (SOC) prediction. The predictability of these variates for the spatial heterogenity of SOC is influenced by the size of DEM resolution. This paper aimed to investigate the resolution effect of the topography at multiple DEM resolutions and select the most important predictors and the optimal resolution for SOC prediction in a large-scale and morphologically complex area. The study area, covering 15 600 km2, was located in a mountainous province in China. A total of 3901 soil samples and three machine learning algorithms including random forest, gradient boosted regression tree, and artificial neural network were used to construct the SOC predictive models at resolutions of 30, 90, 150, 210, 270, 330, and 390 m. Topographic factors were derived from the DEM with an original resolution of 30 m and were subsequently resampled to varying resolutions by using the bilinear interpolation algorithm. The importance of each variate in each predictive model was computed for the most important predictor selection, and the determination coefficient (R2) and Root Mean Square Error (RMSE) of each predictive model were computed for the accuracy evaluation. Comparative analysis on point-based topographic representation, the accuracy of predictive models, and the variable importance was conducted among different resolutions to investigate the resolution effect on the SOC prediction and to select the optimal resolution for this study area. The results showed that topography was the key factor influencing the SOC distribution in this large-scale area due to the region's mountainous nature, and the relative contribution of the topography to predict SOC distribution varied with DEM resolutions. Elevation was the most important topographic factor at all DEM resolutions. Accuracies of predictive models built on the three machine learning algorithms were all dependent on DEM resolution, indicating that DEM resolution had an important effect on SOC prediction. Overall, the random forest predictive models outperformed the other two algorithms, and its most accurate result was obtained at the 210-m resolution with an R2 of 0.25 and an RMSE of 4.77 g kg–1. In conclusion, we found that finer resolutions did not necessarily produce more accurate SOC predictions even in a large-scale and morphologically complex area. Therefore, investigating the resolution effect is suggested to select an optimal resolution for the SOC prediction in other regions characterized by similar geomorphological conditions as this study area. [ABSTRACT FROM AUTHOR]

The area of the Smolenskoe Poozerye National Park is characterized by a complicated and poorly studied soil cover and considerable anthropogenic impact. Soil maps, scale of 1 : 25 000, were compiled for a plot of 8.8 km2 in the southwest of the National Park, using the methods of traditional and digital soil mapping (DSM). The results obtained were compared. Both maps show that the study area is dominated by gray-humus soils (Umbric Cambisol (Loamic) and Arenosols (Ochric)). This is related to the agricultural use and regeneration of abandoned plowed soils. Smaller areas are occupied by Al-Fe-humus and texture-differentiated soils (Podzols (Arenic) and Retisols (Loamic), respectively). In addition, isolated areas of psammozems (Arenosols) have been found and identified on the map compiled by the traditional approach. The general accuracy of the map compiled by the DSM was 55%. The identified leading factors were the following: position in the relief, the parent rock, and vegetation. The distribution of soddy-podzolic soils associated with loamy parent rocks and of mucky-peat soils (Histosols) formed in local depressions and on the floodplain of Baklanovskoe Lake was predicted by the DSM methods with an accuracy of 87 and 60%, respectively. The location of soddy podburs (Entic Rustic Podzols (Ochric)) was predicted with an accuracy of only 29%. Large-scale differentiation of the soil cover was better reflected by the traditional method of the soil cover mapping as compared to the digital approach. [ABSTRACT FROM AUTHOR]

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 aim of this work is to evaluate recent changes in soil salinity status at the Generalovskaya irrigation system in the dry-steppe zone of Volgograd oblast of Russia on the second terrace of the Don River near the Tsimlyansk Reservoir. The studied area has not been irrigated since the mid-1990s. We have used materials of terrain soil survey of the area in 1990 and 2020; data on soil water extracts (1 : 5); measurements of the activities of Na+, Ca2+, and Cl– ions with ion-selective electrodes in soil pastes with a moisture content of 40–45%; and aerial photographs (1990) and satellite image (2020). The studied area was subjected to secondary soil salinization in the 1990s due to the groundwater rise above the critical level. In 25 years after the cessation of irrigation, the groundwater table has descended to a depth of more than 5 m, and secondary-saline soils have been subjected to a gradual desalinization with the development of alkalization in the topsoil. The current soil salinity status inherits the pattern that existed 25 years ago, i.e., the spatial distribution of salts in the studied landscape is generally preserved. We have revealed the presence of calcium chlorides in some soils in 2020, which are identified as evidences of secondary salinization developed by the 1990s. It is shown that the catenary method of terrain soil survey may be used in combination with satellite information and digital data processing for mapping the soil cover under post-irrigation conditions. This enables the reflection of the main soil groups, the depth of the upper saline horizon, and the presence or absence of carbonates on the soil surface. Such information is necessary for the development and selection of optimal reclamation measures. [ABSTRACT FROM AUTHOR]

Soil depth (depth of soil) is a key factor to evaluate the potential of land for farming. Observation of soil depth has been performed by field work over the long term, consuming enormous time and huge labor. Prediction of the soil depth using digital terrain analysis could be conducted not only with less time and labors, but without solum destruction, compared to conventional field observation. In the present study, we analyzed various relationships between soil depth and digital terrain attributes with parent materials dataset, and established a useful model for spatial prediction of the soil depth in the Pongsang Creek Watershed. Prediction models for soil depth were built based on simple linear regression (SLR) and selection-wise non-linear regression (SWNR), respectively, and their results were compared with each other. Use of the SWNR significantly improved the SLR, for every parent material. Globally, the SWNR improved validation performance by 0.36 for R2 and 23.21 for RMSE (R2 = 0.65, RMSE = 5.95), compared to the SLR. This finding confirms high potential of parent material dataset and the SWNR in digital terrain analysis for soil depth prediction. [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]

A soil map provides information on soil spatial distribution. Such information is essential for effective use of soil resources for crop production, land evaluation, spatial planning, environmental control, and for other similar purposes. In this study, a digital soil map for a semiarid region of Afghanistan was developed. This map contains soil taxonomic information up to the subgroup level, which is the first such attempt in Afghanistan. A total of 114 soil samples were collected in and around the Khost Province through an intensive soil survey. The collected samples were classified into 14 subgroups of soils, following the USDA soil classification system. A soil land inference model (SoLIM) was applied for mapping the recognized 14 soil subgroups digitally, via an expert knowledge-based fuzzy soil inference scheme, with surface topography and other spatial data as inputs. The overall accuracies from the error matrix and Kappa statistics were 0.74 and 0.71, respectively. This map was also compared with the currently used soil map. A general agreement between the two maps was found in the spatial distribution of soil classes, at the great group level. However, the newly developed map contains more detailed information on soils, which might be useful for the advanced use of soil information, for example, to better determine the crop type for cultivation by considering the detailed soil properties. Throughout this study, 14 different recognized classes of soil subgroups were digitally mapped in the study area. [ABSTRACT FROM AUTHOR]

A new method of digital mapping of the soil cover pattern with calculation of the share of soils of different taxa and degree classes for soil erosion in the soil associations is proposed. A comparative analysis of soil maps obtained using different methods of construction (visual expert and digital) and with their different contents (displaying the dominant soil or soil associations) has been performed. In the case of mapping by the visual expert method (with the display of the dominant soil), a significant underestimation of the total area of moderately and strongly eroded soils in comparison with the digital mapping is noted. These differences are due to the underestimation of the area of small polygons with moderately and strongly eroded soils in the composition of soil associations on slopes of low steepness and in shallow hollows in the visual expert method of mapping. When the content of digital maps is generalized from soil associations to dominant soil categories, a significant change in information on the degree of soil degradation by erosion is also noted. Comparison of visual expert and digital methods for mapping soils of different taxa indicates a high degree of compliance between the spatial location and area of soil delineations with similar component content in both cases. The greatest differences between the soil maps created by these methods are noted for the soils with periodic overmoistening, namely, meadow-chernozemic (Luvic Chernic Phaeozem (Oxyaquic)) and chernozemic-meadow (Luvic Stagnic Chernic Phaeozem) soils because of the poor consideration for microtopography in traditional mapping. In general, it can be concluded that the creation of a digital map is more difficult in terms of the need to use specialized computer programs and mathematical models. However, the resulting digital databases contain information of a higher level of detail than traditional soil maps. [ABSTRACT FROM AUTHOR]

Active development of the Cis-Ol'khon area in the past decades enhanced the conflicts between land users related to multifunctional land use (residential, agricultural, recreational, and nature-protective), which should be resolved on the basis of scientific data. The aim of this study is to compile a soil map of the coastal zone of the Ol'khon Plateau of 26.3 km2 in area. This area is subjected to increasing recreational impact against the background of grazing and residential land use. Small-scale soil maps of this area show only the most general regularities of the geographical distribution of soils and do not reflect specific features of the soil cover. A digital soil map (on a scale of 1 : 25 000) of the area around the Kurkut Bay was compiled on the basis of the landscape-typological map by M.V. Zagorskaya and a network of 71 soil pits examined in 2015–2018). The classification of Russian soils of 2004 was used for the legend of the soil map; it was also correlated with the World Reference Base for Soil Resources (WRB-2014) system and the Classification and Diagnostics of Soils of the Soviet Union (1977), which was used in the soil map of the Elantsy state farm. Soil subtypes reflecting the entire range of soils in the studied area are the main taxonomic units in the legend for the soil map. Small and frequently alternating elementary soil areas in the studied region are formed under the impact of contrasting soil-forming factors. Lithozems (Leptosols) and gray-humus soils (Dystric Leptosols) occupy the main area. The analytical data and the soil map may be used as the basis for soil monitoring. [ABSTRACT FROM AUTHOR]

Digital medium-scale (1 : 100 000) soil map of the Southern Coast from the Aia Cape on the west to the town of Feodosiya on the east was compiled on the basis of field data obtained by the authors. This map contains actual information on the soil cover of natural, humanly transformed landscapes, and urbanized territories. The map was created using GIS and remote sensing methods (multispectral Landsat images were applied). The legend to the map was based on the Classification and Diagnostic System of Russian Soils (2004). The map comprises 1053 polygons and 34 soil units, which are arranged in four groups: natural soils, soils of terraced slopes, agricultural soils, and soils of urbanized territories. There is also information about rock outcrops, soil erosion intensity, and share of natural soils in the areas of human-modified soils. The analysis of the map demonstrates that natural soils occupy 63% of the area. The main natural soils are Dystric Cambisols (they compose approximately 35% of all natural soils) and Eutric Cambisols (about 23%). Among human-modified soils, soils of urbanized areas make up about 15%; agricultural soils, 12%; and soils of terraced slopes (Escalic qualifier in the WRB) under forest plantations, 10%. The latter soils are widespread and may be named "Postturbozems"; this term is absent in the Classification and Diagnostic System of Russian Soils (2004). The new digital soil map reflects the features of the present-day soil cover of the Southern Coast of Crimea and may be used for agricultural and forest management purposes, as well as for geographical, pedological, and ecological studies. [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]

This paper provides a review of the current state of soil geography and budding directions for the development of pedogeographic research. We mention some new ideas in the frames of structural approach rooting in the classical concept of soil cover pattern and based on new concepts, such as pedodiversity assessment, graph theory, and geostatistical analysis of soil spatial variation. We note the significance of digital soil mapping in the development of the theory and practice of pedogeography and stress that digital soil mapping is a method that cannot replace soil geography as a scientific discipline. There is a need for deeper integration of mathematical methods in traditional soil geography. We stress that pedogeographical models are required for predicting soil properties and regimes even in digital agriculture. We discuss the necessity for adequate reflection of polygenetic soils in the soil mantle, and recommend using both indirect paleogeographic information and current remote and proximate sensing data. We also note the difficulties in predicting the spatial distribution of anthropogenically transformed soils using state factor theory; we discuss the possibilities of broader use of historical and economical geography data. In conclusion, we suggest developing "new soil geography" not only through integration of mathematical methods but also through closer integration with allied sciences. [ABSTRACT FROM AUTHOR]

Soil as a separate natural body occupies certain area with its own set of spectral characteristics within the RED-NIR spectral space. This is an ellipse-shaped area, and its semi-major axis is the soil line for a satellite image. The spectral area for a bare soil surface is neighboring to the areas of black carbon, straw, vegetating plants, and missing RED-NIR values. A reliable separation of the bare soil surface within the spectral space is possible with the technology of spectral neighborhood of soil line. The accuracy of this method is 90%. The determination of the bare soil surface using vegetation indices, both relative (NDVI), and perpendicular (PVI), is incorrect; the accuracy of these methods does not exceed 65%, and for most of the survey seasons it may be lower than 50%. The flat part of the "tasseled cap" described as the soil line, is not a synonym for the area of the bare soil surface. The bare soil surface on the RED-NIR plots occupies significantly smaller areas than the area of soil line according to Kauth and Thomas. [ABSTRACT FROM AUTHOR]

On the basis of digital soil, topographic, and geological maps; raster topography model; forestry materials; and literature data, the digital soil map of the Ussuri River basin (24400 km) was created on a scale of 1: 100000. To digitize the initial paper-based maps and analyze the results, an ESRI ArcGIS Desktop (ArcEditor) v.10.1 (http://www.esri.com) and an open-code SAGA GIS v.2.3 (System for Automated Geoscientific Analyses, http://www.saga-gis.org) were used. The spatial distribution of soil areas on the obtained digital soil map is in agreement with modern cartographic data and the SRTM digital elevation model (SRTM DEM). The regional soil classification developed by G.I. Ivanov was used in the legend to the soil map. The names of soil units were also correlated with the names suggested in the modern Russian soil classification system. The major soil units on the map are at the soil subtypes that reflect the entire vertical spectrum of soils in the south of the Far East of Russia (Primorye region). These are mountainous tundra soils, podzolic soils, brown taiga soils, mountainous brown forest soils, bleached brown soils, meadow-brown soils, meadow gley soils, and floodplain soils). With the help of the spatial analysis function of GIS, the comparison of the particular characteristics of the soil cover with numerical characteristics of the topography, geological composition of catchments, and vegetation cover was performed. [ABSTRACT FROM AUTHOR]

The relationships between the morphometric parameters (MPs) of topography calculated on the basis of digital elevation model (АSTER GDEM, 30 m) and the properties of the plow layer of agrogray soils on a slope were analyzed. The contribution of MPs to the spatial variability of the soil moisture reached 42%; to the content of physical clay (<0.01 mm particles), 59%; to the humus content, 46%; to the total nitrogen content, 31%; to the content of nitrate nitrogen, 28%; to the content of mobile phosphorus, 40%; to the content of exchangeable potassium, 45%; to the content of exchangeable calcium, 67%; to the content of exchangeable magnesium, 40%; and to the soil pH, 42%. A comparative analysis of the plow layer within the eluvial and transitional parts of the slope was performed with the use of geomorphometric methods and digital soil mapping. The regression analysis showed statistically significant correlations between the properties of the plow layer and the MPs describing surface runoff, geometric forms of surface, and the soil temperature regime. [ABSTRACT FROM AUTHOR]

An approach towards an automated updating of medium-scale soil maps via imitation of traditional mapping technologies is suggested. It is based on formulation of the rules of mapping in the form of classification trees for separating different soil cover patterns and on creation of the maps of soil-forming factors with the use of satellite data. Algorithms for mapping alluvial soils (Fluvisols), eroded (abraded), and anthropogenically transformed soils are presented. This approach was tested for the southern (Trans-Oka) part of Moscow oblast. The model for an automated soil mapping was realized using ILWIS software. The polygons of alluvial soils were mapped with a higher accuracy via the automated separation of floodplains according to the digital terrain model. The total area of alluvial soils shown on the medium-scale soil map decreased from 373 to 340 km. Calculations of slope angles according to digital terrain models allowed us to localize soil cover patterns with participation of eroded soils with a higher accuracy; their area decreased insignificantly: from 791 to 781 km. Anthropogenically transformed soils of building areas were mapped for the territory of Moscow oblast on the basis of satellite data for the first time. Their areas were delineated taking into account land use types and comprised 551 km, i.e., 15.4% of the total area (3570 km) of the Trans-Oka part of Moscow oblast. [ABSTRACT FROM AUTHOR]

The coefficients of the soil line are often taken into account in calculations of vegetation indices. These coefficients are usually calculated for the entire satellite image, or are taken as constants without any calculations. In both cases, the informativeness of these coefficients is low and insufficient for the needs of soil mapping. In our study, we calculated soil line coefficients at 8000 lattice points for the territory of Plavsk, Arsen'evsk, and Chern districts of Tula oblast on the basis of 34 Landsat 5, 7, and 8 images obtained in 1985-2014. In order to distinguish between the soil line calculated for a given image and the soil line calculated for lattice points on the basis of dozens of multitemporal images, we suggest that the latter can be referred to as the temporal soil line. The temporal soil line is described by a classical equation: NIR = RED a + b, where a is its slope relative to the horizontal axis (RED), and b is the Y-axis (NIR) intercept. Both coefficients were used to create soil maps. The verification of the maps was performed with the use of data on 1985 soil pits. The informativeness of these coefficients appeared to be sufficient for delineation of eight groups of soils of different taxonomic levels: soddy moderately podzolic soils, soddy slightly podzolic soils, soddy-podzolic soils, light gray forest soils, gray forest soils, dark gray forest soils, podzolized chernozems, and leached chernozems. The b coefficient proved to be more informative, as it allowed us to create the soil map precisely on its basis. In order to create the soil map on the basis of the a coefficient, we had to apply some threshold values of the b coefficient. The bare soil on each of Landsat scenes was separated with the help of the mask of agricultural fields and the notion of the spectral neighborhood of soil line (SNSL). [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]

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]

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]

A new methodological approach aimed at the creation and correction of digital soil maps on the basis of systematized and georeferenced available cartographic and attribute information arranged into a GIS project has been tested. The particular stages and methods of the creation of a digital map of salt-affected soils of Khakassia with the use of GIS technologies in the ArcInfo format are described. All the available cartographic materials, including the Soils of Russia GIS project, digital elevation models, remote sensing data, and analytical data on 82 soil pits (including the authors' materials and previously published data), have been used for the creation of this new map. [ABSTRACT FROM AUTHOR]