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Abstract <p>Arable soil degradation poses a serious threat to global agricultural production and food security. Thus, the monitoring of its initial stages such as soil crust development is of great importance. In our study we presented an approach for such monitoring based on multi-temporal satellite data. Study region, covered by a Landsat 8 OLI scene (180 × 185 km), included the south-eastern part of Moscow, north-eastern part of Tula and western part of Ryazan regions of Russia. The suggested approach is comprised of such steps as (1)&#xa0;multi-temporal satellite data selection and preprocessing; (2) open soil surface identification; (3) dry soil surface detection; (4) detection of soil crust presence in a pixel. The calibration of the approach (steps (3), (4)), was performed based on the field data and the data from two laboratory experiments with soils of the studied region. The main result was a map of the probability of soil crust presence (SCP) for the studied region for the period 2016–2021, outlining the areas with different risk of soil degradation development. The field validation of the map showed that the obtained SCP values are in agreement with the indicators of soil susceptibility to the crust formation.</p>

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Satellite Monitoring of the Initial Stages of Arable Soil Degradation

  • 期刊论文

  • 发表日期：

    2025年4月18日

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  • E. Yu. Prudnikova,
  • I. Yu. Savin,
  • G. V. Vindeker

摘要

Abstract

Arable soil degradation poses a serious threat to global agricultural production and food security. Thus, the monitoring of its initial stages such as soil crust development is of great importance. In our study we presented an approach for such monitoring based on multi-temporal satellite data. Study region, covered by a Landsat 8 OLI scene (180 × 185 km), included the south-eastern part of Moscow, north-eastern part of Tula and western part of Ryazan regions of Russia. The suggested approach is comprised of such steps as (1) multi-temporal satellite data selection and preprocessing; (2) open soil surface identification; (3) dry soil surface detection; (4) detection of soil crust presence in a pixel. The calibration of the approach (steps (3), (4)), was performed based on the field data and the data from two laboratory experiments with soils of the studied region. The main result was a map of the probability of soil crust presence (SCP) for the studied region for the period 2016–2021, outlining the areas with different risk of soil degradation development. The field validation of the map showed that the obtained SCP values are in agreement with the indicators of soil susceptibility to the crust formation.