Soil property analysis using principal components analysis, soil line, and regression models

Past research has attempted to relate surface characteristics of soils to reflectance from remotely sensed images to provide a means for quantifying spatial heterogeneity. Existing procedures have proven valuable, but no research has been performed to compare these techniques. The objective of this research was to compare existing methodologies, that is, principal components analysis (PCA), Chen et al.'s regression model, and the soil line Euclidean distance (SLED) technique, for quantifying spatial heterogeneity in soil surface organic matter (OM) and cation exchange capacity (CEC). The three existing techniques were compared using five bare soil images from three different silt loam to loam fields in the Midwest USA. At the same time as image acquisition, surface (upper 2.54 cm [1 in]) soil properties were measured in situ. Organic matter and CEC were highly correlated (R-2 > 0.70) to the first principal component (PC1) for three bare soil images, moderately correlated (R-2 > 0.40) for one image, and only slightly correlated (R-2 < 0.25) for the final image. The lower correlations were hypothesized to be because of the range in the soil OM and CEC and image exposure. Principal Component I accounted for approximately 95% of the total variance in all the fields; therefore, no correlation was observed between the upper 2.54 cm (1-in) surface soil properties and the second, third, or fourth principal components (PC2, PC3, and PC4, respectively). All three techniques equivalently predicted OM and CEC. However, PCA does not require field-specific regression or soil lines parameters. It is also suggested that PC1 can replace the soil line in a technique for identifying soil-sampling locations.