Predictive Soil Maps Based on Geomorphic Mapping, Remote Sensing, and Soil Databases in the Desert Southwest

Abstract

We present an expert based system to rapidly predict the shallow soil attributes that control dust emissions in the arid southwest U.S. Our system’s framework integrates geomorphic mapping, remote sensing, and the assignment of soil properties to geomorphic map units using a soil database within a geographic information systems (GIS) framework. This expert based system is based on soil state factor-forming model parameters that include: (1) climate data, (2) landform, (3) parent material, and (4) soil age. The four soil-forming data layers are integrated together to query the soil database. To validate the accuracy of the expert based model and resultant predictive soil map, a blind test was performed at Cadiz Valley in the Mojave Desert, California. The desert terrain in Cadiz Valley consists of alluvial fans, fan remnants, sand dunes, and playa features. The test began with three users independently mapping an area of over 335 km² using 1:40,000-scale base maps to rapidly create geomorphic and age class layers, and then integrating these with climate and parent material layers. The results of the four data layers were then queried in the soil data base and soil attributes assigned to map unit layers. The soil-forming model presented here is geomorphic-based, and considers soil age as a significant factor in accurately predicting soil conditions in hyper arid to mildly arid regions. This work comprises a successful first step in the development of an expert-based system to map shallow soil conditions in support of dust emission models in remote desert regions.