Synergistic Fusion of Vegetation Indices and Texture for Enhanced Cropland Mapping: A Spatio-Temporal Machine Learning Perspective

This study aims to automate sample data acquisition and identify the optimal feature combination from remote sensing imagery for delineating cropland boundaries. The objective is to enable real-time, automated extraction of cropland extent, thereby providing critical data for precision agricultural management and crop pest and disease monitoring. Utilizing high-resolution Sentinel-2 time-series data, we established 14 experimental groups based on distinct feature sets: original spectral features, vegetation index (VI) features, VI-derived texture features, and their combinations. Leveraging initial samples automatically procured from a comprehensive database, experiments were conducted using the LightGBM ensemble classifier to pinpoint the most effective experimental configuration for cropland extraction. Key findings include: (1) Temporal feature combinations derived from statistical domains outperform the direct stacking of multitemporal data features. (2) The importance of VIs for cropland extraction, ranked in descending order, is: NDTI > NDVI > LSWI > REP > NDBI > EVI. (3) Experimental groups incorporating texture features demonstrated superior accuracy compared to those relying solely on VI features, with average improvements in Accuracy, F1-score, and ROC AUC of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3.46 \%, 3.48 \%$</tex>, and 3.47 %, respectively.