Eshaghi et al. (2025) A remote sensing-based framework for agricultural drought risk monitoring and assessment: introducing SADFI for disaster risk assessment in Northeastern Iran

Identification

• Journal: Theoretical and Applied Climatology
• Year: 2025
• Date: 2025-12-01
• Authors: Ameneh Eshaghi, Behnam Kamkar
• DOI: 10.1007/s00704-025-05888-z

Research Groups

• Department of Agrotechnology, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran

Short Summary

This study developed a remote sensing-based framework for agricultural drought risk monitoring and assessment in Northeastern Iran, introducing the novel Standardized Agricultural Drought Frequency Index (SADFI). The research evaluated spatio-temporal drought patterns from 2001 to 2023 using satellite-derived indices, revealing significant spatial variations in drought vulnerability across the region.

Objective

• To examine the spatial extent of agricultural drought in the Khorasan Razavi Province.
• To assess the spatiotemporal pattern of drought using the Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and Vegetation Health Index (VHI) over time to identify long-term trends and intensity.
• To analyze the time series to examine the periodic fluctuations of drought in the study area.
• To introduce the Standardized Agricultural Drought Frequency Index (SADFI) as a novel composite index for mapping agricultural drought frequency.

Study Configuration

• Spatial Scale: Agricultural lands of Khorasan Razavi Province, Northeastern Iran, covering an area of 118,854 square kilometers.
• Temporal Scale: 23-year period, from 2001 to 2023, analyzed at monthly and annual scales.

Methodology and Data

• Models used:
  • Temperature Condition Index (TCI)
  • Vegetation Condition Index (VCI)
  • Vegetation Health Index (VHI)
  • Standardized Agricultural Drought Frequency Index (SADFI) - a novel composite index.
  • Global Moran’s I and Local Indicators of Spatial Association (LISA) for spatial autocorrelation analysis.
  • Google Earth Engine (GEE) platform for cloud-based processing.
  • ArcGIS 10.8.2 for spatial analysis and map generation.
  • R software (urca package) for Johansen Cointegration Test.
• Data sources:
  • MODIS data from the Terra satellite.
  • MOD13A2 product for Normalized Difference Vegetation Index (NDVI) with 1 km spatial resolution and 16-day temporal resolution.
  • MOD11A2 product for Land Surface Temperature (LST) with 1 km spatial resolution and 8-day temporal resolution.
  • Digital Elevation Model (DEM).

Main Results

• The years 2008, 2019, and 2020 were identified as the driest, wettest, and wettest years, respectively, during the 23-year study period.
• The SADFI map indicated that a large portion of the mid-latitude agricultural lands in Khorasan Razavi Province experienced no significant drought.
• Monthly TCI showed a decreasing trend from January to July, with drought conditions typically setting in from March and reaching minimum levels in July.
• Monthly VCI exhibited an upward trend from January, peaking in April, and then decreasing, reflecting the seasonal agricultural crop cycle.
• VHI values ranged from 26.16 to 62, with a mean of 43.7, suggesting the presence of mild drought conditions across all study years.
• Severe drought conditions were predominantly concentrated in the northeastern, southeastern, and parts of the northwestern regions, while areas around the Binalood highlands and Joghatai experienced milder drought.
• The most intense drought conditions (VHI classes 4, 5, and 6) were observed during June, July, and August.
• The composite SADFI vulnerability map, using a three-digit code from TCI, VCI, and VHI, showed that optimal conditions (code 1.1.1) covered 4.22% of the study area, mainly in northern, higher-latitude regions. The most unfavorable conditions (e.g., code 2.4.3) were observed in Davarzan and Sabzevar counties and southeastern parts of the province.
• Elevation was the most influential variable on VHI, showing a significant positive correlation (VHI increased with elevation), attributed to increased rainfall and decreased LST at higher elevations.
• Global Moran’s I (0.371) confirmed a moderate but significant positive spatial autocorrelation of VHI, indicating clustered patterns of vegetation health. LISA cluster maps identified distinct high-high (healthy) and low-low (stressed) zones, along with localized anomalies.

Contributions

• Introduction of the Standardized Agricultural Drought Frequency Index (SADFI), a novel composite index that integrates drought intensity, duration, and frequency into a single standardized value, providing a more comprehensive and multidimensional assessment than conventional single-aspect indices.
• Development of a robust remote sensing-based framework for agricultural drought risk monitoring and assessment, particularly valuable for arid and semi-arid regions.
• Enhanced capability to detect subtle drought patterns by integrating thermal and vegetation signals at fine spatiotemporal scales, improving early warning systems.
• Provision of a practical tool for guiding drought preparedness strategies, supporting adaptive agricultural management, and informing resource allocation.
• Proposal of SADFI as a foundational metric for a potential Global Agricultural Drought Atlas (GADA), facilitating standardized, global-scale drought monitoring.

Funding

The authors did not receive support from any organization for the submitted work.

Citation

@article{Eshaghi2025remote,
  author = {Eshaghi, Ameneh and Kamkar, Behnam},
  title = {A remote sensing-based framework for agricultural drought risk monitoring and assessment: introducing SADFI for disaster risk assessment in Northeastern Iran},
  journal = {Theoretical and Applied Climatology},
  year = {2025},
  doi = {10.1007/s00704-025-05888-z},
  url = {https://doi.org/10.1007/s00704-025-05888-z}
}