High-resolution, remotely sensed imagery of bare soil has been successfully used to quantitatively map the spatial variation of the organic-C concentrations (SOC) of surface soil (Chen et al. 2000, Chen et al 2005). This method requires each field to be sampled and mapped separately. Upon examination of remotely sensed images that consist of several fields, some fields look similar with regard to image properties such as the color histogram. By using similarity analysis, it may be possible to group similar fields, analyzing and mapping them together, thereby reducing costs further. The objective of this study was to examine image similarity of agricultural fields, relate image similarity to soil properties, and map SOC for a group of fields at one time. Three types of features (feature vectors), including color histograms, color slope magnitudes, and Haar wavelets, were extracted to analyze field similarity with computer programming. Two methods, similarity clustering with Euclidean distance and similarity matching with the Ward neural networks were used for matching between extracted features. Dissimilarity distance (for the clustering method) and coefficient of determination (for the neural network method) were used for similarity ranking of images (fields). Based on the similarity result, soil organic carbon maps of two crop fields were created with a single processing.