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<p>Soil degradation, often shown as a decrease in clay and an increase in sand fractions, greatly affects the agricultural productivity and health of an ecosystem. This work presents the Dual Polarimetric Soil Textural Classification Index (DPSTCI), based on Sentinel-1 C-band Synthetic Aperture Radar (SAR) data to estimate soil texture (clay and sand fractions), explicitly parameterizing soil moisture dynamics. The DPSTCI developed during the present study was validated with 80 georeferenced field samples covering five different years (2017–2025) in the semi-arid Perambalur district, India. The statistical validations showed that the model achieved peak predictive performance in 2023 and 2025, with high correlation coefficients (r ≥ 0.92 for clay) and dramatically reduced errors (RMSE reduced to ≤ 8.7%). The spatiotemporal analysis evidenced a detrimental trend with the mean clay content declining consistently from about 40% in 2017 to 30% in 2025, which indicates there is progressive soil degradation for this region. The DPSTCI stands as an important tool for accurate monitoring and management of soil quality against hydro-meteorological variability through an efficient remote sensing approach.</p>

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Analysing Soil Texture Variability with Clay and Sandy Fractions Derived from Synthetic Aperture Radar Data

  • 期刊论文

  • 发表日期：

    2026年3月6日

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  • Vijayasurya Krishnan,
  • A. Rajesh,
  • Dharanidharan Selvaraj,
  • Vishnupriyan Marimuthu,
  • Dhilipkumar Bharathiyar

摘要

Soil degradation, often shown as a decrease in clay and an increase in sand fractions, greatly affects the agricultural productivity and health of an ecosystem. This work presents the Dual Polarimetric Soil Textural Classification Index (DPSTCI), based on Sentinel-1 C-band Synthetic Aperture Radar (SAR) data to estimate soil texture (clay and sand fractions), explicitly parameterizing soil moisture dynamics. The DPSTCI developed during the present study was validated with 80 georeferenced field samples covering five different years (2017–2025) in the semi-arid Perambalur district, India. The statistical validations showed that the model achieved peak predictive performance in 2023 and 2025, with high correlation coefficients (r ≥ 0.92 for clay) and dramatically reduced errors (RMSE reduced to ≤ 8.7%). The spatiotemporal analysis evidenced a detrimental trend with the mean clay content declining consistently from about 40% in 2017 to 30% in 2025, which indicates there is progressive soil degradation for this region. The DPSTCI stands as an important tool for accurate monitoring and management of soil quality against hydro-meteorological variability through an efficient remote sensing approach.