Cropland parcels mapping method and distribution characteristics in northeast china's black soil region based on meter-level remote sensing images and cloud platform

Cropland parcels (CPs) constitute the fundamental physical units of agricultural production and management. However, their regional-scale mapping from meter-level very-high-resolution (VHR) imagery remains challenging due to computational constraints, tile-induced boundary fragmentation, and semantic confusion associated with single-date observations. To address these challenges, we develop a cloud-based method for large-scale cropland parcel mapping from meter-level VHR imagery. This method consists of geospatial tiling, HRNet-based boundary extraction from VHR imagery, and parcel-level post-processing, including morphology-based parcel merging and Sentinel-2 phenology-based filtering. This framework enables efficient large-area processing while preserving parcel continuity and boundary integrity. Using this framework, we generate NECCPB-1, a high-resolution cropland parcel boundary dataset for the black soil region of Northeast China (NEC), delineating 32.4 million cropland parcels (≥900 m²) across 40 prefecture-level cities. Independent parcel-based validation indicates a mean extraction precision (EP) of 0.85, a mean area deviation CompD of 0.0042, and a mean intersection-over-union (IoU) of 0.90. In tile-overlap regions, the proposed parcel merging algorithm effectively improves parcel completeness and boundary continuity, achieving an IoU of 0.98. Phenology-based filtering further removes approximately 4.4 million non-cropland parcels (about 12%), reducing confusion with spectrally similar land covers. Cross-comparative analyses show that NECCPB-1 maintains stable accuracy across mountainous and non-mountainous regions, while its mapped cropland area exhibits a mean relative difference of −3.6% compared with the Third National Land Survey. Relative to seven widely used cropland products, NECCPB-1 achieves higher parcel completeness (IR = 0.94) and classification accuracy (precision = 0.93, recall = 0.91, F1-score = 0.92). Beyond mapping accuracy, NECCPB-1 reveals a clear south-to-north gradient in cropland parcel number and area across NEC. Overall, the proposed framework provides a transferable solution for large-scale parcel-level cropland mapping from VHR imagery and offers a stable spatial baseline for boundary-constrained agricultural monitoring and analysis.