Abstract: Based on digital images of unmanned aerial vehicle, the effects of “image-spectrum” fusion indexes based on spectral information and texture features on the estimation of nitrogen nutrition index in winter wheat were investigated, which provided a new method for accurate estimation of nitrogen nutrition status. Digital images of unmanned aerial vehicle and corresponding biomass and nitrogen content data were used. The correlation between image indexes, texture features and nitrogen nutrition index was firstly analyzed, and then the image indexes and texture features were multiplied or divided and fused to form “image-spectrum” fusion indexes, the correlation was also analyzed between “image-spectrum” fusion indexes and nitrogen nutrition index, and “image-spectrum” fusion indexes which sensitive to nitrogen nutrition index were selected based on the integration of grey relation analysis and variance inflation factor. Finally, the ability of image indexes, texture features and “image-spectrum” fusion indexes to estimate nitrogen nutrition index was estimated by partial least square analysis. The results showed that the correlation between “image-spectrum” fusion indexes and nitrogen nutrition index had been greatly improved than the correlation between image indexes, texture features and nitrogen nutrition index. The estimation accuracy of the nitrogen nutrition index model constructed by the “image-spectrum” fusion indexes (R2 equal to 0.644 3) was higher than the models constructed by image indexes (R2 equal to 0.593 8) and texture features (R2 equal to 0.584 5), and the verification result of model which constructed by the “image-spectrum” fusion indexes had the smallest root mean square error of 0.114. The “image-spectrum” indexes based on the fusion of spectral information and texture features could effectively improve the inversion accuracy of the winter wheat nitrogen nutrition index, and provided an effective idea for the inversion of winter wheat nitrogen nutrition diagnosis.