The spatial distribution of crops is an important agricultural parameter, which is used to derive important information about crop productivity and food security. However, crop mapping on a large scale is challenging due to the low spatio-temporal information of satellite data, sparse sampling, and poor computational efficiency for massive data. To alleviate these problems, this study proposes a method based on discrete grids with machine learning to integrate GaoFen-1 and Sentinel-2 imagery. First, the proposed method fuses multi-source satellite data with similar observation characteristics to improve the spatial and temporal coverage of satellites. Second, a data augmentation technique based on a discrete grid framework was proposed to solve the problem of sparse samples. Finally, a machine learning algorithm in a discrete grid was introduced to improve processing efficiency and ensure the crop classification precision of large-scale remote sensing images. An experiment in the Sanjiang Plain area (approximately 108900 km²) of Northeast China showed that the proposed scheme benefited from a high spatio-temporal multi-source dataset and achieved good performance. Compared with a single data source, the accuracy of crop mapping using multi-source optical remote sensing data is higher, attaining up to 86 and 88 % in 2017 and 2018, respectively. Furthermore, the advantages of machine learning in discrete grids over large-scale areas are validated by evaluating the accuracy of different classifiers, which indicates the suitability of discrete grids in data augmentation and large-scale crop mapping. Finally, discrete grid technology offers a possibility for crop mapping over large-scale areas, and improves the processing efficiency of remote sensing big data. The findings in this study can contribute to studies on large-scale crop classification and serve as a reference to them.

作物的空间分布是一个重要的农业参数，用于获取有关作物生产力和粮食安全的重要信息。然而，由于卫星数据的时空信息低、采样稀疏以及海量数据的计算效率低下，大规模的作物制图具有挑战性。为了缓解这些问题，本研究提出了一种基于离散网格和机器学习的方法来集成 GaoFen-1 和 Sentinel-2 图像。首先，该方法融合了具有相似观测特征的多源卫星数据，以提高卫星的时空覆盖率。其次，提出了一种基于离散网格框架的数据增强技术来解决稀疏样本的问题。最后，引入离散网格中的机器学习算法，提高处理效率，保证大尺度遥感影像作物分类精度。三江平原地区试验（约10.89万公里）²) 的结果表明，该方案受益于高时空多源数据集并取得了良好的性能。与单一数据源相比，使用多源光学遥感数据的作物制图精度更高，2017年和2018年分别达到86%和88%。此外，通过评估不同分类器的准确性，验证了离散网格中机器学习在大范围区域中的优势，这表明离散网格在数据增强和大规模作物制图中的适用性。最后，离散网格技术为大范围区域的作物制图提供了可能，提高了遥感大数据的处理效率。