Analysis of the Spatiotemporal Evolution Patterns and Driving Factors of Various Planting Structures in Henan Province Based on Mixed-Pixel Decomposition Methods

Understanding the spatiotemporal evolution patterns and drivers of cropping structures is crucial for adjusting cropping structure policies, ensuring the sustainability of land resources, and safeguarding food security. However, existing research lacks sub-pixel scale data on planting structure, where planted area data are mainly derived from manual counting results. In this study, remote sensing technology was combined with geostatistical methods to realize the spatiotemporal evolution of crop planting structure at sub-pixel scale. Firstly, the spatial distribution of the multiple cropping structure in Henan Province was extracted based on a mixed-pixel decomposition model, and spatiotemporal evolution of the crop planting structure was analyzed using a combination of Sen’s slope estimator and Mann–Kendall trend analysis, as well as centroid migration. Then, Pearson correlation coefficients were calculated to explore the contribution of driving factors. The results indicate the following: (1) from 2001 to 2022, the cropping structure in Henan Province shows a slightly obvious increase. (2) The centroid of different cropping structures migrates to the main production areas as a whole. (3) Among the driving factors, there was a positive correlation with the labor force and a negative correlation with the urbanization rate. This study provides new insights into the evolution of large-scale crop planting structures and offers significant theoretical and practical value for sustainable agricultural development and the optimization of agricultural planting structures.