Physical Prior Knowledge-Guided Reconstruction of Continuous Rice Canopy Spectra from UAV Multispectral Observations

Unmanned aerial vehicle (UAV) multispectral imaging supports efficient crop monitoring but provides limited information on continuous spectral features. This study reconstructed rice canopy reflectance from 400 to 1000 nm using four UAV bands and evaluated whether the recovered spectra remained consistent across scales. Multispectral reflectance data acquired at three flight altitudes were used to analyze their consistency with canopy hyperspectral reflectance, and two data simulation methods coupled with PROSAIL-D were employed to compare the constraining effects of prior knowledge data. Cubic spline, piecewise linear–logistic (PLL), partial least squares regression (PLSR), multilayer perceptron (MLP), and SE-ResNet-Att models were compared across different validation sets. The results showed that a flight altitude of 50 m provided the closest agreement with ground canopy spectra (RMSE, 5.085%). Machine learning models showed higher accuracy based on the C-Vine method than the Monte Carlo sampling method, owing to the narrower spectral datasets generated by the joint parameters. With full-prior training, PLSR performed best with ground-resampled inputs (RMSE, 1.352%), whereas SE-ResNet-Att achieved the lowest UAV-field RMSE of 4.298%. However, the proposed model showed limited advantages over simpler models in the leave-one-growth-stage-out validation and model ablation experiment, indicating that the benefits of increasing network complexity were limited. Although reconstructed spectra reproduced broad red-edge and near-infrared patterns, blue relative errors and red-edge absolute errors remained prominent with UAV inputs. The reconstructed red-edge position was correlated with measured values (r = 0.928) but showed a negative bias (ranging from −20 to −9 nm). Its exploratory correlations with canopy water and nitrogen contents (r = 0.589 and 0.727) decreased to 0.110 and 0.203 after adjustment for growth stage and leaf area index relationships. The method proposed in this study supports the extraction and exploratory analysis of additional spectral features from UAV multispectral imagery, but quantitative trait estimation and transfer across independent fields, years, and rice cultivars require further validation.