Abstract: Karst mountainous areas are influenced by complex cloudy and rainy weather. This brings great difficulties to the extraction of planting structure information using the remote sensing technology. Sentinel-1-based crop identification has unique advantages in precision agriculture. It can obtain the information on regional main crops in time and accurately, thus playing a significant role in formulating agricultural policies and guiding agricultural production. This study investigated Guanling County based on Google images in 2020, Sentinel-1 time series data from April to August, and UAV remote sensing data. First, the plots were extracted using the D_LinkNet model. Then, the planting structures were classified based on the LightGBM module. Finally, the spatial differentiation characteristics of main crops and the influencing mechanism of planting structures in the study area were explored combined with geographic detectors. The results are as follows. ① The crops in Guanling County showed an uneven spatial distribution pattern of more crops in the northwest and less crops in the southeast. ② The influence of factor interaction was greater than that of single factors. The distribution of cultivated land was mainly influenced by traffic location and drainage capacity, followed by factors such as elevation and traffic location. ③ The extraction results of crop planting structures are consistent with the proportions shown in the statistical yearbook, with confusion-matrix overall precision of 0.87 and Kappa coefficient of 0.83. The results can help understand the formation mechanisms and differences in the spatial differentiation of different crop planting structures in Karst mountainous areas. Therefore, this study can provide a scientific basis for the optimization and adjustment of planting structures and the analysis of influencing factors.