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Remote Sens. 2019, 11(2), 128;

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)

Ho Chi Minh City Institute of Resources Geography, Vietnam Academy of Science and Technology, Mac Dinh Chi 1, Ben Nghe, 1 District, Ho Chi Minh City 700000, Vietnam
Space Technology Institute, Vietnam Academy of Science and Technology, Hoang Quoc Viet 18, Cau Giay, Hanoi 10000, Vietnam
Division of Forest, Nature and Landscape, Department of Earth and Environmental Sciences, KU Leuven, 3000 Leuven, Belgium
Institute of Techniques for Special Engineering (ITSE), Military Technical Academy, Hoang Quoc Viet 236, Cau Giay, Hanoi 10000, Vietnam
Geoinformatics Unit, the RIKEN Center for Advanced Intelligence Project (AIP), Mitsui Building, 15th floor, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran 14174-66191, Iran
Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
Geographic Information System Group, Department of Business and IT, University of South-Eastern Norway, N-3800 Bø i Telemark, Norway
Author to whom correspondence should be addressed.
Received: 11 November 2018 / Revised: 4 January 2019 / Accepted: 7 January 2019 / Published: 11 January 2019
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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. View Full-Text
Keywords: soil salinity; sentinel; machine learning; Mekong river delta; Vietnam soil salinity; sentinel; machine learning; Mekong river delta; Vietnam

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Hoa, P.V.; Giang, N.V.; Binh, N.A.; Hai, L.V.H.; Pham, T.-D.; Hasanlou, M.; Tien Bui, D. 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). Remote Sens. 2019, 11, 128.

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