Fine-scale crop type information is essential for agricultural monitoring, irrigation management, and food security assessment. This study mapped three major crops—wheat, corn, and sunflower—in the Hetao Irrigation District, China, using multi-temporal Sentinel-2 optical imagery and Sentinel-1 SAR observations at the parcel scale. A multi-source feature set, including spectral bands, vegetation and red-edge indices, moisture-related variables, radar backscatter coefficients, and derived radar features, was constructed from the full growing season. An LSTM network was used to learn temporal representations of crop phenological dynamics, and the resulting embeddings were then combined with traditional machine learning classifiers, including Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost), for final classification. The results show that the hybrid framework substantially improves classification performance compared with the corresponding non-LSTM classifiers. Among all tested models, XGBoost + LSTM achieved the best performance, with an overall accuracy of 93.61%, a Kappa coefficient of 91.66%, and a mean IoU of 87.41%. The class-wise F1-scores were 85.61% for wheat, 97.22% for corn, and 87.27% for sunflower. Additional experiments further confirmed the advantages of parcel-based aggregation in improving spatial consistency and reducing mixed-field noise. The proposed framework provides a promising parcel-scale workflow for crop type mapping in fragmented irrigation districts, while its transferability across years and regions still requires further validation.

Potato is an important staple crop both in China and worldwide, playing a critical role in ensuring global food security. Accurate mapping of the potato distribution is essential for detecting planting areas, estimating crop yields, and optimizing planting structures, thereby supporting sustainable agricultural development. However, remote sensing techniques for mapping potato distribution are still in their infancy, as most attention has been focused on the three major crops—maize, wheat, and rice. Consequently, this study proposed a cropland field-parcel-scale methodology for mapping potato distribution in Siziwang Banner, Inner Mongolia Autonomous Region, China. This methodology integrates edge detection, image segmentation, and machine learning algorithm, leveraging multi-temporal Sentinel-2 imagery to achieve accurately and effectively map the potato distribution. The results of detected edge from the four 10 m resolution Sentinel-2 bands (blue, green, red and near infrared band) revealed that Canny edge detection can provide more sufficient information for edge extraction than Sobel edge detection. The extracted edges of the Canny edge detection algorithm are more closed and complete than the others, which is extremely important for accurate image segmentation. A comprehensive and robust edge map was generated by applying a weighted aggregation method to the edges initially extracted from each of the four spectral bands. Subsequently, the watershed segmentation algorithm was applied to these aggregated edges to delineate field parcels and index thresholds used to differentiate the cultivated field parcels and non-cultivated field parcels. The methodology achieved an overall accuracy (OA) of 85% and an Intersection-Over-Union (IoU) ratio of 0.82. Finally, a random forest (RF) classifier was employed to map potato distribution by integrating spectral and index features at the field-parcel scale, achieving an overall mapping accuracy of 80%. The producer's accuracy (PA) and user's accuracy (UA) for potato mapping were 93.3% and 81.6%, respectively. As such, this study delivers a significant methodological support for mapping the fourth staple crop. The framework established here serves as a critical baseline for advancing crop type mapping, precise parcel extraction, and yield estimation, thereby supporting more strategic decision-making in potato cultivation and harvest.