Panda et al. (2026) Assessing meteorological drought indices for monitoring agricultural drought using SPEI: a remote sensing approach

Identification

• Journal: Journal of Atmospheric and Solar-Terrestrial Physics
• Year: 2026
• Date: 2026-01-06
• Authors: Kanhu Charan Panda, Pradosh Kumar Paramaguru, Ram Mandir Singh, Shailesh Singh
• DOI: 10.1016/j.jastp.2026.106726

Research Groups

• Department of Soil Conservation, National PG College (Barhalganj), DDU Gorakhpur University, Gorakhpur, UP, India
• Automation and Plant Engineering Division, ICAR-National Institute of Secondary Agriculture, Namkum, Ranchi, Jharkhand, India
• Department of Agricultural Engineering, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi, UP, India
• K. Banerjee Centre of Atmospheric and Ocean Studies, IIDS, Nehru Science Centre, University of Allahabad, Allahabad, UP, India

Short Summary

This study compared seven meteorological drought indices against the Standardised Precipitation Evapotranspiration Index (SPEI) to identify suitable proxies for agricultural drought in a tropical river basin using remote sensing data, finding that SPI and CZI are the most reliable alternatives when evaluated comprehensively.

Objective

• To identify meteorological drought indices suitable for monitoring agricultural drought in a tropical river basin using remote sensing satellite data.

Study Configuration

• Spatial Scale: Subarnarekha River basin (tropical river basin)
• Temporal Scale: Not explicitly defined for the entire study period, but analysis involved 3-month SPEI.

Methodology and Data

• Models used: Standardised Precipitation Index (SPI), Deciles Index (DI), Percent of Normal Index (PNI), Rainfall Anomaly Index (RAI), China-Z Index (CZI), Modified China-Z Index (MCZI), Z-score Index (ZSI), and Standardised Precipitation Evapotranspiration Index (SPEI).
• Data sources: NASA POWER satellite data

Main Results

• The China-Z Index (CZI) exhibited the highest correlation with the 3-month SPEI (R² = 0.88–0.94), followed by the Rainfall Anomaly Index (RAI) (R² = 0.85–0.90).
• Initial assessment based solely on correlation coefficients categorized SPI, CZI, and MCZI as 'excellent'; ZSI and RAI as 'good'; and PNI and DI as 'poor' in replicating SPEI.
• Integrating correlation, Root Mean Square Error (RMSE), and statistical distribution for evaluation reclassified MCZI and SPI from "good" to "excellent" categories.
• SPI and CZI demonstrated superior alignment with SPEI distributions across both dry and wet periods, establishing them as the most reliable alternatives for agricultural drought assessment.
• The study emphasizes that relying solely on correlation coefficients can lead to erroneous results, advocating for comprehensive evaluation criteria incorporating multiple statistical metrics.

Contributions

• Provides a comprehensive comparison of seven meteorological drought indices against SPEI for agricultural drought monitoring in a tropical river basin.
• Highlights the limitations of using only correlation coefficients for evaluating drought indices and proposes an integrated evaluation approach (correlation, RMSE, and statistical distribution).
• Identifies SPI and CZI as the most reliable meteorological drought indices for assessing agricultural drought in tropical and data-scarce regions.
• Emphasizes the importance of robust, multi-metric evaluation criteria for reliable drought assessment.

Funding

• Not explicitly mentioned in the provided text.

Citation

@article{Panda2026Assessing,
  author = {Panda, Kanhu Charan and Paramaguru, Pradosh Kumar and Singh, Ram Mandir and Singh, Shailesh},
  title = {Assessing meteorological drought indices for monitoring agricultural drought using SPEI: a remote sensing approach},
  journal = {Journal of Atmospheric and Solar-Terrestrial Physics},
  year = {2026},
  doi = {10.1016/j.jastp.2026.106726},
  url = {https://doi.org/10.1016/j.jastp.2026.106726}
}