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Drought is one of the common natural disasters with a wide range of occurrences in terms of space and time, and with varying levels of severity, that may result in economic damage and health issues to humans. This study focuses on assessing drought severity in the Central Highlands of Vietnam based on ground meteorological stations and multispectral remote sensing data. A Modification of the Normalized Difference Drought Index (MNDDI) was developed to enhance the effectiveness of remote sensing indices in the drought assessment. Results indicate that MNDDI outperforms Normalized Difference Drought Index and other investigated indicators, such as Normalized Difference Vegetation Index, Normalized Difference Latent Heat Index, and Normalized Difference Water Index, in representing the Earth's surface response to drought events. Correlations ranging from 0.85 to 0.63 were identified between MNDDI and various time scales of the commonly used meteorological drought indicator, namely the Standardized Precipitation Index, during the drought year of 2015. This work also reveals the superiority of MNDDI in portraying the response of land cover types to drought situations. The finding of a severe drought phenomenon in critical agricultural zones is highly consistent with the report from the Ministry of Agriculture and Rural Development of Vietnam. This study contributes valuable insights to the preliminary assessment of drought through remote sensing data, offering a foundation for precise drought outlooks and effective risk management strategies., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Saltwater intrusion has become one of the most concerning issues in the Vietnamese Mekong Delta (VMD) due to its increasing impacts on agriculture and food security of Vietnam. Reliable estimation of salinity plays a crucial role to mitigate the impacts of saltwater intrusion. This study developed a hybrid technique that merges satellite imagery with numerical simulations to improve the estimation of salinity in the VMD. The salinity derived from Landsat images and by numerical simulations was fused using the Bayesian inference technique. The results indicate that our technique significantly reduces the uncertainties and improves the accuracy of salinity estimates. The Nash-Sutcliffe coefficient is 0.74, which is much higher than that of numerical simulation (0.63) and Landsat estimation (0.6). The correlation coefficient between the ensemble and measured salinity is relatively high (0.88). The variance of the ensemble salinity errors (5.0 ppt 2 ) is lower than that of Landsat estimation (10.4 ppt 2 ) and numerical simulations (9.6 ppt 2 ). The proposed approach shows a great potential to combine multiple data sources of a variable of interest to improve its accuracy and reliability wherever these data are available., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)