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Soil maps derived from random or grid-based sampling schemes are often an important part of precision crop management. Sampling and soil analysis to derive such maps require a large investment of both time and money. Aerial photos have been used as a soil mapping aid for years. Studies have shown such an approach can be useful for defining management units in precision farming, but these studies are often limited to a single field, not an entire farming operation. In this study, multispectral airborne [green, red, near infrared (NIR), and thermal] and satellite (SPOT and Landsat TM) data were used to derive soil textural class maps for 350 ha of a 770 ha research and demonstration farm in Maricopa, Arizona. These maps were compared to soil textural analysis results from samples in the top 30 cm of the soil profile at an approximate grid spacing of 120 m. Differences in tillage, residue, soil moisture, etc. between fields limited the accuracy of spectral classification procedures when applied across the entire study area. However, using spectral classification procedures on a field-by-field basis, it was possible to map areas of soil textural class with reasonable accuracy. These results are specific to the study area and may not apply at other locations due to the numerous factors that can contribute to a soil’s spectral response. Classification procedures were also used with vegetation present over the study area later in the season. Resulting vegetation classes may be helpful in deciding if soil classes impact crop development enough to warrant different management practices.