Robust Hyperspectral Estimation of Winter Wheat Aboveground Dry Biomass Using CARS-UVE Band Selection and Transfer-Oriented Validation

Field hyperspectral sensing can estimate crop biomass, but model ranking may depend strongly on validation design. We evaluated winter wheat aboveground dry biomass (AGDB) estimation using 84 canopy spectra collected across two growing seasons and seven nitrogen-management treatments in Shanxi, China. Six spectral inputs were compared with CARS-UVE band selection, partial least squares regression (PLSR), and support vector regression (SVR). Under a conventional 70/30 pooled split, SG + CARS-UVE + SVR gave the highest apparent accuracy (R2 = 0.8864, RMSE = 0.1174 kg m−2, RPD = 2.9665). This advantage was not stable. Across 20 SG-based repeated splits, CARS-UVE-SVR reached a mean R2 of 0.7413 with a 95% confidence interval of 0.6941–0.7885, similar to full-band PLSR (0.7448, 0.7058–0.7837), and pairwise tests showed no significant R2 advantage. Cross-year transfer further favored simpler latent-variable models: SG + CARS-UVE + PLSR reached R2 = 0.7577 in the 2021 → 2022 direction, whereas the pooled best SVR model dropped to R2 = 0.3402. A stricter same-window cross-year analysis produced weak or negative R2 values, showing that broad phenological biomass gradients supported much of the pooled accuracy. Recurrent selected regions occurred near 436–441 nm, 506–516 nm, and 711–713 nm. These findings suggest that repeated and transfer-oriented validation should be used routinely before hyperspectral biomass models are interpreted for cross-season crop monitoring.