What question did this study set out to answer?

The study aims to analyze the interrelationships between meteorological and agricultural droughts in Bangladesh during the pre-monsoon season.

April 18, 2026Open Access

Remote sensing and GIS-supported framework of pre-monsoon drought assessment in Bangladesh (2000–2022) using CHIRPS-based SPI-3 and MODIS-derived vegetation and temperature indices

Key Points

The study aims to analyze the interrelationships between meteorological and agricultural droughts in Bangladesh during the pre-monsoon season.
Employed standardized precipitation index (SPI-3) to assess meteorological drought.
Utilized MODIS-derived indices: NDVI, VCI, TCI, and VHI for agricultural drought evaluation.
Conducted Pearson correlation analysis to explore relationships between meteorological and vegetation indices over three months.
Decline in SPI-3 throughout the pre-monsoon period, indicating lower rainfall than historical averages.
Improved agricultural conditions suggested by NDVI, VCI, and VHI between 2011 and 2020.
No significant relationship found between agricultural and meteorological droughts, possibly due to irrigation practices.

Abstract

Drought, a critical global concern, has a significant impact on agriculture, economies, and the environment. Monitoring droughts is essential for mitigating their adverse effects. However, despite numerous studies, the dynamics of meteorological drought and its correlation with agricultural drought remain unclear. This study aims to fill this gap by examining meteorological and agricultural droughts in Bangladesh during the pre-monsoon season (March–May) and exploring their interrelationships. To achieve this, we employed the standardized precipitation index (SPI-3) to assess meteorological drought, and Moderate Resolution Imaging Spectroradiometer (MODIS)-derived indices, including the normalized difference vegetation index (NDVI), vegetation condition index (VCI), temperature condition index (TCI), and vegetation health index (VHI), to analyze agricultural drought. We employed a matrix-based Pearson correlation method to assess the relationship between these factors over three months. The results indicate a decline in SPI-3 throughout the pre-monsoon period, suggesting lower rainfall than historical averages. However, vegetation-based indices such as NDVI, VCI, and VHI suggest improved agricultural conditions between 2011 and 2020, indicating an increase in crop cultivation in recent years. NDVI values exceeded 0.4, while VCI and VHI surpassed 40%, signifying minimal drought conditions in most areas. Pearson correlation analysis revealed no significant relationship between agricultural and meteorological droughts, as SPI-3 did not consistently align with vegetation-based indices. This discrepancy may be attributed to irrigation practices and extensive vegetation cover in certain regions. The findings of this study offer practical implications for authorities utilizing remote sensing-based indices for drought preparedness and management, equipping them with valuable insights for future research and informed policy decisions.

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