The rapid survey and accurate mapping of the spatial distribution of crops using remote sensing are fundamental to modern precision agriculture. However, limitations in the acquisition, processing, and analysis of remote sensing images impact the mapping accuracy of traditional crop planting structures. Therefore, there is an urgent need to conduct spatial modeling and feature analysis for the uncertainty in crop classification. Using the Ningxia Yellow River irrigation area as a trial area and farmland parcels as the basic spatial units, this study classified crops on a parcel scale utilizing multi-source remote sensing data and machine learning algorithms. Then, an uncertainty calculation model was constructed based on mixed entropy, yielding the spatial distribution of the uncertainty of crop types in farmland parcels. Afterward, multi-source auxiliary data were employed to build a regression model for the uncertainty, and the potential impacts of related geographical variables on the uncertainty were explored. The experiment results indicate that 1.49 million vector units were constructed for the farmland parcels during the farmland extraction and classification session, yielding an overall crop classification accuracy of 0.80. The mapping results aligned well with the actual agricultural management units, and the classification results proved more better than the traditional pixel-based methods. The uncertainty in the parcel-scale crop classification was generally lower, with significant differences among crop types. The uncertainty was low for rice, vegetable plots, and alfalfa, relatively higher for wheat of single- and double-cropping patterns, and moderate for maize. The uncertainty in parcel-scale crop classification is influenced by various environmental factors such as planting structure and resource conditions, exhibiting the most significant correlations with crop type and water accessibility.