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Abstract <p>The aim of the present work was to develop and test a method for creating a comprehensive digital map of soils with outcropping rubble, sand, and sandy loam deposits and surface calcareous soils based on satellite images and field research data in the southern part of the Volga Upland. The scientific novelty of this work is the integrated approach to the assessment of soils in unproductive agricultural lands formed under the influence of anthropogenic and natural factors that require different land reclamation measures. The object of this study was the key plot located in the western part of the Oroshaemaya research station of the Volga–Don irrigation system in Volgograd oblast. Field studies were carried out in August–September 2020–2023. Route and topographical and profile surveys were conducted. On routes, the presence of carbonates was determined by the boiling rate upon contact with 10% HCl solution for daytime soil surfaces, and three topographic soil profiles were defined with the description of soils and soil-forming rocks. A Pleiades satellite image dated April 25, 2020, with a resolution of 0.5–0.7 m after processing obtained in the B1–B4 and panchromatic channels was used. It was found that the spectral brightness in all channels, which reflects the geological rocks located close to the surface and surface calcareous soils, had similar but still discriminable values, which made it possible to use the satellite image to separate these objects. The satellite image of the key site was classified separately to identify soils with different soil-forming and underlying rocks and to determine the boiling rate of surface-calcareous soils. Classification was performed using the Random Forest algorithm with training based on field data points. Only fields with an open soil surface were analyzed. The classification accuracy ranged from 0.75 to 0.90 in both cases. After classification, a bitmap file was obtained for each field, which was used to calculate the areas occupied by each of the soil types studied. The use of this approach made it possible to discriminate the surface-stony, sandy, and sandy loam soils from the surface-calcareous soils that require different land reclamation measures.</p>

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Soil Mapping in Unproductive Lands in the Southern Part of the Volga Upland (Volgograd Oblast) Based on Satellite Images

  • 期刊论文

  • 发表日期：

    2025年12月1日

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  • I. N. Gorokhova,
  • L. A. Tarnopolskii,
  • E. I. Pankova

摘要

Abstract

The aim of the present work was to develop and test a method for creating a comprehensive digital map of soils with outcropping rubble, sand, and sandy loam deposits and surface calcareous soils based on satellite images and field research data in the southern part of the Volga Upland. The scientific novelty of this work is the integrated approach to the assessment of soils in unproductive agricultural lands formed under the influence of anthropogenic and natural factors that require different land reclamation measures. The object of this study was the key plot located in the western part of the Oroshaemaya research station of the Volga–Don irrigation system in Volgograd oblast. Field studies were carried out in August–September 2020–2023. Route and topographical and profile surveys were conducted. On routes, the presence of carbonates was determined by the boiling rate upon contact with 10% HCl solution for daytime soil surfaces, and three topographic soil profiles were defined with the description of soils and soil-forming rocks. A Pleiades satellite image dated April 25, 2020, with a resolution of 0.5–0.7 m after processing obtained in the B1–B4 and panchromatic channels was used. It was found that the spectral brightness in all channels, which reflects the geological rocks located close to the surface and surface calcareous soils, had similar but still discriminable values, which made it possible to use the satellite image to separate these objects. The satellite image of the key site was classified separately to identify soils with different soil-forming and underlying rocks and to determine the boiling rate of surface-calcareous soils. Classification was performed using the Random Forest algorithm with training based on field data points. Only fields with an open soil surface were analyzed. The classification accuracy ranged from 0.75 to 0.90 in both cases. After classification, a bitmap file was obtained for each field, which was used to calculate the areas occupied by each of the soil types studied. The use of this approach made it possible to discriminate the surface-stony, sandy, and sandy loam soils from the surface-calcareous soils that require different land reclamation measures.