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Potential of Multi-Source Multispectral vs. Hyperspectral Remote Sensing for Winter Wheat Nitrogen Monitoring

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Gepubliceerd in:Remote Sensing vol. 17, no. 15 (2025), p. 2666-2685
Hoofdauteur: Chen Xiaokai
Andere auteurs: Miao Yuxin, Kusnierek Krzysztof, Li Fenling, Wang, Chao, Shi Botai, Wu, Fei, Chang Qingrui, Kang, Yu
Gepubliceerd in:
MDPI AG
Onderwerpen:
United States > US
Wheat
Accuracy
Nitrogen
Regression analysis
Satellite imagery
Least squares method
Remote sensing
Data processing
Unmanned aerial vehicles
Winter
Data integration
Monitoring
Machine learning
Spectral reflectance
Prediction models
Vegetation
Agricultural economics
Regression
Support vector machines
Precision agriculture
Sensors
Fertilizers
Remote sensing systems
Data collection
Crops
Winter wheat
Emission standards
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Samenvatting:Timely and accurate monitoring of crop nitrogen (N) status is essential for precision agriculture. UAV-based hyperspectral remote sensing offers high-resolution data for estimating plant nitrogen concentration (PNC), but its cost and complexity limit large-scale application. This study compares the performance of UAV hyperspectral data (S185 sensor) with simulated multispectral data from DJI Phantom 4 Multispectral (P4M), PlanetScope (PS), and Sentinel-2A (S2) in estimating winter wheat PNC. Spectral data were collected across six growth stages over two seasons and resampled to match the spectral characteristics of the three multispectral sensors. Three variable selection strategies (one-dimensional (1D) spectral reflectance, optimized two-dimensional (2D), and three-dimensional (3D) spectral indices) were combined with Random Forest Regression (RFR), Support Vector Machine Regression (SVMR), and Partial Least Squares Regression (PLSR) to build PNC prediction models. Results showed that, while hyperspectral data yielded slightly higher accuracy, optimized multispectral indices, particularly from PS and S2, achieved comparable performance. Among models, SVM and RFR showed consistent effectiveness across strategies. These findings highlight the potential of low-cost multispectral platforms for practical crop N monitoring. Future work should validate these models using real satellite imagery and explore multi-source data fusion with advanced learning algorithms.
ISSN:2072-4292
DOI:10.3390/rs17152666
Bron:Advanced Technologies & Aerospace Database