  Ground referencing is essential for supervised crop mapping. However,
conventional ground truthing involves extensive field surveys and post
processing, which is costly in terms of time and labor. In this study, we
applied a convolutional neural network (CNN) model to explore the efficacy of
automatic ground truthing via Google street view (GSV) images in two distinct
farming regions: central Illinois and southern California. We demonstrated the
feasibility and reliability of the new ground referencing technique further by
performing pixel-based crop mapping with vegetation indices as the model input.
The results were evaluated using the United States Department of Agriculture
(USDA) crop data layer (CDL) products. From 8,514 GSV images, the CNN model
screened out 2,645 target crop images. These images were well classified into
crop types, including alfalfa, almond, corn, cotton, grape, soybean, and
pistachio. The overall GSV image classification accuracy reached 93% in
California and 97% in Illinois. We then shifted the image geographic
coordinates using fixed empirical coefficients to produce 8,173 crop reference
points including 1,764 in Illinois and 6,409 in California. Evaluation of these
new reference points with CDL products showed satisfactory coherence, with 94
to 97% agreement. CNN-based mapping also captured the general pattern of crop
type distributions. The overall differences between CDL products and our
mapping results were 4% in California and 5% in Illinois. Thus, using these
deep learning and GSV image techniques, we have provided an efficient and
cost-effective alternative method for ground referencing and crop mapping.
