Estimating winter wheat biomass by coupling deep learning and hierarchical model using proximal remote sensing data

Timely and accurate estimation of component biomass of winter wheat, including leaf dry biomass (LDB), stem dry biomass (SDB), and reproductive organ dry biomass (RDB), is critical for crop growth monitoring and yield assessment. Canopy spectra mainly reflect leaf information, allowing for effective LDB estimation, whereas estimating SDB and RDB requires consideration of growth stage effects. To address this, we developed a hybrid biomass estimation framework by combining deep learning with biomass allocation law. Specifically, (1) a component biomass hierarchical (CBH) model was proposed based on accumulated growing degree days (AGDD) and biomass allocation laws; (2) a deep learning model (LBNet), based on two-dimensional fractional-order differential (2DFOD) hyperspectral images, was pre-trained on PROSAIL-simulated data and fine-tuned with field data to improve LDB estimation; and (3) the LBNet and CBH models were integrated to estimate and map component biomass across multiple scales. The hybrid framework achieved robust performance across interannual, regional, and UAV-based validations. For LDB, the root mean square error (RMSE) was 0.28–0.38 t ha −1 , with a normalized RMSE (nRMSE) of 9.79%–14.50%. The RMSEs for SDB and RDB were 0.88–1.63 t ha −1 (nRMSE = 11.05%–19.25%) and 0.76–2.22 t ha −1 (nRMSE = 9.29%–22.66%), respectively. Overall, the proposed method provides an effective approach for multi-stage biomass estimation of winter wheat and demonstrates highly promising potential for applications in smart agriculture and crop yield assessment.