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<p>Drought events significantly influence the regional dynamics of crop growth conditions under climate change. In Bangladesh, the Ganges-Jamuna-Brahmaputra Floodplains are increasingly diminished by the rising frequency and severity of drought events, posing significant challenges to agricultural systems. This study investigates long-term drought dynamics using multi-source satellite data and drought indices to evaluate the spatial and temporal impacts of drought alongside climate-driven changes in water use efficiency (WUE) across the agricultural ecosystem. The Vegetation Health Index (VHI), Standardized Precipitation Index (SPI), and Advanced Drought Response Index (ADRI) were used to evaluate drought severity from 2002 to 2022. VHI quantifies agricultural drought, SPI measures meteorological drought using remote sensing precipitation data, and ADRI provides an advanced drought response perspective. The SPI shows mild droughts nearly every year, with extreme drought in 2006 and moderate-to-severe droughts in 2006, 2013, 2014, and 2018, when experienced below-average annual precipitation of 2337 mm. Satellite-derived SPI exhibited a strong and highly significant correlation with weather station observations (R² = 0.94, p &lt; 0.0001). Additionally, MODIS-derived datasets were analyzed to explore the relationship between drought dynamics and WUE. Annual VHI trends indicated mild-to-moderate drought, with severe droughts in 2006, 2011, 2013, 2014, and 2016. Severe pre-monsoon droughts occurred in 2006, 2013, 2014, and 2018, while post-monsoon drought responses varied, benefiting <i>Boro</i> rice production in 2009, 2014, and 2018. The monsoon season remained largely drought-free due to sufficient rainfall. Strong correlations between VHI and ADRI (R² = 0.98, p&lt; 0.001) and between SPI from remote sensing and weather station data (r = 0.94, p &lt; 0.0001) validated the satellite-based approach. WUE averaged 13.47 g C m⁻² mm⁻¹, peaking at 19.87 g C m⁻² mm⁻¹ in 2004 and reaching a low of 7.11 g C m⁻² mm⁻¹ in 2002. These findings will contribute to mitigating drought impacts by enhancing agricultural strategies and refining climate change–focused agroecological zoning in Bangladesh and similar climatic regions across continental scales.</p> Graphical Abstract <p>The graphical abstract provides an overview of the study on long-term drought impacts on agriculture in Bangladesh using multi-source remote sensing data. The background and conceptual framework illustrate how temperature and precipitation influence drought dynamics through ecosystem processes linked to soil moisture, evapotranspiration, photosynthesis, and vegetation health. Data from the Moderate Resolution Imaging Spectroradiometer (MODIS), Soil Moisture Active Passive (SMAP), Tropical Rainfall Measuring Mission (TRMM), and Global Precipitation Measurement (GPM) satellites, along with weather station data, were used to derive key environmental variables such as land surface temperature (LST), normalized difference vegetation index (NDVI), normalized difference water index (NDWI), gross primary productivity (GPP), precipitation, soil moisture, and evapotranspiration (ET). The study employed three drought indices to explore drought dynamics: (i) the Vegetation Health Index (VHI), which incorporates the NDVI-based Vegetation Condition Index (VCI) and the LST-based Temperature Condition Index (TCI), (ii) the Standardized Precipitation Index (SPI) based on precipitation, and (iii) the Advanced Drought Response Index (ADRI), integrating temperature, soil moisture, precipitation, and vegetation parameters. Results from 2002 to 2022 reveal annual drought patterns, with spatial maps showing varying drought intensities and bar charts illustrating trends in VHI, SPI, and ADRI. In conclusion, the strong correlation R² = 0.94; p &lt; 0.001) between satellite-based drought indices and weather station data underscores the critical role of temperature and precipitation in drought monitoring, highlighting the value of remote sensing for agricultural drought assessment.</p> <p></p>

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Assessing the Impact of Long-Term Drought on Agriculture in Bangladesh Using Multisource Remote Sensing Data

  • 期刊论文

  • 发表日期：

    2025年8月22日

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  • Nur Hussain,
  • Md Saifuzzaman,
  • Mohd. Shamsul Alam,
  • Md Shamim Ahamed

摘要

Drought events significantly influence the regional dynamics of crop growth conditions under climate change. In Bangladesh, the Ganges-Jamuna-Brahmaputra Floodplains are increasingly diminished by the rising frequency and severity of drought events, posing significant challenges to agricultural systems. This study investigates long-term drought dynamics using multi-source satellite data and drought indices to evaluate the spatial and temporal impacts of drought alongside climate-driven changes in water use efficiency (WUE) across the agricultural ecosystem. The Vegetation Health Index (VHI), Standardized Precipitation Index (SPI), and Advanced Drought Response Index (ADRI) were used to evaluate drought severity from 2002 to 2022. VHI quantifies agricultural drought, SPI measures meteorological drought using remote sensing precipitation data, and ADRI provides an advanced drought response perspective. The SPI shows mild droughts nearly every year, with extreme drought in 2006 and moderate-to-severe droughts in 2006, 2013, 2014, and 2018, when experienced below-average annual precipitation of 2337 mm. Satellite-derived SPI exhibited a strong and highly significant correlation with weather station observations (R² = 0.94, p < 0.0001). Additionally, MODIS-derived datasets were analyzed to explore the relationship between drought dynamics and WUE. Annual VHI trends indicated mild-to-moderate drought, with severe droughts in 2006, 2011, 2013, 2014, and 2016. Severe pre-monsoon droughts occurred in 2006, 2013, 2014, and 2018, while post-monsoon drought responses varied, benefiting Boro rice production in 2009, 2014, and 2018. The monsoon season remained largely drought-free due to sufficient rainfall. Strong correlations between VHI and ADRI (R² = 0.98, p< 0.001) and between SPI from remote sensing and weather station data (r = 0.94, p < 0.0001) validated the satellite-based approach. WUE averaged 13.47 g C m⁻² mm⁻¹, peaking at 19.87 g C m⁻² mm⁻¹ in 2004 and reaching a low of 7.11 g C m⁻² mm⁻¹ in 2002. These findings will contribute to mitigating drought impacts by enhancing agricultural strategies and refining climate change–focused agroecological zoning in Bangladesh and similar climatic regions across continental scales.

Graphical Abstract

The graphical abstract provides an overview of the study on long-term drought impacts on agriculture in Bangladesh using multi-source remote sensing data. The background and conceptual framework illustrate how temperature and precipitation influence drought dynamics through ecosystem processes linked to soil moisture, evapotranspiration, photosynthesis, and vegetation health. Data from the Moderate Resolution Imaging Spectroradiometer (MODIS), Soil Moisture Active Passive (SMAP), Tropical Rainfall Measuring Mission (TRMM), and Global Precipitation Measurement (GPM) satellites, along with weather station data, were used to derive key environmental variables such as land surface temperature (LST), normalized difference vegetation index (NDVI), normalized difference water index (NDWI), gross primary productivity (GPP), precipitation, soil moisture, and evapotranspiration (ET). The study employed three drought indices to explore drought dynamics: (i) the Vegetation Health Index (VHI), which incorporates the NDVI-based Vegetation Condition Index (VCI) and the LST-based Temperature Condition Index (TCI), (ii) the Standardized Precipitation Index (SPI) based on precipitation, and (iii) the Advanced Drought Response Index (ADRI), integrating temperature, soil moisture, precipitation, and vegetation parameters. Results from 2002 to 2022 reveal annual drought patterns, with spatial maps showing varying drought intensities and bar charts illustrating trends in VHI, SPI, and ADRI. In conclusion, the strong correlation R² = 0.94; p < 0.001) between satellite-based drought indices and weather station data underscores the critical role of temperature and precipitation in drought monitoring, highlighting the value of remote sensing for agricultural drought assessment.