Estimating wheat grain yield by assimilating phenology and LAI with the WheatGrow model based on theoretical uncertainty of remotely sensed observation

Assimilating remotely sensed observation into crop model solves the model parameterization in regional scale by considering the uncertainty of observation and simulation. However, a fixed value of observation uncertainty could not describe the spatio-temporal heterogeneity of observation error. This study developed a method to estimate wheat yield in China by assimilating remotely sensed observation into WheatGrow model. A dynamic background value of WheatGrow and a theoretical uncertainty of remotely sensed observation were proposed, and a strategy assimilating phenology and LAI hierarchically was applied at county and plot scales. At the county scale, phenology was estimated using the time sequence of PlanetScope images, sowing date was estimated using one-class support vector classification, heading and maturity date were estimated using function-fitting method. The revisiting cycle of PlanetScope was used to determine the theoretical uncertainty of remotely sensed observed sowing date (RS-sowing date), while the uncertainty in RS-heading and RS-maturity dates were calculated using field measurements. LAI was estimated using Sentinel-2 images. The theoretical uncertainty of remotely sensed observed LAI (RS-LAI) was developed using a synthetic dataset. At the plot scale, RS-heading date and RS-LAI were estimated using the same method as for the county scale based on images obtained with a RedEdge-MX camera carried by drone. The results indicated that the theoretical uncertainty sufficiently estimated the trend of LAI error with canopy density. The assimilation strategies based on theoretical uncertainty relieved the error propagation caused by poor parameterization of model background value and the RS obser-vation error in dense canopy, outperforming the results based on constant uncertainty. After hierarchical assimilation based on theoretical uncertainty, the root mean square errors of phenology, LAI and yield were 4.3 d, 0.75, and 825 kg/ha at the county scale, and 4.1 d, 0.57 and 969 kg/ha at the plot scale. These results showed that the hierarchical assimilation based on theoretical uncertainty could improve the accuracy of yield estimation.