Enhanced Feature Extraction From Assimilated VTCI and LAI With a Particle Filter for Wheat Yield Estimation Using Cross-Wavelet Transform.

To further reveal the relationships between different variables and yield at each growth stage of winter wheat, an approach for estimating regional yields of winter wheat at multiple time scales was developed by assimilating the CERES-Wheat model simulations and remotely sensed observations. Specifically, the particle filter assimilation algorithm was chosen to assimilate the simulated soil moisture at the depth of 0–20 cm and leaf area index (LAI) and MODIS retrieved vegetation temperature condition index (VTCI) and LAI. The resonance periods of time series assimilated VTCIs and LAIs at different growth stages of winter wheat with crop yield were analyzed separately using the cross-wavelet transform to determine the variation relationships between the assimilated variables and yield at multiple time scales, and the calculated weights of assimilated VTCI and LAI at each growth stage of winter wheat were used to establish a yield estimation model. Both assimilated VTCI and LAI could comprehensively integrate the effects of the CERES-Wheat model simulations and remotely sensing observations, and cross-wavelet transformed time series VTCIs and LAIs at each growth stage had specific resonance periods with the time series yields, regardless of whether they were assimilated or not. Compared with the unassimilated variables, assimilated VTCI and LAI were given greater weights at the key growth stages, namely VTCI at the jointing and heading-filling stages and LAI at the heading-filling and milk maturity stages, enhancing feature extraction and the accuracy of yield estimation was improved.