Improved Maize Mapping Through Optimizing Spatio-Temporal Feature Selection

Abstract

Accurate and timely mapping of maize distribution is crucial for national food security and sustainable agricultural development. However, maize classification in diverse agricultural landscapes is often hindered by subjective thresholds and limited feature representativeness in time-series remote sensing data. To overcome these limitations, we introduce an optimized feature selection time-weighted dynamic time warping (OFS-TWDTW) method that integrates multidimensional feature selection from Sentinel-2 imagery. Our approach begins by constructing robust standard maize curves through phenological and morphological sample screening to ensure sample reliability. We then apply the Relief algorithm to evaluate feature importance, followed by separability analysis and autocorrelation removal to select an optimal set of discriminative phenological features, enhancing classification efficiency. Finally, we employ TWDTW with an adaptive minimum distance classification to eliminate reliance on subjective thresholds. By conducting extensive evaluation across three climatically diverse regions in China (Dezhou, Pingliang, and Nenjiang), OFS-TWDTW achieved overall accuracies of 95.36%, 93.62%, and 90.59%, respectively. Notably, it demonstrated superior robustness over the traditional NDVI-based baseline, particularly in resolving spectral confusion between maize and soybean in complex landscapes. This method reduces misclassification and omission errors, offering a scalable, high-accuracy solution for large-scale crop mapping with broader applicability to other crops.