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)

Pham Hoa,Dieu Tien Bui,Tien-Dat Pham,Mahdi Hasanlou,Le Hai,Nguyen Binh,Nguyen Giang

doi:10.3390/rs11020128

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.

Highlights

Soil salinity, which has significantly affected on agricultural activities worldwide, is considered as one of the major environmental hazards caused by natural or human-induced processes
It could be seen that GLCMVariance(γVo H) has the highest permutation-based mean squared error (MSE) reduction value (135.33) indicating that it is the most important variable for the study area
This research has evaluated the potential of Sentinel-1 Synthetic Aperture Radar (SAR) imagery and the five state-of-the-art machine learning algorithms to map soil salinity intrusion in the Ben Tre province located on the Mekong River Delta of Vietnam

Summary

Introduction

Soil salinity, which has significantly affected on agricultural activities worldwide, is considered as one of the major environmental hazards caused by natural or human-induced processes This phenomenon has become increasingly more severe due to the climate change impacts associated with the rising sea level [1,2]. Various studies have successfully employed remote sensing data to map soil salinity using multispectral optical sensors and hyperspectral data based on the correlation between several indices information derived from spectrum bands and soil reflectance spectra [4,5,6,7,8,9]. Several studies employed very high spatial resolution (VHS) i.e., the QuickBird and the IKONOS imageries to assess soil salinity using a variety of vegetation indices They pointed out that high spatial resolution data often produce better results compared to medium spatial resolution in mapping soil salinity [5,7]. A limited and very few available hyperspectral data resources have resulted in difficulties in mapping soil salinity in large areas

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Conclusion