An Efficient Parcel-Center Sampling Strategy for Parcel-Scale Crop Classification Using Multiresolution Remote Sensing

Parcel-scale crop classification is fundamental to agricultural remote sensing and precision agriculture. The integration of detailed parcel information with medium-spatial-resolution remote sensing data has become an important technical approach to parcel-scale crop classification. For medium-spatial-resolution imagery, the mismatch between parcel boundaries and pixel size makes the parcel–pixel matching strategy a critical step in constructing parcel-level classification features. Existing full-pixels, pixel-center inclusion, and area-weighted strategies generally require identifying pixels associated with each parcel, determining spatial relationships, and performing statistical aggregation. When applied to thousands of parcels across multiple acquisition dates, repeated spatial overlay and multipixel aggregation can become a computational bottleneck in parcel-level feature construction. To address this issue, and assuming sufficient within-parcel feature consistency, a single pixel corresponding to an interior center point or a local window around that point is used to construct parcel-level classification features, thereby reducing the number of pixels involved in spatial matching and feature aggregation. Pringsewu Regency, Lampung Province, Indonesia, which has a highly fragmented parcel pattern, was selected as a case study. Sentinel-2, Landsat-8, Sentinel-1, and topographic data were integrated for parcel-scale crop classification. The results show that the parcel-center sampling strategy substantially reduced the cost of parcel-level feature construction under both the Sentinel-2 and Landsat-8 configurations. Compared with the three multipixel strategies, under the experimental conditions of this study, PCS achieved speedups of approximately 5.2–13.3-fold under Sentinel-2 and 7.3–17.1-fold under Landsat-8. Relative to the full-pixels strategy, OA increased by 0.55 percentage points under the Sentinel-2 configuration and decreased by 1.30 percentage points under the Landsat-8 configuration. Across parcel scales, the parcel-center sampling strategy generally maintained classification performance similar to that of the conventional multipixel strategies, and the resulting spatial patterns of the major crops were broadly consistent. Using a parcel-center pixel or local window can therefore retain sufficient classification information while substantially reducing the cost of parcel-level feature construction. This approach provides a simplified solution for efficient parcel-scale crop classification mapping in fragmented agricultural landscapes using medium-resolution imagery.