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The soil line is a linear relationship between the near‐infrared (NIR) and red (R) reflectance of bare soil as characterized by slope and intercept parameters. Vegetation indices use soil line parameters extensively in crop growth analyses. Research indicates that the soil line can be related to site‐specific soil conditions within a field, especially organic C content. This relationship may provide a means for directing soil sampling. However, these soil and crop growth remotely sensed predictions require accurate estimates of soil line parameters. Determining soil line parameters by manually extracting reflectance characteristics of bare soil pixels can be cumbersome. This research proposes an automated soil line identification routine capable of deriving soil line parameters from bare soil or vegetated remotely sensed images. The automated routine estimates soil line parameters by deriving a set of minimum NIR digital numbers across the R band range. Pixels that contradict soil line theory are removed through an iterative process. The routine was evaluated using bare soil images of two fields in the Midwest USA and 15 multispectral digital video images of South Texas grain sorghum fields dominated by vegetated cover. This research compared soil line parameters derived from the automated routine to actual soil line parameters obtained by extracting R and NIR digital numbers from identifiable bare soil pixels within the images and also by manually inspecting plots of R versus NIR digital numbers for all pixels within an image. The routine performed reasonably well in matching the estimated actual soil line parameters with minimal adjustment between images.

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Site-specific soil and crop management will require rapid low-cost sensors that can generate position-referenced data that measure important soil properties that impact crop yields. Apparent electrical conductivity (EC a ) is one such measure. Our main objective was to determine which commonly measured surface soil properties were related to EC a at six sites in the Texas Southern High Plains, USA. We used the Veris 3100 and Geonics EM-38 EC mapping systems on 12 to 47 ha areas in six center-pivot irrigation sites. Soil samples were taken from 0-150 mm on a 0.1 to 0.8 ha grid and analyzed for routine nutrients and particle size distribution. At four of the six sites, shallow EC a measured with the Veris 3100 (EC a-sh ) positively correlated to clay content. Clay content was negatively related with EC a-sh at one site, possibly due to low bulk density of the shallow calcic horizon at that site. Other soil properties that were often correlated with EC a included soil extractable Ca 2+ , Mg 2+ , Na + , CEC, silt and soluble salts. Extractable K + , NO 3 ) , SO 4 ) , Mehlich-3-P, and pH were not related to EC a . Partial least squares regression (PLS) of seven soil properties explained an average of 61%, 51% and 37% of the variation in observed shallow EC a-sh, deep EC a with the Veris 3100 (EC a-dp ) and EC a with the Geonics EM-38 (EC aem ), respectively. Including nugget, range and sill parameters from a spherical semivariance model of the residuals from PLS regression improved the fit of mixed models in 15 of 18 cases. Apparent EC, therefore can provide useful information to land-users about key soil properties such as clay content and extractable Ca 2+ , but that spatial covariance in these relationships should not be ignored.