Remote sensing techniques to predict salinity intrusion: application for a data-poor area of the coastal Mekong Delta, Vietnam

Salinity intrusion is a complex issue in coastal and estuarine areas. Currently, remote sensing techniques have been widely used to monitor water quality changes, ranging from inland river networks to deep oceans. The Vietnamese Mekong Delta is an important rice-growing area, and intrusion of saline water into irrigated freshwater-based agriculture areas is one of the most crucial constraints for agriculture development. This study aimed at building a numerical model to realize the salinity intrusion through the relationship between reflectance from the Landsat-8 Operational Land Imager images and salinity levels measured in situ. A total of 103 observed samples were divided into 50% training and 50% test. Multiple Linear Regression, Decision Trees and Random Forest (RF) approaches were applied in the study. The result showed that the RF approach was the best model to estimate salinity along the coastal river network in the study area. However, the large samples size needed was a significant challenge to circumscribe predicting ability of the RF model. The reflectance has a good correlation with salinity when locations (latitude-longitude) of salinity measured stations were added as a parameter of the Step-wise model with R-square 77.48% in training and 74.16% in test while Root Mean Square Error was smaller than 3.