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<p>Accurate spatiotemporal information on soil moisture conditions is crucial for mitigating crop losses and ensuring food security in agricultural regions. This study presents a methodology for retrieving surface soil moisture (SSM) and assessing drought conditions in the Lower Mekong River Basin (LMRB) using Sentinel-1 Synthetic Aperture Radar (SAR) data. The data were processed for the dry seasons spanning 2015 to 2024 using a triangle-based approach. The validation of soil moisture estimates against the reference data showed moderate to strong agreement, with correlation coefficients (<i>r</i>) values ranging from 0.33 to 0.74, and root mean square errors (RMSE) between 0.03 and 0.12, and mean absolute errors (MAE) from 0.02 to 0.03, confirming the method’s reliability for regional SSM monitoring. Spatial analysis indicated persistent drought severity in coastal areas, while temporal trends revealed notable monthly and interannual fluctuations, with peak conditions occurring during 2015–2016, influenced by El Niño and atmospheric oscillations. Subsequently, monthly drought probabilities were computed and spatially integrated with cropping area data to support regional decision-making and adaptive strategies for water and agricultural management. This approach demonstrates the effectiveness of Sentinel‑1 SAR data for retrieving SSM and advancing regional decision-making and adaptive water and agricultural management practices.</p> Graphical Abstract <p></p> <p>This graphical abstract presents a robust methodology for monitoring SSM and assessing drought conditions in the LMRB using Sentinel-1 SAR observations. Data processing was carried out for the dry seasons between 2015 and 2024 employing a triangle-based approach. Soil moisture estimates were validated against reference data, confirming methodological robustness with correlation coefficients (r = 0.33–0.74), RMSE values of 0.03–0.12, and MAE values of 0.02–0.03. Spatial analysis revealed persistent drought severity in coastal areas, while temporal trends indicated notable fluctuations, with peak drought conditions observed during 2015–2016 due to El Niño influences. Monthly drought probabilities were computed and spatially integrated with cultivated areas to support adaptive strategies for water and agricultural management. The research findings highlighted the application of Sentinel-1 SAR data in regional decision-making, enabling effective drought monitoring and mitigation efforts to ensure food security in vulnerable agricultural regions.</p>

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Decoding Cropland Drought Dynamics: Insights from Sentinel-1 SAR Observations in the Lower Mekong River Basin

  • 期刊论文

  • 发表日期：

    2026年2月4日

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  • Nguyen-Thanh Son,
  • Chi-Farn Chen,
  • Huan-Sheng Lin,
  • Cheng-Ru Chen,
  • Chein-Hui Syu,
  • Yi-Ting Zhang,
  • Tsang-Sen Liu,
  • Lam-Dao Nguyen

摘要

Accurate spatiotemporal information on soil moisture conditions is crucial for mitigating crop losses and ensuring food security in agricultural regions. This study presents a methodology for retrieving surface soil moisture (SSM) and assessing drought conditions in the Lower Mekong River Basin (LMRB) using Sentinel-1 Synthetic Aperture Radar (SAR) data. The data were processed for the dry seasons spanning 2015 to 2024 using a triangle-based approach. The validation of soil moisture estimates against the reference data showed moderate to strong agreement, with correlation coefficients (r) values ranging from 0.33 to 0.74, and root mean square errors (RMSE) between 0.03 and 0.12, and mean absolute errors (MAE) from 0.02 to 0.03, confirming the method’s reliability for regional SSM monitoring. Spatial analysis indicated persistent drought severity in coastal areas, while temporal trends revealed notable monthly and interannual fluctuations, with peak conditions occurring during 2015–2016, influenced by El Niño and atmospheric oscillations. Subsequently, monthly drought probabilities were computed and spatially integrated with cropping area data to support regional decision-making and adaptive strategies for water and agricultural management. This approach demonstrates the effectiveness of Sentinel‑1 SAR data for retrieving SSM and advancing regional decision-making and adaptive water and agricultural management practices.

Graphical Abstract

This graphical abstract presents a robust methodology for monitoring SSM and assessing drought conditions in the LMRB using Sentinel-1 SAR observations. Data processing was carried out for the dry seasons between 2015 and 2024 employing a triangle-based approach. Soil moisture estimates were validated against reference data, confirming methodological robustness with correlation coefficients (r = 0.33–0.74), RMSE values of 0.03–0.12, and MAE values of 0.02–0.03. Spatial analysis revealed persistent drought severity in coastal areas, while temporal trends indicated notable fluctuations, with peak drought conditions observed during 2015–2016 due to El Niño influences. Monthly drought probabilities were computed and spatially integrated with cultivated areas to support adaptive strategies for water and agricultural management. The research findings highlighted the application of Sentinel-1 SAR data in regional decision-making, enabling effective drought monitoring and mitigation efforts to ensure food security in vulnerable agricultural regions.