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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]

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]