An improved zoning crop mapping approach in complex agricultural landscapes considering crop heterogeneity.

Timely and accurate crop information is crucial for ensuring food security and promoting sustainable agricultural management. The characteristics of small-scale farming in China contribute to fragmented cropland and a diverse range of crop types, which present challenges for fine-scale crop mapping. This study considers the impact of landscape heterogeneity on crop mapping and proposes a zoning crop mapping method. Firstly, the method involves zoning based on the computation of landscape metrics. Subsequently, optimal feature schemes and classification algorithms are selected for crop mapping. The results indicate that compared to other single-model methods, the crop zoning mapping approach can enhance the accuracy of crop mapping in complex agricultural landscapes.