<p>Accurate classification of cropland and crop types is essential for agricultural management in arid irrigation districts but remains challenging due to spectral similarity among crops and interference from heterogeneous non-cropland backgrounds. To address these challenges, this study develops a hierarchical framework for cropland and crop-type mapping using Sentinel-2 time-series data on the Google Earth Engine platform. A two-stage strategy is adopted, in which cropland is first delineated using a support vector machine with a radial basis function kernel (SVM_RBF) based on multi-temporal spectral features and indices, followed by crop classification within the cropland mask using a random forest (RF) model incorporating phenology-driven temporal features. Using Sentinel-2 imagery (10&#xa0;m resolution) and ground-truth samples from the Qingtongxia Irrigation District, the proposed approach achieves an overall accuracy of 94.61% for cropland classification. The overestimation of cropland area is reduced to 9.7%, compared with 17–19% in existing high-resolution land-cover products. For major crops such as maize and rice, classification accuracies exceed 86%, representing an improvement of 5–10% over single-stage classification approaches. This study also provides a systematic comparison between the derived cropland map and existing high-resolution land-cover products, highlighting the challenges that such products may encounter when applied to heterogeneous irrigation-district environments. Overall, this study provides an interpretable and practical framework for improving crop mapping in complex irrigation environments.</p>