Transferable spectral models for high-throughput estimation of winter wheat net photosynthetic rate: a cross-scale approach using leaf and UAV hyperspectral data

Introduce Net photosynthetic rate (NPR) is a key indicator of photosynthetic capacity that directly governs the production and accumulation of biomass. Non-destructive sensing techniques based on multi-scale sensors (e.g. field spectroradiometers and UAV platforms) face challenges in simultaneously achieving high accuracy, strong transferability, and high-throughput monitoring. Methods This study addresses the urgent need for cross-scale extension of spectral models to bridge this methodological gap. We focused on estimating the NPR of winter wheat using leaf hyperspectral reflectance and extending the estimation model to a UAV platform by statistically characterizing and correcting spectral discrepancies between field spectroradiometer and UAV hyperspectral measurements. At first, the raw spectral reflectance was transformed into relevant vegetation indices and extracted sensitive features using Competitive Adaptive Reweighted Sampling (CARS) and Successive Projections Algorithm (SPA). Then, estimation models for the NPR were constructed using Random Forest (RF) and Partial Least Squares Regression (PLSR) methods. Finally, the performance of the eight estimation models was compared using coefficient of determination (R 2 ) and Root Mean Square Error (RMSE). The UAV images from different growing seasons were input into the optimal model to generate spatial monitoring maps. Results The results showed that transforming raw spectral reflectance into vegetation indices significantly improved model performance. RF exhibited notably higher accuracy than PLSR, with the VI-SPARF model achieving the best predictive performance. Through effective calibration, the system achieved cross-scale application from ground-based spectrometers to UAV hyperspectral imaging. Conclusion Overall, this study confirms the robustness and transferability of spectral-physiological relationships across temporal scales and sensing platforms, providing methodological support for operational, high throughput monitoring of crop photosynthesis.