This study aims at evaluating the performance of seven highly spatial resolution remote sensing data in litchi canopy nitrogen content estimation. The litchi canopy reflectance were collected by ASD field spectrometer. Then the canopy spectral data were resampled based on the spectral response functions of each satellite sensors (Geo-eye, GF-WFV1, Rapid-eye, WV-2, Landsat 8, WV-3, and Sentinel-2). The spectral indices in literature were derived based on the simulated data. Meanwhile, the successive projection algorithm (SPA) was used to extract the sensitive variables. The partial least square regression (PLSR) was used to develop the nitrogen estimation model. The results indicated that the Worldview-3 and Sential-2 provided the better prediction of nitrogen content (R2c=0.60, RMSEc=0.18, R2cv=0.55, RMSEcv=0.20) than the other simulated satellite data. The bands in visible and near infrared region played an important role in nitrogen estimation since the absorption of chlorophyll. And the usage of bands in SWIR together with bands in VNIR can improve the performance of nitrogen estimation model.