A Field Parcel Scale Algorithm for Mapping Potato Distribution Using Multitemporal Sentinel-2 Images

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.