Retrieval of Nitrogen Content in Apple Canopy Based on Unmanned Aerial Vehicle Hyperspectral Images Using a Modified Correlation Coefficient Method
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Acquisition and Preprocessing
2.2.1. Hyperspectral Image Acquisition and Preprocessing
2.2.2. Apple Canopy Leaf Collection and Nitrogen Content Determination
2.3. Methodology Description
2.3.1. Extraction of Canopy Hyperspectral Images Based on NDCSI
2.3.2. Screening of Sensitive Wavelengths Using MCCM Method
- (1)
- Correlation coefficient calculation: a correlation analysis was conducted among the spectral data from the UAV hyperspectral image and nitrogen content.
- (2)
- Location of first sensitive wavelength determined: the wavelengths that are higher than 0.5 were correlated with the nitrogen content, screened out and arranged according to the wavelength order. The first wavelength with correlation higher than 0.5 was identified as the first sensitive wavelength.
- (3)
- Nitrogen content sensitive wavelength screening: the wavelength autocorrelation threshold was set, the correlation between the first sensitive wavelength and the remaining wavelengths was analyzed, and the position of the second sensitive wavelength was determined according to the threshold. A correlation analysis was conducted among the spectral data of the determined second sensitive wavelength and those of other wavelengths, and the position of the third sensitive wavelength was determined according to the autocorrelation threshold between the wavelengths. This step was repeated until all bands were covered, and finally all sensitive wavelengths of nitrogen content within the band range of the hyperspectral data were determined.
2.3.3. Construction of Spectral Characteristic Parameters
2.3.4. Model Establishment and Testing
3. Results
3.1. Determination of NDCSI Threshold and the Extraction of Apple Canopy Hyperspectral Images
3.2. Spectral Characteristics Analysis of Tree Canopy
3.3. Sensitive Wavelength Screening and Spectral Parameter Construction of Nitrogen Content Based on MCCM
3.3.1. Correlation Analysis between Canopy Spectral Reflectance and Nitrogen Content
3.3.2. Threshold Determination and Sensitive Wavelength Screening Based on MCCM
3.3.3. Construction of Spectral Parameters
3.4. Construction and Verification of Apple Tree Canopy Nitrogen Retrieval Model
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Sample | Sample Number | Max Value/% | Min Value/% | Average Value/% | Standard Deviation |
---|---|---|---|---|---|
Total | 100 | 2.882 | 2.373 | 2.628 | 0.104 |
Modelling Set | 67 | 2.882 | 2.373 | 2.621 | 0.113 |
Validation Set | 33 | 2.798 | 2.450 | 2.642 | 0.083 |
Degree | Gamma | Coef0 | Nu | Epsilon | Cashe size | Cost | Shrinking | Prob | p |
---|---|---|---|---|---|---|---|---|---|
3 | 2 | 0.001 | 0.5 | 0.001 | 100 | 1 | 1 | 1 | 0.01 |
Implied Network Layers | Enter the Number of Layer Nodes | Minimum Training Speed | Dynamic Parameter | SIGMOID | Allowable Error | Permission Iterative Number |
---|---|---|---|---|---|---|
1 | 6 | 0.1 | 0.6 | 0.9 | 0.0001 | 1000 |
Model Building | |||||
---|---|---|---|---|---|
Correlation Threshold | Sensitive Band/nm | R2 | RMSE | R2 | RMSE |
0.95 | 470, 474, 490, 514, 582, 634, 682 nm | 0.571 | 0.074 | 0.587 | 0.052 |
0.9 | 470, 510, 622, 686 nm | 0.501 | 0.080 | 0.582 | 0.053 |
0.85 | 470, 618, 686 nm | 0.546 | 0.081 | 0.487 | 0.055 |
0.8 | 470, 650 nm | 0.530 | 0.081 | 0.478 | 0.056 |
Spectral Characteristic Parameter | Correlation Coefficient | Spectral Characteristic Parameter | Correlation Coefficient |
---|---|---|---|
DDI(470;474;490) | 0.525 | DDI(474;490;682) | 0.609 |
DDI(470,474;514) | 0.544 | DDI(474;514;582) | 0.657 |
DDI(470,474;582) | 0.649 | DDI(474;514;634) | 0.697 |
DDI(470,474;634) | 0.672 | DDI(474;514;682) | 0.624 |
DDI(470,474;682) | 0.569 | DDI(474;582;634) | 0.735 |
DDI(470;490;514) | 0.560 | DDI(474;582;682) | 0.678 |
DDI(470;490;582) | 0.659 | DDI(474;634;682) | 0.657 |
DDI(470;490;634) | 0.678 | DDI(490;514;582) | 0.647 |
DDI(470;490;682) | 0.591 | DDI(490;514;634) | 0.694 |
DDI(470;514;582) | 0.641 | DDI(490;514;682) | 0.609 |
DDI(470;514;634) | 0.677 | DDI(490;582;634) | 0.728 |
DDI(470;514;682) | 0.605 | DDI(490;582;682) | 0.664 |
DDI(470;582;634) | 0.718 | DDI(490;634;682) | 0.641 |
DDI(470;634;682) | 0.662 | DDI(514;582;634) | 0.716 |
DDI(470;634;682) | 0.642 | DDI(514;582;682) | 0.640 |
DDI(474;490;514) | 0.579 | DDI(514;634;682) | 0.577 |
DDI(474;490;582) | 0.678 | DDI(582;634;682) | 0.537 |
DDI(474;490;634) | 0.701 |
MLR | PLS | SVM | BPNN | ||
---|---|---|---|---|---|
Training Set | R2 | 0.643 | 0.581 | 0.733 | 0.681 |
RMSE/% | 7.11 | 7.31 | 6.00 | 6.43 | |
nRMSE/% | 15.29 | 18.61 | 12.76 | 17.09 | |
MAE/% | 5.66 | 6.01 | 4.49 | 5.21 | |
Validation set | R2 | 0.576 | 0.547 | 0.671 | 0.670 |
RMSE/% | 6.42 | 5.53 | 4.73 | 4.64 | |
nRMSE/% | 26.23 | 20.93 | 14.83 | 15.19 | |
MAE/% | 5.81 | 4.20 | 3.98 | 3.60 |
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Li, M.; Zhu, X.; Li, W.; Tang, X.; Yu, X.; Jiang, Y. Retrieval of Nitrogen Content in Apple Canopy Based on Unmanned Aerial Vehicle Hyperspectral Images Using a Modified Correlation Coefficient Method. Sustainability 2022, 14, 1992. https://doi.org/10.3390/su14041992
Li M, Zhu X, Li W, Tang X, Yu X, Jiang Y. Retrieval of Nitrogen Content in Apple Canopy Based on Unmanned Aerial Vehicle Hyperspectral Images Using a Modified Correlation Coefficient Method. Sustainability. 2022; 14(4):1992. https://doi.org/10.3390/su14041992
Chicago/Turabian StyleLi, Meixuan, Xicun Zhu, Wei Li, Xiaoying Tang, Xinyang Yu, and Yuanmao Jiang. 2022. "Retrieval of Nitrogen Content in Apple Canopy Based on Unmanned Aerial Vehicle Hyperspectral Images Using a Modified Correlation Coefficient Method" Sustainability 14, no. 4: 1992. https://doi.org/10.3390/su14041992