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Applied and Environmental Soil Science
Volume 2013 (2013), Article ID 798094, 11 pages
http://dx.doi.org/10.1155/2013/798094
Research Article

Soil-Landscape Modeling and Remote Sensing to Provide Spatial Representation of Soil Attributes for an Ethiopian Watershed

1Burie Agricultural College, P.O. Box Rissneleden 19, Sundbyberg, 17453 Stockholm, Sweden
2Amhara Regional Agricultural Research Institute (ARARI), P.O. Box 527, Bahir Dar, Ethiopia
3School of Natural Resources Management and Environmental Science, Haramaya University, P.O. Box 138, Dire Dawa, Ethiopia
4International Center for Agricultural Research in the Dry Areas (ICARDA), P.O. Box 950764, Amman 11195, Jordan

Received 5 June 2013; Revised 29 August 2013; Accepted 3 September 2013

Academic Editor: Davey Jones

Copyright © 2013 Nurhussen Mehammednur Seid et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Information about the spatial distribution of soil properties is necessary for natural resources modeling; however, the cost of soil surveys limits the development of high-resolution soil maps. The objective of this study was to provide an approach for predicting soil attributes. Topographic attributes and the normalized difference vegetation index (NDVI) were used to provide information about the spatial distribution of soil properties using clustering and statistical techniques for the 56 km2 Gumara-Maksegnit watershed in Ethiopia. Multiple linear regression models implemented within classified subwatersheds explained 6–85% of the variations in soil depth, texture, organic matter, bulk density, pH, total nitrogen, available phosphorous, and stone content. The prediction model was favorably comparable with the interpolation using the inverse distance weighted algorithm. The use of satellite images improved the prediction. The soil depth prediction accuracy dropped gradually from 98% when 180 field observations were used to 65% using only 25 field observations. Soil attributes were predicted with acceptable accuracy even with a low density of observations (1-2 observations/2 km2). This is because the model utilizes topographic and satellite data to support the statistical prediction of soil properties between two observations. Hence, the use of DEM and remote sensing with minimum field data provides an alternative source of spatially continuous soil attributes.