Ghosh et al. (2026) Spatio-temporal dynamics of drought using the remote-sensed data and Google Earth Engine (GEE) in the semi-arid region of India

Identification

• Journal: Dynamics of Atmospheres and Oceans
• Year: 2026
• Date: 2026-01-01
• Authors: Arijit Ghosh, Azizur Rahman Siddiqui
• DOI: 10.1016/j.dynatmoce.2025.101641

Research Groups

• Department of Geography, School of Liberal Arts, Noida International University, Uttar Pradesh, India
• Department of Geography, University of Allahabad, Prayagraj, Uttar Pradesh, India

Short Summary

This study investigates the severity, frequency, and spatial extension of drought occurrences in the semi-arid Bundelkhand region of India using remote-sensed data and Google Earth Engine, identifying 2001 and 2003 as the driest years and highlighting specific districts with high drought month counts.

Objective

• To recognize the severity and frequency of drought occurrences.
• To estimate the spatial extension of drought during diverse seasons in a semi-arid region of India.

Study Configuration

• Spatial Scale: Bundelkhand region (comprising MP Bundelkhand and UP Bundelkhand), India.
• Temporal Scale: Long-term data, with specific drought analysis for the period 2000–2023.

Methodology and Data

• Models used: Standardized Precipitation Index (SPI), Vegetation Condition Index (VCI), Google Earth Engine (GEE).
• Data sources: NASA Power precipitation data, remote-sensed data (processed via GEE).

Main Results

• The years 2001 and 2003 were identified as the driest, with SPI values of -2.23 in MP Bundelkhand and -1.73 in UP Bundelkhand, respectively.
• Banda district in UP experienced the highest number of drought months (127, including moderate, severe, and extreme), followed by Panna (116) and Lalitpur (113).
• Post-monsoon Vegetation Health Index (VHI) values ranged from a lowest of 17.23 to a highest of 74.23.
• The winter season (2000–2023) experienced severe drought, while other seasons generally experienced moderate droughts.

Contributions

• Provides a comprehensive spatio-temporal analysis of drought dynamics in the vulnerable semi-arid Bundelkhand region of India.
• Demonstrates the effective application of cloud-based platforms like Google Earth Engine for processing large datasets and real-time drought monitoring.
• Offers valuable insights for future researchers and policymakers in drought measurement and management strategies in India and globally.

Funding

Not specified in the provided text.

Citation

@article{Ghosh2026Spatiotemporal,
  author = {Ghosh, Arijit and Siddiqui, Azizur Rahman},
  title = {Spatio-temporal dynamics of drought using the remote-sensed data and Google Earth Engine (GEE) in the semi-arid region of India},
  journal = {Dynamics of Atmospheres and Oceans},
  year = {2026},
  doi = {10.1016/j.dynatmoce.2025.101641},
  url = {https://doi.org/10.1016/j.dynatmoce.2025.101641}
}