Uncertainty propagation analysis of remote sensing data in a coupled crop-radiative transfer model using particle filter and winding stairs

Crop models can serve as decision-support tools, but their uncertainty must be accounted for. While previous research has shown effective calibration of crop models using remote sensing (RS) data, the remaining uncertainty is rarely quantified. This study investigated the propagation of errors associated with RS data in a coupled crop-radiative transfer model in two steps. First, the results of a Particle Filter (PF) process were examined to assess the uncertainty of the model parameters and outputs. Next, the Winding Stairs (WS) method was used to quantify the contribution of crop model parameters uncertainty to the total model uncertainty. The results show that parameters related to crop growth rate contribute more to the variance of simulated Leaf Area Index (LAI) and yield than the phenology-related parameters. These findings can guide future research to improve the model reliability by focusing on calibrating the parameters with a higher impact on model outcome uncertainty. • A coupled crop-radiative-transfer model was calibrated with remote sensing data. • The model output values and uncertainties were assessed using a particle filter. • Uncertainty propagation analysis was performed using the Winding Stairs method. • The suggested framework was applied to synthetic and real-life case studies. • Phenology contributed markedly less to the uncertainty than the crop growth rate.