Improving Crop-Type Mapping in Fragmented Agricultural Landscapes with Parcel Constraints and HLSS30-Derived Phenological Features
TL;DR
Results indicate that HLSS30-derived temporal and phenological features, when summarized within reliable crop parcel boundaries, provide an efficient and interpretable basis for regional multi-crop mapping in fragmented agricultural landscapes.
Abstract
Accurate crop-type mapping is essential for agricultural monitoring, but pixel-based products often suffer from within-field fragmentation, boundary noise, and limited consistency with field management units. This study developed a parcel-constrained crop classification approach using the HLSS30 product from the Harmonized Landsat and Sentinel 2 framework. HLSS30 data preprocessing and parcel-level feature extraction were conducted in Google Earth Engine, and parcel-level spectral, vegetation index, and phenological features were used to train a Random Forest classifier with feature selection and nested stratified cross-validation. A pixel-level classification experiment was used as the baseline for comparison. The HLSS30 time series captured class specific differences in canopy establishment, peak greenness, and senescence. Compared with the pixel-level baseline, parcel-level classification increased overall accuracy from 76.5% to 89.4%, increased Kappa from 0.690 to 0.859, and achieved a macro F1 score of 0.8677. Parcel constraints also reduced salt and pepper noise, improved field-level spatial coherence, and supported reliability interpretation through posterior entropy and parcel internal variability. These results indicate that HLSS30-derived temporal and phenological features, when summarized within reliable crop parcel boundaries, provide an efficient and interpretable basis for regional multi-crop mapping in fragmented agricultural landscapes.