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Utilization of the Fusion of Ground-Space Remote Sensing Data for Canopy Nitrogen Content Inversion in Apple Orchards

Zhang, Canting ; Zhu, Xicun ; Li, Meixuan ; Xue, Yuliang ; Qin, Anran ; Gao, Guining ; Wang, Mengxia ; Jiang, Yuanmao

Horticulturae, 2023-10, Vol.9 (10), p.1085 [Periódico revisado por pares]

Basel: MDPI AG

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Título:
Utilization of the Fusion of Ground-Space Remote Sensing Data for Canopy Nitrogen Content Inversion in Apple Orchards
Autor: Zhang, Canting ; Zhu, Xicun ; Li, Meixuan ; Xue, Yuliang ; Qin, Anran ; Gao, Guining ; Wang, Mengxia ; Jiang, Yuanmao
Assuntos: Accuracy ; Apple ; Apples ; Canopies ; canopy nitrogen content ; Correlation coefficient ; Correlation coefficients ; Crop management ; data fusion ; Data integration ; Fruit trees ; Fruits ; Geospatial data ; ground-space remote sensing ; Inversion ; Leaves ; Machine learning ; Mathematical models ; Monitoring ; Multisensor fusion ; Neural networks ; Nitrogen ; Nutrient status ; Orchards ; Parameters ; Remote sensing ; Remote sensing systems ; Response functions ; Simulation ; spatial inversion ; Spectral sensitivity ; Technical services ; Unmanned aerial vehicles
É parte de: Horticulturae, 2023-10, Vol.9 (10), p.1085
Descrição: 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.
Editor: Basel: MDPI AG
Idioma: Inglês

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Utilization of the Fusion of Ground-Space Remote Sensing Data for Canopy Nitrogen Content Inversion in Apple Orchards

Zhang, Canting ; Zhu, Xicun ; Li, Meixuan ; Xue, Yuliang ; Qin, Anran ; Gao, Guining ; Wang, Mengxia ; Jiang, Yuanmao

Basel: MDPI AG

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