Soil-Landscape Estimation and Evaluation Program(SLEEP)to predict spatial distribution of soil attributes for environmental modeling

作     者：Feras M.Ziadat Yeganantham Dhanesh David Shoemate Raghavan Srinivasan Balaji Narasimhan Jaclyn Tech 

作者机构：Food and Agriculture Organization of the United Nations(FAO)00153 RomeItaly Department of Civil EngineeringTexas A&M UniversityTX 77840 College StationUSA Spatial Sciences LaboratoryTexas A&M UniversityCollege StationUSA De partment of Civil EngineeringIndian Institute of Technology Madras600036 ChennaiIndia International Center for Agricultural Research in the Dry Areas-ICARDABeirut 1108-2010Lebanon 

出 版 物：《International Journal of Agricultural and Biological Engineering》 (国际农业与生物工程学报（英文）)

年 卷 期：2015年第8卷第3期

页      面：158-172页

核心收录：

学科分类：09[农学] 0903[农学-农业资源与环境] 090301[农学-土壤学] 

基　　金：This model is a result of collaborative efforts between the International Center for Agricultural Research in the Dry Areas(ICARDA)and Texas A&M University The authors would like to acknowledge the financial support by the CGIAR Research Program on Water,Land and Ecosystems(WLE),USAID-linkages program,Middle East Water and Livelihood Initiative-WLI,and the Coca-Cola Foundation 

主　　题：GIS remote sensing terrain analyses watershed SWAT inverse distance weighted Kriging 

摘      要：The spatial distribution of surface and subsurface soil attributes is an important input to environmental *** attributes represent an important input to the Soil and Water Assessment Tool(SWAT),which influence the accuracy of the modeling *** ArcGIS-based tool was developed to predict soil attributes and provide inputs to *** essential inputs are digital elevation model and field *** soil data/maps can be used to derive observations when recent field surveys are not *** layers,such as satellite images and auxiliary data,improve the prediction *** model contains a series of steps(menus)to facilitate iterative *** steps are summarized in deriving many terrain attributes to characterize each pixel based on local attributes as well as the characteristics of the contributing *** 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 ***(Soil-Landscape Estimation and Evaluation Program)utilizes Pedo-transfer functions to provide the spatial distribution of the necessary unmapped soil data needed for SWAT *** application of the tool demonstrated acceptable accuracy and better spatial distribution of soil attributes compared with two spatial interpolation *** analysis indicated low sensitivity of SWAT prediction to the number of field observations when SLEEP is used to provide the soil *** demonstrates the potential of SLEEP to support SWAT modeling where soil data is scarce.