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Soil & Water Management & Conservation

Analyzing Digital Terrain Attributes to Predict Soil Attributes for a Relatively Large Area

Dep. of Land, Water and Environment, Faculty of Agriculture, Univ. of Jordan, P.O. Box 13693, Amman 11942, Jordan

^* Corresponding author (fziadat{at}ju.edu.jo)

Accurate information about soil attributes, presented in a spatially continuous form, is prerequisite for many land resources management applications. The availability of detailed soil maps and its ability to supply such information for modern tools and applications are questionable. Some alternatives, based on using terrain attributes derived from digital elevation model (DEM) to predict soil attributes are investigated in this study. A study area of 148 km² in the northern part of Jordan was used. The area is covered by detailed soil map and 2193 field observations, from which the soil attributes were extracted. Terrain attributes derived from 20-m resolution DEM were utilized to predict soil attributes by implementing different statistical and clustering techniques. The use of multiple linear regression models within small watershed subdivisions enabled the prediction of soil depth for 89.3% of the field observations within ±50 cm, the water-holding capacity (WHC) for 75.8% within ±50 mm cm^–1 and the surface cover percentage for 78.7% within ±10%. The models also predicted surface cover type for 94.5% of the field observations, erosion type for 48.4%, erosion class for 98.0%, and soil texture for 90.3%, within one class difference between predicted and field estimated classes. Comparing these results with estimates of soil attributes using the soil map indicated that the modeling of the soil-landscape relationships within small watershed subdivisions is a promising approach to predict soil attributes for large areas. An important feature is the spatial distribution of the predicted soil attributes, which is provided in more detailed form than what the soil map provides.

Abbreviations: CTI, compound topographic index • DEM, digital elevation model • RMSE, root mean square errors • WHC, water-holding capacity