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dc.contributorDhanesh, Yegananthamen_US
dc.contributorShoemate, Daviden_US
dc.contributorSrinivasan, Raghavanen_US
dc.contributorNarasimhan, Balajien_US
dc.contributorTech, Jaclynen_US
dc.creatorZiadat, Feras M.en_US
dc.date2015-03-17en_US
dc.date.accessioned2016-04-24T09:36:29Z
dc.date.available2016-04-24T09:36:29Z
dc.identifierhttps://ijabe.org/index.php/ijabe/issue/view/48en_US
dc.identifierhttps://mel.cgiar.org/reporting/download/hash/ZeFv23ZEen_US
dc.identifier.citationFeras M. Ziadat, Yeganantham Dhanesh, David Shoemate, Raghavan Srinivasan, Balaji Narasimhan, Jaclyn Tech. (17/3/2015). Soil-Landscape Estimation and Evaluation Program (SLEEP) to predict spatial distribution of soil attributes for environmental modeling. International Journal of Agricultural and Biological Engineering, 8 (3), pp. 158-172.en_US
dc.identifier.urihttps://hdl.handle.net/20.500.11766/4660
dc.description.abstractThe spatial distribution of surface and subsurface soil attributes is an important input to environmental modeling. Soil attributes represent an important input to the Soil and Water Assessment Tool (SWAT), which influence the accuracy of the modeling outputs. An ArcGIS-based tool was developed to predict soil attributes and provide inputs to SWAT. The essential inputs are digital elevation model and field observations. Legacy soil data/maps can be used to derive observations when recent field surveys are not available. Additional layers, such as satellite images and auxiliary data, improve the prediction accuracy. The model contains a series of steps (menus) to facilitate iterative analysis. The steps are summarized in deriving many terrain attributes to characterize each pixel based on local attributes as well as the characteristics of the contributing area. The model then subdivides the entire watershed into smaller facets (subdivisions of subwatersheds) and classifies these into groups. A linear regression model to predict soil attributes from terrain attributes and auxiliary data are established for each class and implemented to predict soil attributes for each pixel within the class and then merged for the entire watershed or study area. SLEEP (Soil–Landscape Estimation and Evaluation Program) utilizes Pedo-transfer functions to provide the spatial distribution of the necessary unmapped soil data needed for SWAT prediction. An application of the tool demonstrated acceptable accuracy and better spatial distribution of soil attributes compared with two spatial interpolation techniques. The analysis indicated low sensitivity of SWAT prediction to the number of field observations when SLEEP is used to provide the soil layer. This demonstrates the potential of SLEEP to support SWAT modeling where soil data is scarce.en_US
dc.formatPDFen_US
dc.languageenen_US
dc.publisherChinese Society of Agricultural Engineeringen_US
dc.rightsCC-BY-4.0en_US
dc.sourceInternational Journal of Agricultural and Biological Engineering;8,(2015) Pagination 158,172en_US
dc.subjectterrain analysesen_US
dc.subjectinverse distance weighteden_US
dc.subjectswaten_US
dc.titleSoil-Landscape Estimation and Evaluation Program (SLEEP) to predict spatial distribution of soil attributes for environmental modelingen_US
dc.typeJournal Articleen_US
cg.subject.agrovocwatershedsen_US
cg.subject.agrovocgisen_US
cg.subject.agrovocremote sensingen_US
cg.subject.agrovockrigingen_US
cg.contributor.centerInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.contributor.centerFood and Agriculture Organization of the United Nations - FAOen_US
cg.contributor.centerTexas A&M University - TAMUen_US
cg.contributor.centerIndian Institute of Technology Madras, Department of Civil Engineeringen_US
cg.contributor.crpCGIAR Research Program on Dryland Systems - DSen_US
cg.contributor.funderUnited States Agency for International Development - USAIDen_US
cg.contributor.projectMiddle East North Africa Water and Livelihoods Initiative (WLI) - Regionalen_US
cg.contributor.project-lead-instituteInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.coverage.regionEastern Africaen_US
cg.coverage.countryETen_US
cg.contactferas.ziadat@fao.orgen_US
dc.identifier.statusOpen accessen_US
mel.project.openhttp://www.icarda.org/wli/en_US
mel.impact-factor1.267en_US
mel.funder.grant#United States Agency for International Development - USAID :EEM-G-00-04--00010-00en_US


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