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<p>This study examines agricultural drought and its impacts across diverse land cover types, addressing the limitations of existing global soil moisture datasets, which often lack sufficient local detail due to their coarse spatial resolutions (10–40 km). Leveraging Sentinel-1 satellite imagery, we produced high-resolution soil moisture data (10-m resolution) for the plains of Khuzestan Province, Iran, and introduced a novel drought index, the Optimized Soil Moisture Condition Index (OSMCI), developed using the Particle Swarm Optimization (PSO) algorithm and Interior Point method. The OSMCI was validated against ground-based Standardized Precipitation Index (SPI) measurements at 3-, 6-, and 9-month intervals and compared with the existing SMCI index. Results demonstrated that OSMCI outperformed SMCI in drought assessment accuracy at most ground stations, with normalized root mean square error (NRMSE) values below 0.3, indicating a strong correlation with ground-based indices. This high-resolution index offers a valuable tool for advancing drought monitoring, improving water resource management, and supporting agricultural planning.</p>

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A novel high-resolution soil-moisture mapping using Sentinel-1-imagery and optimization-based for a new precise remote sensing drought index

  • 期刊论文

  • 发表日期：

    2025年12月6日

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  • F. Balouei,
  • M. Kabolizadeh,
  • H. Rabiei-Dastjerdi

摘要

This study examines agricultural drought and its impacts across diverse land cover types, addressing the limitations of existing global soil moisture datasets, which often lack sufficient local detail due to their coarse spatial resolutions (10–40 km). Leveraging Sentinel-1 satellite imagery, we produced high-resolution soil moisture data (10-m resolution) for the plains of Khuzestan Province, Iran, and introduced a novel drought index, the Optimized Soil Moisture Condition Index (OSMCI), developed using the Particle Swarm Optimization (PSO) algorithm and Interior Point method. The OSMCI was validated against ground-based Standardized Precipitation Index (SPI) measurements at 3-, 6-, and 9-month intervals and compared with the existing SMCI index. Results demonstrated that OSMCI outperformed SMCI in drought assessment accuracy at most ground stations, with normalized root mean square error (NRMSE) values below 0.3, indicating a strong correlation with ground-based indices. This high-resolution index offers a valuable tool for advancing drought monitoring, improving water resource management, and supporting agricultural planning.