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For most of the arable land in Russia (132–137 million ha), the dominant and accurate soil information is stored in the form of map archives on paper without coordinate reference. The last traditional soil map(s) (TSM, TSMs) were created over 30 years ago. Traditional and/or archival soil map(s) (ASM, ASMs) are outdated in terms of storage formats, dates, and methods of production. The technology of constructing a multitemporal soil line (MSL) makes it possible to update ASMs and TSMs based on the processing of big remote-sensing data (RSD). To construct an MSL, the spectral characteristics of the bare soil surface (BSS) are used. The BSS on RSD is distinguished within the framework of the conceptual apparatus of the spectral neighborhood of the soil line. The filtering of big RSD is based on deep machine learning. In the course of the work, a vector georeferenced version of the ASM and an updated soil map were created based on the coefficient "C" of the MSL. The maps were verified based on field surveys (76 soil pits). The updated map is called the map of soil interpretation of the coefficient "C" (SIC "C"). The SIC "C" map has a more detailed legend compared to the ASM (7 sections/chapters instead of 5), greater accuracy (smaller errors of the first and second kind), and potential suitability for calculating soil organic matter/carbon (SOM/SOC) reserves (soil types/areals in the SIC "C" map are statistically significant are divided according to the thickness of the organomineral horizon and the content of SOM in the plowed layer). When updating, a systematic underestimation of the numbers of contours and areas of soils with manifestations of negative/degradation soil processes (slitization and erosion) on the TSM was established. In the process of updating, all three shortcomings of the ASMs/TSMs (archaic storage, dates, and methods of creation) were eliminated. The SIC "C" map is digital (thematic raster), modern, and created based on big data processing methods. For the first time, the actualization of the soil map was carried out based on the MSL characteristics (coefficient "C"). [ABSTRACT FROM AUTHOR]

The long-term spectral characteristics of the bare soil surface (BSS) in the BLUE, GREEN, RED, NIR, SWIR1, and SWIR2 Landsat spectral bands are poorly studied. Most often, the RED and NIR spectral bands are used to analyze the spatial heterogeneity of the soil cover; in our opinion, it is outmoded and seems unreasonable. The study of multi-temporal spectral characteristics requires the processing of big remote sensing data based on artificial intelligence in the form of convolutional neural networks. The analysis of BSS belongs to the direct methods of analysis of the soil cover. Soil degradation can be detected by ground methods (field reconnaissance surveys), modeling, or digital methods, and based on the remote sensing data (RSD) analysis. Ground methods are laborious, and modeling gives indirect results. RSD analysis can be based on the principles of calculation of vegetation indices (VIs) and on the BSS identification. The calculation of VIs also provides indirect information about the soil cover through the state of vegetation. BSS analysis is a direct method for analyzing soil cover heterogeneity. In this work, the informativeness of the long-term (37 years) average spectral characteristics of the BLUE, GREEN, RED, NIR, SWIR1 and SWIR2 bands of the Landsat 4–8 satellites for detecting areas of soil degradation with recognition of the BSS using deep machine learning methods was estimated. The objects of study are the spectral characteristics of kastanozems (dark chestnut soils) in the south of Russia in the territory of the Morozovsky district of the Rostov region. Soil degradation in this area is mainly caused by erosion. The following methods were used: retrospective monitoring of soil and land cover, deep machine learning using convolutional neural networks, and cartographic analysis. Six new maps of the average long-term spectral brightness of the BSS have been obtained. The information content of the BSS for six spectral bands has been verified on the basis of ground surveys. The informativeness was determined by the percentage of coincidences of degradation facts identified during the RSD analysis, and those determined in the field. It has been established that the spectral bands line up in the following descending order of information content: RED, NIR, GREEN, BLUE, SWIR1, SWIR2. The accuracy of degradation maps by band was determined as: RED—84.6%, NIR—82.9%, GREEN—78.0%, BLUE—78.0%, SWIR1—75.5%, SWIR2—62.2%. [ABSTRACT FROM AUTHOR]

The maps of stable intra-field heterogeneity of soil cover were created based on big satellite (remote sensing) data using the technology of the spectral neighbourhood of the soil line. The maps were created for 29 fields with a total area of 2,000 hectares (ha). On the field XI-1, various amounts of nitrogen spring fertilizer supplement on winter wheat were applied. This differentiation was carried out on 110 rectangular plots with dimensions of 70 × 90 m. Four different amounts of fertilizers were applied to 110 fragments of the XI-1 field in the checkerboard pattern. It was established that the responsiveness of winter wheat to fertilizers varied by different zones of the map with stable intra-field heterogeneity. In low fertility zone, increasing fertilization from 50 kg ha−1 to 350 kg ha−1ammonium nitrate (from 17.2 to 120.4 kg ha−1N) resulted in an increase in wheat yield by 26%, versus 50% in normal fertility zone and 74% in high fertility zone. The stable intra-field heterogeneity map based on big satellite data can be used to intensify the precision farming. [ABSTRACT FROM AUTHOR]