Abstract: Quickly and accurately obtaining total nitrogen content（LNC）of canopy leaves in large-scale orchards is a basic requirement for achieving modern precision agriculture．Canopy spectral images of two typical orchards in Jingning County, Gansu Province were collected using a drone hyperspectral imager（391.9 to 1006.2 nm）, including artificially irrigated apple demonstration orchards and naturally rained apple orchards．Original spectral reflectance（OD）, reciprocal spectrum（RT）, logarithmic spectrum（LF）, and first-order differential spectrum（FD）of 160 canopy leaf samples from two regions were comprehensively compared．Difference spectral index（DSI）, soil adjusted vegetation index（SAVI）, and normalized differential spectral index（NDSI）for any combination of two spectral bands were constructed, correlation between three spectral indices and leaf nitrogen content was analyzed, and a univariate linear regression model and spectral indices were used to construct the best LNC estimation model for apple canopy in two regions．Research showed that correlation between FD-SAVI（825, 536）in artificial irrigation areas and LF-SAVI（854, 392）in natural rainfall areas was the strongest, and a univariate linear regression model was constructed based on FD-SAVI and LF-SAVI．FD-SAVI-ULRM estimation model constructed in artificial irrigation areas has the highest accuracy and validation set R² and E_RMSE were 0.6601 and 0.0678; LF-SAVI-ULRM estimation model constructed in natural rainfall areas has the highest accuracy, with validation set R² and E_RMSE was 0.6746 and 0.0665．This experiment used the LNC model to draw the LNC estimation maps of apple tree canopy leaves in two experimental areas, achieving precise control and refined management of total nitrogen content of orchard leaves．