Crop Type Classification by Simultaneous Use of Satellite Images of Different Resolutions

Accurate and timely identification of crop types has significant economic, agricultural, policy, and environmental applications. The existing remote sensing methods to identify crop types rely on remotely sensed images of high temporal frequency in order to utilize phenological changes in crop reflectance characteristics. However, these image sets generally have relatively low spatial resolution. This tradeoff makes it difficult to classify remotely sensed images in fragmented landscapes where field sizes are smaller than the resolution of imaging sensor. Here, we develop a method for combining high spatial resolution (high-resolution) data with images with low spatial resolution but with high time frequency to achieve a superior classification of crop types. The solution is implemented and tested on both synthetic and real data sets as a proof of concept. We show that, by incorporating high-temporal-frequency but low spatial resolution data into the classification process, up to 20% of improvement in classification accuracy can be achieved even if very few high-resolution images are available for a location. This boost in accuracy is roughly equivalent to including an additional high-resolution image to the temporal stack during the classification process. The limitations of the current algorithm include computational performance and the need for ideal crop curves. Nevertheless, the resulting boost in accuracy can help researchers create superior crop type classification maps, thereby creating the opportunity to make more informed decisions.