Improving crop rotation classification using a random forest model incorporating spatial heterogeneity

Abstract

Accurate and timely classification of crop rotations is essential to confront the issues of agricultural management and food crisis. Crop growth conditions generally exhibit a strong spatial heterogeneity pattern, resulting in crop growth characteristics varying with locations, limiting the classification accuracy of crop rotation. To overcome this limitation, an improved method named random forest based on rotation zoning strategy (RF_RZS) that classifies crop rotations under the consideration of spatial heterogeneity is proposed. In RF_RZS, the regionalization with dynamically constrained agglomerative clustering and partitioning method is used to adaptively mine the spatial homogeneous subzones of soil organic carbon (SOC), which represent the spatial heterogeneity pattern of crop rotation given the highly correlated relationship between crop rotation and SOC. The Boruta algorithm is employed to select the optimal feature subset within each subzone. A random forest classification method is applied to categorize crop rotations within the subzones. Two integrated indexes, OA_OZ and Kc_OZ, are proposed to evaluate the comprehensive performance of RF_RZS. Furthermore, a series of traditional methods is employed for comparison and evaluation. Results demonstrate that the OA_OZ and Kc_OZ of the RF_RZS method are 0.90 and 0.88, respectively, which are increased by 3–23% and 3–27%, respectively, compared with those of traditional classification methods. This research involves mixed cropping pattern of multiple rotational crops, and the proposed methodology can provide an effective guide for the classification of rotational crops with a fragmented distribution and a complex cropping structure.