Summer Maize Leaf Area Index Estimation Using Approximate Days After Sowing and Dual-Domain Paired-Difference Features

Residue-soil mixing and reduced spectral sensitivity complicate summer maize leaf area index (LAI) estimation from unmanned aerial vehicle multispectral imagery. We compared full-plot and canopy domains, paired differences, neighborhood statistics, and approximate days after sowing (DAS), calculated from a common reference date. Five input schemes (B0–B4) and four algorithms were evaluated using 345 samples collected on five dates in one growing season. Spatial validation comprised ten repetitions of five-fold grouped cross-validation, with preprocessing and feature selection restricted to training data. For gradient boosting decision trees, R2 increased from 0.515 for B0 to 0.686 for B3 and 0.846 for B4, whereas RMSE decreased from 0.834 to 0.671 and 0.470. Low-LAI Bias decreased from 0.580 to 0.343 and 0.160. Fixed-capacity controls supported conditional improvements after adding paired differences and neighborhood statistics. Approximate DAS produced the largest sequential RMSE reduction, mainly through between-date adjustment. Time-baseline and training-fold residual analyses distinguish calendar-time adjustment from within-date predictive information. High-LAI underestimation and unseen-date errors delimit application beyond the sampled dates and production-field conditions. Within this defined scope, the framework provides a reproducible comparison of image-domain representations under heterogeneous production conditions.