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Surface soil texture controls many important ecological, hydrological, and geomorphic processes in arid regions and is therefore important from a land-management perspective. Soil survey efforts have traditionally fulfilled this need, but they are constrained by the size, remoteness, and inaccessibility of many arid regions, which renders simple field measurements prohibitively expensive. This article compares several different predictive soil-mapping techniques with a sparse data set in order to develop surficial soil texture maps. Our results suggest that data collected at the landscape scale can be used as input to predictive soil-mapping techniques to create maps of soil texture at higher fidelity and a fraction of the cost than would be required using traditional methods.