Raja et al. (2026) Integrated drought monitoring using remote sensing and meteorological indices in arid western Rajasthan

Identification

• Journal: Discover Civil Engineering
• Year: 2026
• Date: 2026-02-02
• Authors: Azmat Raja, T. Gopikrishnan, Raunak Kumar
• DOI: 10.1007/s44290-026-00412-2

Research Groups

• Department of Civil Engineering, Supaul College of Engineering, Supaul, Bihar, India
• Department of Civil Engineering, National Institute of Technology Patna, Patna, Bihar, India
• Department of Civil Engineering, Government Engineering College, West Champaran, Bettiah, Bihar, India

Short Summary

This study assessed the efficacy of remote sensing and meteorological drought indices for monitoring agricultural droughts in western Rajasthan, India. It found that the Standardized Vegetation Index (SVI) strongly correlates with meteorological indices and crop yield, making it a reliable tool for agricultural drought assessment in arid environments.

Objective

• To assess the performance of five climatic drought indices (Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Palmer Drought Severity Index (PDSI), Self-Calibrated PDSI (scPDSI), Rainfall Anomaly Index (RAI)) and compare them with the satellite-derived Standardized Vegetation Index (SVI).
• To analyze drought trends using the Modified Mann–Kendall (M–K) trend test, which accounts for autocorrelation in climatic time series.
• To introduce and apply a Degree of Dryness Index (DDI) that integrates multiple indicators to assess drought intensity across years.
• To validate the correlation between drought indices, particularly SVI, and crop yield data for major rain-fed crops in the study region.

Study Configuration

• Spatial Scale: Western Rajasthan, India, encompassing 12 districts (Barmer, Bikaner, Churu, Ganganagar, Hanumangarh, Jaisalmer, Jalore, Jodhpur, Nagaur, Pali, Sikar, and Sirohi). The region covers approximately 248,000 square kilometers, located between latitudes 23°N to 30°N and longitudes 69°E to 78°E.
• Temporal Scale:
  • Weather data: 1979 to 2013.
  • MODIS EVI data: 2000 to 2014.
  • SVI analysis: 2000 to 2013.
  • Degree of Dryness Index (DDI): 1979 to 2013.

Methodology and Data

• Models used:
  • Standardized Precipitation Index (SPI)
  • Standardized Precipitation Evapotranspiration Index (SPEI)
  • Palmer Drought Severity Index (PDSI)
  • Self-Calibrated PDSI (scPDSI)
  • Rainfall Anomaly Index (RAI)
  • Standardized Vegetation Index (SVI) (derived from MODIS Enhanced Vegetation Index (EVI) or Normalized Difference Vegetation Index (NDVI))
  • Degree of Dryness Index (DDI)
  • Modified Mann–Kendall (M–K) trend test
  • Sen’s slope estimator
  • Pearson correlation coefficient
  • Spearman rank correlation
• Data sources:
  • Daily and monthly weather data (1979–2013) from 24 weather stations in western Rajasthan (globalweather.tamu.edu).
  • MODIS MOD13Q1 product (2000–2014) for EVI (250-meter spatial resolution, 16-day composite imagery) (USGS earth data - lpdaacsvc.cr.usgs.gov/appeears/).
  • Yield data for major rainfed crops from the Department of Agriculture Rajasthan.

Main Results

• Western Rajasthan exhibits high inter-annual precipitation variability, with annual average precipitation ranging from 59 millimeters to 750 millimeters, and peak rainfall occurring in July and August.
• The Rainfall Anomaly Index (RAI) identified 1987 as the most severe drought year (RAI < -4) and 2011 as an extremely wet year (RAI > 6).
• The Standardized Vegetation Index (SVI) showed strong coherence with PDSI, scPDSI, SPI (6, 9, and 12 months), and SPEI (6 and 9 months), with 2002 identified as a severe drought year (SVI < -1.5).
• Longer time scales (9- and 12-month) for SPI and SPEI showed stronger agreement with PDSI and scPDSI in detecting prolonged drought conditions.
• Trend analysis revealed statistically significant increasing trends for scPDSI, SVI, SPI (3, 6, 9, and 12 months), and SPEI12. These trends were robust even after removing extreme years.
• Spatial analysis confirmed the alignment of remote-sensed SVI with meteorological indices, showing widespread vegetation stress in the drought year 2009 and vigorous growth in the wet year 2011.
• The Degree of Dryness Index (DDI) identified 1987 as the highest drought year for SPI (11), PDSI (26), and scPDSI (26), and 2002 for SPEI (14) and SVI (18).
• SVI exhibited strong positive correlations with the yield of major rainfed crops: Deshi cotton (Pearson r = 0.86, Spearman ρ = 0.84), maize (r = 0.82, ρ = 0.71), groundnut (r = 0.77, ρ = 0.73), and bajra (r = 0.77, ρ = 0.82).

Contributions

• Developed an integrated framework for agricultural drought monitoring by combining multiple meteorological and remote sensing indices (SPI, SPEI, PDSI, scPDSI, RAI, SVI, DDI).
• Validated the efficacy of SVI as a reliable remote-sensing tool for agricultural drought assessment in arid environments, demonstrating strong correlations with both meteorological indices (especially at longer time scales) and actual crop yields.
• Applied the Modified Mann–Kendall test to account for autocorrelation in climatic time series for trend analysis, revealing robust significant increasing trends in SVI, scPDSI, and long-lag SPI/SPEI.
• Introduced and applied the Degree of Dryness Index (DDI) to provide a consolidated annual assessment of drought intensity, complementing multi-scalar indices.
• Established a replicable model for regional drought early-warning systems and agricultural risk assessment, particularly valuable for data-scarce, climate-sensitive regions.

Funding

No funding was obtained for this study.

Citation

@article{Raja2026Integrated,
  author = {Raja, Azmat and Gopikrishnan, T. and Kumar, Raunak},
  title = {Integrated drought monitoring using remote sensing and meteorological indices in arid western Rajasthan},
  journal = {Discover Civil Engineering},
  year = {2026},
  doi = {10.1007/s44290-026-00412-2},
  url = {https://doi.org/10.1007/s44290-026-00412-2}
}