Daria Alekseevna Zhulidova

Two directions of soil research digitalization were identified and discussed. The first one is the creation and maintenance of databases with the reflectance spectra in the region of 300—2500 nm for the upper soil horizons coupled with their main physico)chemical properties. The second one is the accumulation and updating of already published spectra in the visible range (400—750 nm), with the inclusion of data on all horizons of the soil profile, allowing to perform horizons diagnostics, as well as the whole profile identification. The second direction commonly uses the soil color indicators, both in international optical systems and in Russian specific indicator systems. The algorithms can be used to study openaccess global soil libraries.

We created a three-dimensional (3D) model of the spatial arrangement of soil horizons with broken boundaries using digital images. This technique was tested on a Retisol — an Alfisol with a glossic horizon. Photographs were taken for 11 vertical sections (2.5 cm distance between sections) of the soil profile for an area of 30 × 45 cm. Colorimetric accuracy of the images was tested against measurements of moist soil samples made with a portable spectrophotometer. The selected best images were calibrated using an internal color calibration method for color correction. The images were combined into a common array to build a 3D optical soil horizon map using the CIELAB color space. A protocol for processing the 3D soil images was created that showed 3D soil structure. It was found that the CIELAB color coordinates can be used to distinguish and delineate AE, E, and EB horizons. We then tested the method to assess soil carbon stocks and found that the stocks using the 3D model were 28% higher than when calculated using the 2D model. We conclude that the optical 3D mapping method can accurately represent the 3D structure and can be used to quantify soil horizon variations.

For landscape conditions in the upper reaches of the river Klyazma, Solnechnogorsk district, Moscow region, the height and reserves of snow cover were investigated, and the chemical composition of the snow was determined. The basis for considering the component composition of snow cover was the geochemical taxonomy of chemical elements based on the characteristics of water migration and abundance.

Data from 23 snow sampling points were interpolated in SAGA GIS using the inverse distance weighting (IDW) method. On this basis, zones differing in the chemical composition of snow are identified. One of the zones is confined to the M-10 Moscow-St. Petersburg highway, while the second borders on populated areas. The area close to the highway is characterized by increased levels of calcium, sodium, aluminum, and chloride ions in the snow cover. The second zone, bordering populated areas, is characterized by a high content of calcium, copper, and manganese in the snow. For the third zone, low concentrations of components in the snow were observed, which are characteristic of a superaquatic landscape due to the distance from sources of pollution.

The studied composition of snow waters belongs to the bicarbonate-sodium-calcium-chloride class. It has been shown that the height and reserves of snow cover are partially controlled by two factors: the type of elementary landscape and the type of ecosystem. Against this background, the spatial distribution of concentrations of elements and anions in snow is predominantly controlled by the anthropogenic factor.

Approximately one third of the scientific publications dedicated to digital soil mapping includes morphometric variables as predictors for soil cover models. For each specific territory with its own set of specific landforms, a unique set of significant morphometric variables is needed to describe the features of such a complex system as soil cover, selected directly with the research tasks. Thus, the aim of this research is to find relationships between soils taxa as vell as its morphological features determining soil diagnostic at different taxonomic levels, and morphometric variables in the case study for the farmlands soil cover on the left bank of the Don River in the Voronezh region, Liskinsky district. Soil cover consists not only of bleached, leached, and typical chernozems, but also chernozem-like meadow soil. Methods of parametric and nonparametric statistics confirm that the most important morphometric variables influencing all analyzed soil properties with diagnostic importance for determining soil allocations are  catchment area, vertical curvature and minimum curvature. The largest number of correlations between morphometric variables and soil properties occurs at maximum level of model generalization in the range of 17 m, 51 m, 102 m, 306 m.