Cropland resources are essential for the provision of food production, which is one of the most fundamental needs of human life. Change detection (CD) technology enables the dynamic monitoring of high-resolution cropland resource images acquired through remote sensing satellite sensors. However, current CD methods are not capable of extracting meaningful change information from dense and continuously distributed cropland. In addition, the common feature fusion processing often results in information redundancy and the loss of key features. Therefore, we propose a cross-attention guided group aggregation network (CAGNet) to achieve effective cropland CD. Specifically, we adopt a cross-attention (CA) module to enhance the capability of extracting and characterizing the features of the changed region, reducing the influence of noise and pseudo-change on CD. To alleviate the loss of key information during the multiscale feature fusion process and thus improve the CD performance, we design a group aggregation (GA) module that gradually groups and aggregates the bitemporal features from coarse to fine. Finally, we use a fully convolutional network to obtain the detailed CD results. Furthermore, we demonstrate the effectiveness of knowledge transfer in the field of CD. It allows the models to obtain the underlying mechanisms and characterization capabilities of changed features on the building CD dataset in advance, which significantly improves the performance of various methods on the cropland CD dataset. The experimental results show that CAGNet’s quantitative metrics results on the cropland dataset (CL-CD) outperform the other ten benchmarked methods, achieving an F1-score of 79.53%.