Bayesian merging of numerical modeling and remote sensing for saltwater
intrusion quantification in the Vietnamese Mekong Delta
Abstract
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 0.73, which is much higher than that of
numerical simulation (0.69) and Landsat estimation (0.67). The
correlation coefficient between the merged and measured salinity is
relatively high (0.75). The variance of the ensemble salinity errors
(2.57 ppt2) is lower than that of Landsat estimation
(3.65 ppt2) and numerical simulations (8.69
ppt2). The proposed approach in this study 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.