Feature replacement and fusion enhance the accuracy of canopy spectral monitoring models for winter wheat leaf water content

• The effect of pre-processing on canopy spectra has two-fold effect. • Suitable feature selection and modelling methods contribute to LWC monitoring. • Feature replacement/fusion improves LWC prediction accuracy and robustness. • For different bands, it is essential to choose an appropriate preprocessing method. Leaf Water Content (LWC) is vital for assessing winter wheat growth. Due to spectral band sensitivity variations, improving spectral feature fitting through preprocessing and optimization is a challenging issue. This study systematically investigates wavelength-dependent LWC sensitivity under multiple preprocessing operators to identify spectral bands highly correlated with winter wheat LWC under different preprocessing methods and evaluate band replacement (representation selection at fixed wavelength) and fusion strategies (constrained union within the same feature-selection method with wavelength de-duplication) for optimizing feature combinations and enhancing model accuracy and robustness. Various feature extraction techniques were applied to identify LWC-correlated spectral bands. Bands replacement (producing “Replacement Features”) and constrained feature fusion (producing “Fusion Features”) were introduced for feature optimization. Multiple modeling methods were employed to assess LWC monitoring performance using initial, replaced, and fused feature sets under a consistent train–test split with training-set cross-validation. The combination of first derivative (FD) preprocessing, Iterative Variable Subset Optimization (IVSO) feature selection, and Backpropagation Neural Network (BPNN) modeling yielded the best monitoring results. Fusion Features selected by IVSO achieved the highest accuracy, evaluated by R 2 , RMSE, and Akaike information criterion (AIC) (R 2 Train = 0.960, RMSE Train = 0.016, AIC Train = -1010.061; R 2 Test = 0.956, RMSE Test = 0.015, AIC Test = -98.362). Both band replacement and fusion enhanced model robustness and addressed spectral sensitivity issues. This study demonstrated the importance of preprocessing, feature optimization, and modeling in improving LWC monitoring. The proposed multi-preprocessing representation replacement and constrained fusion framework improved LWC estimation accuracy, supporting precision agriculture for winter wheat.