Abstract Drought is a period of below‐average precipitation in a given region, resulting in a shortage of water supply, whether atmospheric, surface, or groundwater. Countries like Bangladesh suffer from frequent drought due to deficient and uneven rainfall, poor or delayed rainfall, and high temperatures. This study introduces an integrated framework for assessing drought severity in Bangladesh by employing the analytical hierarchy process, a multi‐criteria decision‐making technique, to develop an integrated drought index (IDI). The remote sensing multi‐sensor derived datasets, such as long‐term moderate resolution imaging spectroradiometer (MODIS) satellite data (normalized difference vegetation index and land surface temperature), rainfall from CHIRPS datasets, and soil moisture from ERA 5 datasets, were used to quantify drought for the period 2001–2021. The IDI is generated by integrating multi‐spectral indices, such as vegetation condition index (VCI), temperature condition index (TCI), precipitation condition index (PCI), and soil moisture condition index (SMCI), using an analytical hierarchy process‐derived weightage with a consistency ratio of 9.5% and a consistency index of 0.009 value. The weightage or parameters priority comes out to be 19% of VCI, 9% of TCI, 67% of PCI, and 5% of SMCI. Subsequently, the agricultural integrated drought index (AIDI) was developed to assess drought conditions in agricultural areas specifically. To achieve this, an agricultural land mask was applied using the MODIS Land Cover Type product (MCD12Q1). Additionally, observed rainfall data were used to generate the standardized precipitation index (SPI), which correlated with the AIDI. The results demonstrated a strong correlation between AIDI and SPI, with a coefficient of determination ( R 2 ) of 0.75. The findings reveal significant spatial and temporal variability in drought conditions across Bangladesh. The northwestern region is particularly vulnerable due to delayed monsoons and inadequate water availability, while the southwestern region also experiences severe drought impacts driven by abnormal rainfall patterns. This integrated framework, combining satellite‐based remote sensing observations with land surface model outputs, provides a robust framework for early drought detection and monitoring, providing critical insights for water resource management and agricultural planning in the context of both national challenges and global climate variability.
Alam et al. (Thu,) studied this question.
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