Prediction of wheat stem biomass using a new unified model driven by phenological variable under remote-sensed canopy vegetation index constraints

Timely and accurate prediction of stem dry biomass (SDB) is crucial for monitoring crop growing status. However, conventional biomass estimation models are often limited by the influence of crop growth phase, which significantly restricts their temporal and spatial transferability. This study aimed to develop a semi-mechanistic stem biomass prediction model (PVWheat-SDB) using phenological variable (PV) to accurately predict winter wheat SDB across different growth stages. The core of the model is to predict SDB using PV under remote-sensed canopy vegetation indices (VIs) constraint. The results demonstrated that VIs can quantify the variations in stem growth equations under different planting conditions and varieties. The developed a PVWheat-SDB model using normalized difference red edge (NDRE) and accumulated growing degree days (AGDD) performed well for SDB predictions, with R2, RMSE, nRMSE and MAE values of 0.88, 75.48 g/m2, 8.04 % and 55.36 g/m2 for the validation datasets of field spectral reflectance, and 0.82, 81.76 g/m2, 11.22 % and 62.82 g/m2 when transferred to unmanned aerial vehicle (UAV) hyperspectral images. Furthermore, the model can not only estimate SDB at the current growth stage, but also predict SDB of subsequent phenological stages. The growth stage stacking strategy indicated that the model accuracy improves significantly as more growth stages are incorporated, especially during the reproductive stages. These results all highlight the robustness and transferability of the PVWheat-SDB model in accurately predicting SDB across different growing seasons and growth stages.