Crop mapping in countries with small-scale farming: a case study for West Shewa, Ethiopia

Remote sensing is nowadays considered to be a valuable input for the annual collection of crop statistics. Derived crop maps can serve as a baseline for yield or area estimation or to target next year's census. For subsistence farming, where small parcels are mixed with other land use, crop mapping remains very challenging. This article evaluates the potential of discriminating crops in West Shewa, an area with small-scale farming in central Ethiopia. A hard classification of high-resolution (30m) images, yielding good results for commercial farming, could not deal with mixed pixels due to the small parcels. Very high resolution (4m) images have a more appropriate pixel size, although they only cover subsets of the region. The very high resolution classification was used to calibrate a neural network for sub-pixel classification of the high resolution images. The accuracies were not satisfactory, but did at least demonstrate the potential of this approach.