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<p>Drought is a natural hazard that cannot be entirely prevented but can be effectively monitored and mitigated through timely prediction and spatio-temporal analysis. This study assesses the efficacy of remote sensing and meteorological drought indices for monitoring agricultural droughts in western Rajasthan, India. Meteorological indices including the Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Palmer Drought Severity Index (PDSI), Self-Calibrated PDSI (scPDSI), and Rainfall Anomaly Index (RAI) are evaluated alongside the remote-sensing-based Standard Vegetation Index (SVI). A modified Mann–Kendall test is applied to detect significant trends, and the Degree of Dryness Index (DDI) is used to quantify drought severity over time. Results indicate a strong correlation of SVI with SPI (6, 9, and 12&#xa0;months), SPEI (particularly 12-month), PDSI, and scPDSI. Notably, SVI, scPDSI, and 12-month SPEI reveal statistically significant increasing trends. Spatial analysis confirms the alignment between remote-sensed SVI and meteorological indices during both dry (2009) and wet (2011) years. SVI values also exhibit significant correlations with the yield of major rainfed crops—Deshi cotton (r = 0.742), maize (r = 0.67), bajra (r = 0.599), and groundnut (r = 0.59). These findings reinforce the potential of SVI as a reliable remote-sensing tool for agricultural drought assessment in arid environments.</p>

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Integrated drought monitoring using remote sensing and meteorological indices in arid western Rajasthan

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月2日

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  • Azmat Raja,
  • T. Gopikrishnan,
  • Raunak Kumar

摘要

Drought is a natural hazard that cannot be entirely prevented but can be effectively monitored and mitigated through timely prediction and spatio-temporal analysis. This study assesses the efficacy of remote sensing and meteorological drought indices for monitoring agricultural droughts in western Rajasthan, India. Meteorological indices including the Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Palmer Drought Severity Index (PDSI), Self-Calibrated PDSI (scPDSI), and Rainfall Anomaly Index (RAI) are evaluated alongside the remote-sensing-based Standard Vegetation Index (SVI). A modified Mann–Kendall test is applied to detect significant trends, and the Degree of Dryness Index (DDI) is used to quantify drought severity over time. Results indicate a strong correlation of SVI with SPI (6, 9, and 12 months), SPEI (particularly 12-month), PDSI, and scPDSI. Notably, SVI, scPDSI, and 12-month SPEI reveal statistically significant increasing trends. Spatial analysis confirms the alignment between remote-sensed SVI and meteorological indices during both dry (2009) and wet (2011) years. SVI values also exhibit significant correlations with the yield of major rainfed crops—Deshi cotton (r = 0.742), maize (r = 0.67), bajra (r = 0.599), and groundnut (r = 0.59). These findings reinforce the potential of SVI as a reliable remote-sensing tool for agricultural drought assessment in arid environments.