Cropland parcels are fundamental units in agricultural production, and their precise delineation is critical for cadastral management and precision agriculture. However, heterogeneous agricultural landscapes with fragmented patches, complex land cover, and indistinct boundaries pose significant challenges for automated parcel delineation. Unmanned aerial systems (UASs) offer flexible, high-resolution multi-temporal spectral and elevation data, providing potential opportunities for mapping patched parcels. This study proposed an automated method for mapping patched cropland parcels using centimeter-level digital surface models (DSMs) and digital orthophoto maps (DOMs), validated at three typical sites in the Sichuan Basin. The method integrates (1) threshold segmentation of topographic relief to distinguish field surfaces from borders; (2) vegetation removal using a visible-band difference vegetation index (VDVI) mask; and (3) morphological refinement to produce high-precision vectorized field polygons. Results show that integrating bi-temporal UAS elevation and spectral data enables accurate, automated field extraction. Area-based mapping accuracy reached 98.1%, with an overall accuracy (OA) of 96.1% and a Kappa coefficient (KC) of 0.92. Field-count correctness was 93.3%, and the relative error of boundary length was 4.55%. Notably, parcels with regular shapes achieved even higher accuracy, with OA of 99.1% and KC of 0.98. By leveraging UAS-based elevation and spectral data, the proposed method can offer an alternative way to precise delineation of patched field boundary and provides reliable technical support for cadastral mapping and cropland surveys in agricultural regions.
Yong et al. (2026) studied this question.