As the major nutrient affecting crop growth, accurate assessing of nitrogen (N) is crucial to precise agricultural management. Although improvements based on ground and satellite data nitrogen in monitoring crops have been made, the application of these technologies is limited by expensive costs, covering small spatial scales and low spatiotemporal resolution. This study strived to explore an effective approach for inversing and mapping the distributions of the canopy nitrogen concentration (CNC) based on Unmanned Aerial Vehicle (UAV) hyperspectral image data in a typical apple orchard area of China. A Cubert UHD185 imaging spectrometer mounted on a UAV was used to obtain the hyperspectral images of the apple canopy. The range of the apple canopy was determined by the threshold method to eliminate the effect of the background spectrum from bare soil and shadow. We analyzed and screened out the spectral parameters sensitive to CNC, including vegetation indices (VIs), random two-band spectral indices, and red-edge parameters. The partial least squares regression (PLSR) and backpropagation neural network (BPNN) were constructed to inverse CNC based on a single spectral parameter or a combination of multiple spectral parameters. The results show that when the thresholds of normalized difference vegetation index (NDVI) and normalized difference canopy shadow index (NDCSI) were set to 0.65 and 0.45, respectively, the canopy’s CNC range could be effectively identified and extracted, which was more refined than random forest classifier (RFC); the correlation between random two-band spectral indices and nitrogen concentration was stronger than that of other spectral parameters; and the BPNN model based on the combination of random two-band spectral indices and red-edge parameters was the optimal model for accurately retrieving CNC. Its modeling determination coefficient (R²) and root mean square error (RMSE) were 0.77 and 0.16, respectively; and the validation R² and residual predictive deviation (RPD) were 0.75 and 1.92. The findings of this study can provide a theoretical basis and technical support for the large-scale, rapid, and non-destructive monitoring of apple nutritional status.

Utilizing multi-source remote sensing data fusion to achieve efficient and accurate monitoring of crop nitrogen content is crucial for precise crop management. In this study, an effective integrated method for inverting nitrogen content in apple orchard canopies was proposed based on the fusion of ground-space remote sensing data. Firstly, ground hyper-spectral data, unmanned aerial vehicles (UAVs) multi-spectral data, and apple leaf samples were collected from the apple tree canopy. Secondly, the canopy spectral information was extracted, and the hyper-spectral and UAV multi-spectral data were fused using the Convolution Calculation of the Spectral Response Function (SRF-CC). Based on the raw and simulated data, the spectral feature parameters were constructed and screened, and the canopy abundance parameters were constructed using simulated multi-spectral data. Thirdly, a variety of machine-learning models were constructed and verified to identify the optimal inversion model for spatially inverting the canopy nitrogen content (CNC) in apple orchards. The results demonstrated that SRF-CC was an effective method for the fusion of ground-space remote sensing data, and the fitting degree (R²) of raw and simulated data in all bands was higher than 0.70; the absolute values of the correlation coefficients (|R|) between each spectral index and the CNC increased to 0.55–0.68 after data fusion. The XGBoost model established based on the simulated data and canopy abundance parameters was the optimal model for the CNC inversion (R² = 0.759, RMSE = 0.098, RPD = 1.855), and the distribution of the CNC obtained from the inversion was more consistent with the actual distribution. The findings of this study can provide the theoretical basis and technical support for efficient and non-destructive monitoring of canopy nutrient status in apple orchards.