Show simple item record

dc.contributor.authorPham Viet, Hoa
dc.contributor.authorNguyen Vu, Giang
dc.contributor.authorNguyen An, Binh
dc.contributor.authorLe Vu Hong, Hai
dc.contributor.authorTien-Dat, Pham
dc.contributor.authorMahdi, Hasanlou
dc.contributor.authorTien Bui, Dieu
dc.date.accessioned2019-11-05T09:41:36Z
dc.date.available2019-11-05T09:41:36Z
dc.date.created2019-01-07T11:23:57Z
dc.date.issued2019
dc.identifier.citationRemote Sensing. 2019, 11 (2)nb_NO
dc.identifier.issn2072-4292
dc.identifier.urihttp://hdl.handle.net/11250/2626540
dc.descriptionLicensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) licensenb_NO
dc.description.abstractSoil 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.nb_NO
dc.language.isoengnb_NO
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleSoil 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)nb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionpublishedVersionnb_NO
dc.rights.holder© 2019 by the authorsnb_NO
dc.source.pagenumber21nb_NO
dc.source.volume11nb_NO
dc.source.journalRemote Sensingnb_NO
dc.source.issue2nb_NO
dc.identifier.doi10.3390/rs11020128
dc.identifier.cristin1651363
cristin.unitcode222,57,1,0
cristin.unitnameInstitutt for økonomi og IT
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal