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Leaf nitrogen content is an important index to evaluate the growth of vegetation. It is of great significance to understand the change of nitrogen content in walnut canopy quickly, efficiently and accurately, so as to control the growth of trees in time and implement precise management. Taking the expanding period of walnut fruit as an example, the hyperspectral remote sensing image data of 5-year-old walnut forest land was obtained by GS-2 imaging spectrometer on the low altitude UAV remote sensing platform. The ENVI 5.3 software was used to identify and extract the walnut, soil and shadow in the observation range, and according to the spectral differences of different objects to find the non-intersection and large difference band between walnut, soil and shadow to determine the canopy range, and verify its extraction accuracy through support vector machine method. According to the NDVI, RVI and DVI vegetation indexes, the characteristic sensitive bands indicating the nitrogen content of the canopy were screened, and the correlation and estimation ability of 9 spectral parameters with the nitrogen content of the walnut canopy. Using the screened feature-sensitive bands as input variables of BP neural network model, the nitrogen content of walnut canopy was estimated. The screened feature-sensitive bands was used as the input variable of BP neural network model to estimate the nitrogen content of walnut canopy. The results showed that when the spectral reflectance at B100 (550.7) was more than 0.10 and that at B233 (779.4) was more than 0.70, the canopy range of walnut could be identified and determined effectively. Its drawing accuracy was as high as 96.43%. Based on the correlation between nitrogen content in walnut canopy and NDVI, RVI and DVI vegetation indexes, B33 (440.6), B165 (660.7), B186 (697.0) and B347 (986.4) were determined as the characteristics of indicating nitrogen content sensitive band. The estimation models based on the three reconstructed vegetation indices NDVI(986.4, 697.0), RVI(986.4, 697.0) , and DVI(660.7, 440.6) all reached extremely significant levels. Among them, NDVI(986.4,697.0) constructed by two bands of B347 (986.4) and B186 (697.0) was more close to the measured value in the diagnosis of nitrogen content in walnut forest canopy, and the accuracy of the estimation model was the highest. The estimation model based on BP neural network had higher estimation accuracy than the nine spectral parameters, the R 2 of verification reached 0.805. The estimation model had the highest accuracy and certain estimation reliability.