Mapping corn dynamics using limited but representative samples with adaptive strategies

Mapping the corn dynamics at a large scale and multiple years is essential for global food security. Traditional mapping approaches by collecting training samples from field surveys are labor-intensive, challenging large-scale mapping of corn dynamics over the long term. This study developed an efficient approach to map large-scale corn dynamics in the main corn districts of the United States (US) using adaptive strategies for collecting high-quality training samples. First, this study proposed an automatic approach to collect stable and representative corn samples from the crop data layers (CDL) product. Then, this study improved the mapping performance of corn at a large scale and in earlier years by using adaptive strategies to collect limited (less than 500) but high-quality training samples. Finally, this study assessed and discussed the model performance across spaces and years using multi-source datasets from 2001 to 2020. Our results indicate the proposed approach with adaptive strategies can generate robust classification models with good performance in mapping large-scale corn dynamics. In our study area, the mean overall accuracy (OA) of corn is about 88% if using the CDL data as a reference. Besides, the R2 of corn areas at the county scale between our result and the surveyed acreage statistics is above 0.9, suggesting the proposed strategy is helpful for mapping corn dynamics across different years. The proposed approach demonstrated that limited but representative samples could map corn dynamics at a large scale with good performance, showing significant improvement compared to the traditional approach. It is feasible to map crop dynamics with multiple types by combining existing crop products with collected field samples, particularly in China, where crop products at the national scale are still lacking.