A Landscape-Clustering Zoning Strategy to Map Multi-Crops in Fragmented Cropland Regions Using Sentinel-2 and Sentinel-1 Imagery with Feature Selection

Crop mapping using remote sensing is a reliable and efficient approach to obtaining timely and accurate crop information. Previous studies predominantly focused on large-scale regions characterized by simple cropping structures. However, in complex agricultural regions, such as China’s Huang-Huai-Hai region, the high crop diversity and fragmented cropland in localized areas present significant challenges for accurate crop mapping. To address these challenges, this study introduces a landscape-clustering zoning strategy utilizing multi-temporal Sentinel-1 and Sentinel-2 imagery. First, crop heterogeneity zones (CHZs) are delineated using landscape metrics that capture crop diversity and cropland fragmentation. Subsequently, four types of features (spectral, phenological, textural and radar features) are combined in various configurations to create different classification schemes. These schemes are then optimized for each CHZ using a random forest classifier. The results demonstrate that the landscape-clustering zoning strategy achieves an overall accuracy of 93.52% and a kappa coefficient of 92.67%, outperforming the no-zoning method by 2.9% and 3.82%, respectively. Furthermore, the crop mapping results from this strategy closely align with agricultural statistics at the county level, with an R 2 value of 0.9006. In comparison with other traditional zoning strategies, such as topographic zoning and administrative unit zoning, the proposed strategy proves to be superior. These findings suggest that the landscape-clustering zoning strategy offers a robust reference method for crop mapping in complex agricultural landscapes.