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. 1. Introduction Quantitative information and spatial distribution of soil properties are among the main prerequisites for achieving sustainable land management. The accuracy of soil information determines, to a large extent, the reliability of land resources management decisions [1–3]. Conventional soil surveys are usually used to derive information about soils and their distribution [4]; however, limited areas are covered by detailed soil information mainly due to the high costs of surveys [5]. Furthermore, the spatial distribution of soil characteristics as represented by a conventional soil map does not reflect the distribution in the field because of the polygon-based mapping employed [6]. In polygon-based mapping, soils are assumed to be homogeneous within the polygon, and abrupt changes take place at the boundaries between polygons [7, 8]. Soils usually show a diffuse spatial distribution that is hard to address in polygon-based soil maps [9]. Many researchers have suggested continuous raster maps as a better alternative to mapping soils and their properties [6, 7, 9]. In soil science, the
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