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dc.contributorZucca, Claudioen_US
dc.contributorMhaimeed, Ahmad S.en_US
dc.contributorAl-Shafie, Waleed M.en_US
dc.contributorFadhil, Ayad M.en_US
dc.contributorNangia, Vinayen_US
dc.contributorZhu, Fangheen_US
dc.contributorLiu, Guangpingen_US
dc.creatorWu, Weichengen_US
dc.identifier.citationWeicheng Wu, Claudio Zucca, Ahmad S. Mhaimeed, Waleed M. Al-Shafie, Ayad M. Fadhil, Vinay Nangia, Fanghe Zhu, Guangping Liu. (5/9/2018). Soil salinity prediction and mapping by machine learning regression in Central Mesopotamia, Iraq. Land Degradation and Development.en_US
dc.description.abstractSoil salinization affects crop production and food security. Mapping spatial distribution and severity of salinity is essential for agricultural management and development. This study was aimed to test the effectiveness of machine learning algorithms for soil salinity mapping taking the Mussaib area in Central Mesopotamia as an example. A combined dataset consisting of Landsat 5 Thematic Mapper (TM) and ALOS L‐band radar data acquired at the same time was used for fulfilling the task. Relevant biophysical indicators were derived from the TM images, and the soil component was retrieved by removing the vegetation contribution from the L‐band radar backscattering coefficients. Field‐measured salinity at the three corner plots of triangles were averaged to represent the salinity of these triangular areas. These averaged plots were converted into raster by either direct rasterization or buffering‐based rasterization into different cell size to create the training set (TS). One of the three triangle corners was randomly selected to constitute a validation set (VS). Using this TS, the support vector regression (SVR) and random forest regression (RFR) algorithms were then applied to the combined dataset for salinity prediction. Results revealed that RFR performed better than SVR with higher accuracy (93.4–94.2% vs. 85.2–89.4%) and less normalized root mean square error (NRMSE; 6.10–7.69% vs. 10.29–10.52%) when calibrated with both TS and VS. In comparison, prediction by multivariate linear regression (MLR) achieved in our previous study using the same datasets also showed less NRMSE than SVR. Hence, both RFR and MLR are recommended for soil salinity mapping.en_US
dc.publisherWiley (12 months)en_US
dc.sourceLand Degradation and Development;(2018)en_US
dc.titleSoil salinity prediction and mapping by machine learning regression in Central Mesopotamia, Iraqen_US
dc.typeJournal Articleen_US
cg.creator.idZucca, Claudio: 0000-0002-8636-0511en_US
cg.creator.idNangia, Vinay: 0000-0001-5148-8614en_US
cg.subject.agrovoccrop productionen_US
cg.subject.agrovocsoil salinityen_US
cg.subject.agrovocmachine learningen_US
cg.contributor.centerInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.contributor.centerMinistry of Agriculture Iraqen_US
cg.contributor.centerGuangdong Academy of Agricultural Sciences - GDAASen_US
cg.contributor.centerUniversity of Baghdad, College of Agricultureen_US
cg.contributor.centerEast China Institute of Technology - ECITen_US
cg.contributor.centerSalahaddin University Erbilen_US
cg.contributor.crpCGIAR Research Program on Water, Land and Ecosystems - WLEen_US
cg.contributor.funderInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.contributor.projectCGIAR Research Program on WLE (CRP 5) - WI/W2 Fundingen_US
cg.contributor.project-lead-instituteInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.coverage.regionWestern Asiaen_US
cg.isijournalISI Journalen_US
dc.identifier.statusOpen accessen_US
cg.journalLand Degradation and Developmenten_US

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