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This study assesses hailstorm-induced agricultural damage in Jammu & Kashmir using Sentinel-2 imagery from pre-storm (April 1–10, 2023) and post-storm (June 15–25, 2023) periods. Normalized Difference Vegetation Index (NDVI) was calculated to quantify vegetation health changes, with NDVI differences indicating damage severity. ESA World Cover 2021 data isolated cropland, and NDVI change thresholds (− 0.01, − 0.10, − 0.20) classified damage into no, mild, moderate, and severe categories. A composite severity layer integrated these classes for spatial analysis. Area statistics revealed the extent of impacted cropland, while economic losses and affected farmers were estimated using regional yield and market data. Results highlight significant cropland damage, with severe areas indicating substantial economic and socioeconomic impacts. This methodology demonstrates the efficacy of remote sensing for rapid, scalable disaster assessment in agricultural regions.

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Evaluating Agricultural Losses from Hailstorms in Jammu & Kashmir Using Remote Sensing

  • 章节

  • 发表日期：

    2025年

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  • Aaqib Ashraf Bhat,
  • Suraj Kumar Singh,
  • Shruti Kanga

摘要

This study assesses hailstorm-induced agricultural damage in Jammu & Kashmir using Sentinel-2 imagery from pre-storm (April 1–10, 2023) and post-storm (June 15–25, 2023) periods. Normalized Difference Vegetation Index (NDVI) was calculated to quantify vegetation health changes, with NDVI differences indicating damage severity. ESA World Cover 2021 data isolated cropland, and NDVI change thresholds (− 0.01, − 0.10, − 0.20) classified damage into no, mild, moderate, and severe categories. A composite severity layer integrated these classes for spatial analysis. Area statistics revealed the extent of impacted cropland, while economic losses and affected farmers were estimated using regional yield and market data. Results highlight significant cropland damage, with severe areas indicating substantial economic and socioeconomic impacts. This methodology demonstrates the efficacy of remote sensing for rapid, scalable disaster assessment in agricultural regions.