Soil Salinity Mapping Using SAR Sentinel-1 Data and Advanced Machine Learning Algorithms: A Case Study at Ben Tre Province of the Mekong River Delta (Vietnam)

Abstract

Soil salinity caused by climate change associated with rising sea level is considered as one of the most severe natural hazards that has a negative effect on agricultural activities in the coastal areas in most tropical climates. This issue has become more severe and increasingly occurred in the Mekong River Delta of Vietnam. The main objective of this work is to map soil salinity intrusion in Ben Tre province located on the Mekong River Delta of Vietnam using the Sentinel-1 Synthetic Aperture Radar (SAR) C-band data combined with five state-of-the-art machine learning models, Multilayer Perceptron Neural Networks (MLP-NN), Radial Basis Function Neural Networks (RBF-NN), Gaussian Processes (GP), Support Vector Regression (SVR), and Random Forests (RF). For this purpose, 63 soil samples were collected during the field survey conducted from 4–6 April 2018 corresponding to the Sentinel-1 SAR imagery. The performance of the five models was assessed and compared using the root-mean-square error (RMSE), the mean absolute error (MAE), and the correlation coefficient (r). The results revealed that the GP model yielded the highest prediction performance (RMSE = 2.885, MAE = 1.897, and r = 0.808) and outperformed the other machine learning models. We conclude that the advanced machine learning models can be used for mapping soil salinity in the Delta areas; thus, providing a useful tool for assisting farmers and the policy maker in choosing better crop types in the context of climate change.

Using Machine Learning for Estimating Rice Chlorophyll Content from In Situ Hyperspectral Data

“…Gaussian process regression (GPR) is a powerful machine learning algorithm and has been widely used in remote sensing community, including estimating aboveground forest biomass [29] and soil salinity [30]. GPR is suitable for solving problems with a small sample size; when the sample size is large or has high dimensional features it becomes inefficient.…”

Assessing Spatiotemporal Drought Dynamics and Its Related Environmental Issues in the Mekong River Delta

“…Besides, a limited number of meteorological stations in the MRD also creates a challenge to monitor drought at the pixel level. Although the climatic conditions and vegetation are quite similar over this delta, criteria such as land use and land cover (LULC) types and pattern play a crucial role in drought variations [57,73,80]. Hence, it is recommended to define appropriate drought index for the region based on the multi-parameter perspective and through the application of remote sensing data and techniques.…”

“…ET, PET, EVI, and LST), (ii) assessment of the local correlation among each other (considering also their standardized values), (iii) development of a new drought index based on the concept of the drought severity index (DSI) [24], and (iv) pattern dynamics and trend analysis in agricultural drought. The Mekong River Delta was selected as a case study because of the impact of drought along with salinity during the dry season on the region [57,73]. Besides, the comparison of EDSI and other drought indices was performed to test our hypothesis and to enhance its capabilities.…”

Mapping of Soil Total Nitrogen Content in the Middle Reaches of the Heihe River Basin in China Using Multi-Source Remote Sensing-Derived Variables

“…A wide range of information can be extracted from SAR data [14], the most common of which is the backscatter coefficient. The backscattering intensity of SAR data can be used to obtain information about surface properties and has been widely used in soil science research to obtain information about soil roughness [15], soil moisture [16], and soil salinity [17]. The application of SAR images in mapping soil properties depends on the sensitivity of backscattering intensity to changes in soil moisture and land surface conditions [18].…”