Knowledge-informed cascaded sampling for corn mapping across heterogeneous growing environments

Mapping corn distribution is challenging when samples are sparse and optical data is frequently cloud-contaminated. We propose a lightweight sample generation strategy that links peak signals with whole-season consistency to derive high-confidence corn samples. It includes three components: (1) aligning corn peaks within a phenology-aware time window, (2) integrating corn separability with two complementary indices—the corn spectral index, combining red-edge and shortwave infrared signals, and the corn radar index, based on Sentinel-1 polarization channels to ensure structural-dielectric consistency; and (3) enforcing whole-season similarity in timing, shape, and amplitude using a novel Peak- and Temporal-Weighted Dynamic Time Warping (PTW-DTW) algorithm. Compared to existing algorithms, PTW-DTW improves class separability, especially for non-corn crops with similar peaks but different shoulders. Experiments across three sites achieved overall accuracies of 91.40%, 91.70%, and 95.64%, outperforming single-source baselines and remaining stable across years and regions. County-level area estimates correlated well with official statistics (R2 > 0.98). The generated samples matched well with field data, achieving robust accuracy with fluctuations below 0.5%. Mixed them increased accuracy by about 2%. Relying only on standard composite and common input, this strategy provides consistent regional corn maps and transferable samples in data-limited settings.