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Winter wheat take-all is a quarantine disease that causes wheat to be significantly reduced or even rejected. Rapid and non-destructive monitoring of the spatial distribution of winter wheat take-all is of great significance for its prevention and control. The UAV-equipped imaging hyperspectral sensor was used as the remote sensing platform. The imaging hyperspectral image combined with the ground disease survey data was used to try to map the distribution of wheat take-all in the field scale. The quality of UHD185 spectral data was evaluated by synchronously acquired terrestrial ASD hyperspectral data. The statistical analysis and remote sensing inversion mapping techniques were used to calculate the differential spectral index (DSI) and ratio spectral index (RSI). Normalized difference spectral index (NDSI) and disease index (DI) were constructed to determine the coefficient equipotential map, and the optimal spectral index and DI were constructed to construct a linear regression model, and the partial least squares constructed with three indices were constructed. The accuracy and robustness of the prediction model constructed by regression method were compared. Finally, the model was tested with independent data. The results showed that the ASD spectral data of winter wheat canopy was significantly correlated with UHD185 spectral data, R2 was above 0.97, and the three spectral indices were compared with DI to construct a partial least squares regression model and the model verification results were obtained (R2=0.6292, RMSE is 10.2%, MAE is 16.6%). The results showed that DSI(R818,R534) had the highest contribution to the model with the formula for linear regression model of DSI (R818,R534) and DI as y=-6.4901x+1.4613 (R2=0.8605, RMSE is 7.3%, MAE is 19.1%), which was verified by independent samples for model accuracy (R2=0.76, RMSE is 14.9%, MAE is 11.7%, n=20). Finally, the model was used to invert the DI of the plot, and the spatial distribution map of winter wheat take-all was made. The research provided a technical basis for UAV hyperspectral remote sensing in the accurate monitoring and application of winter wheat take-all. It provided a theoretical basis for the future satellite remote sensing to explore large-scale monitoring of winter wheat take-all.