Digital Soil Mapping in the Absence of Field Training Data: A Case Study Using Terrain Attributes and Semiautomated Soil Signature Derivation to Distinguish Ecological Potential

Spatially explicit data for soil properties governing plant water availability are needed to understand mechanisms influencing plant species distributions and predict plant responses to changing climate. This is especially important for arid and semiarid regions. Spatial data representing surrogates for soil forming factors are becoming widely available (e.g., spectral and terrain layers). However, field-based training data remain a limiting factor, particularly across remote and extensive dry lands. We present a method to map soils with Landsat ETM+ imagery and high-resolution (5 m) terrain (IFSAR) data based on statistical properties of the input data layers that do not rely on field training data. We then characterize soil classes mapped using this semiautomated technique. The method distinguished spectrally distinct soil classes that differed in subsurface rather than surface properties. Field evaluations of the soil classification in conjunction with analysis of long-term vegetation dynamics indicate the approach was successful in mapping areas with similar soil properties and ecological potential.