Modeling and Prediction of Soil Organic Matter Content Based on Visible-Near-Infrared Spectroscopy
Abstract
:1. Introduction
2. Material and Methods
2.1. Soil Sample Collection
2.2. Visible-Near-Infrared Spectra Collection
2.3. Modeling and Optimization
2.3.1. Spectral Preprocessing and Band Optimization
2.3.2. Modeling Methods and Model Evaluation
3. Results
3.1. Partition of Sample Sets
3.2. Spectral Preprocessing
3.3. Wavelength Selection
4. Conclusions and Discussion
Author Contributions
Funding
Conflicts of Interest
References
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Absolute Water Content (%) | Bulk Density (g/cm3) | Saturated Water Capacity (%) | Capillary Water Capacity (%) | Non-Capillary Porosity (%) | Capillary Porosity (%) | Total Porosity (%) | |
---|---|---|---|---|---|---|---|
Average | 31.61 | 0.76 | 80.82 | 59.92 | 15.58 | 44.93 | 60.52 |
SD | 8.00 | 0.09 | 10.47 | 9.03 | 3.70 | 5.57 | 4.88 |
Max | 49.53 | 1.03 | 97.54 | 75.95 | 24.58 | 54.08 | 68.54 |
Min | 7.83 | 0.60 | 62.23 | 43.25 | 8.24 | 31.92 | 49.10 |
Plot No. | Number of Samples | Maximum (g/kg) | Minimum (g/kg) | Average Value (g/kg) | Standard Deviation (g/kg) |
---|---|---|---|---|---|
1 | 15 | 11.9713 | 8.1776 | 10.294 | 1.1384 |
2 | 15 | 11.4979 | 6.8753 | 8.942 | 1.6408 |
3 | 15 | 9.1034 | 4.7914 | 6.923 | 1.2043 |
4 | 15 | 15.6771 | 5.9419 | 9.319 | 2.6095 |
5 | 15 | 13.5342 | 7.4258 | 9.646 | 1.6054 |
6 | 15 | 15.9396 | 6.7213 | 12.248 | 3.1646 |
7 | 15 | 21.6961 | 7.3095 | 14.7311 | 4.3775 |
Total | 105 | 21.6961 | 4.7914 | 10.3005 | 3.3790 |
Sample Grouping | Number of Samples | Minimum (g/kg) | Maximum (g/kg) | Average (g/kg) | Standard Deviation (g/kg) | Variance (g/kg) |
---|---|---|---|---|---|---|
Calibration set | 70 | 3.0344 | 21.6956 | 10.9455 | 3.6987 | 1.3678 |
Validation set | 35 | 5.8685 | 20.8697 | 9.4811 | 3.5534 | 1.2624 |
Spectral Preprocessing | Calibration Models | Validation Models | ||||
---|---|---|---|---|---|---|
nLVs | R2 | RMSEP | nLVs | R2 | RMSEP | |
Original Spectrum | 5 | 0.7781 | 0.1655 | 2 | 0.4992 | 0.1789 |
S-G Smoothing | 6 | 0.7968 | 0.1584 | 2 | 0.4284 | 0.1911 |
SNV | 4 | 0.7640 | 0.1707 | 1 | 0.4142 | 0.1935 |
MSC | 4 | 0.7620 | 0.1715 | 1 | 0.4131 | 0.1936 |
1st Derivative | 1 | 0.2541 | 0.3035 | 6 | 0.9699 | 0.0438 |
2nd Derivative | 1 | 0.2332 | 0.3078 | 1 | 0.4781 | 0.1826 |
S-G-SNV | 6 | 0.8082 | 0.1539 | 1 | 0.3603 | 0.2022 |
S-G-MSC | 6 | 0.8072 | 0.1543 | 1 | 0.3595 | 0.2023 |
1st Derivative -SNV | 1 | 0.3018 | 0.2937 | 1 | 0.4930 | 0.1800 |
1st Derivative -MSC | 1 | 0.1268 | 0.3284 | 1 | 0.0069 | 0.2519 |
2nd Derivative -SNV | 1 | 0.2580 | 0.3027 | 1 | 0.4603 | 0.1857 |
2nd Derivative -MSC | 2 | 0.3227 | 0.2892 | 9 | 0.7670 | 0.1220 |
Spectral Preprocessing | Number of Combinations | Number of Selected Variables | Percentage of the Original Variables | Intervals | PLS Components | RMSE |
---|---|---|---|---|---|---|
S-G-SNV | 2 | 215 | 10.00% | 2, 14 | 6 | 0.2249 |
3 | 323 | 15.02% | 2, 11, 13 | 6 | 0.2131 | |
4 | 430 | 19.99% | 2, 9, 12, 13 | 6 | 0.2142 | |
S-G-MSC | 2 | 215 | 10.00% | 2, 14 | 6 | 0.2224 |
3 | 323 | 15.02% | 2, 11, 13 | 6 | 0.2120 | |
4 | 430 | 19.99% | 2, 9, 12, 13 | 6 | 0.2123 |
Spectral Preprocessing | Feature Variable Selection Methods | Calibration Models | Validation Models | ||||
---|---|---|---|---|---|---|---|
nLVs | R2 | RMSE | nLVs | R2 | RMSE | ||
S-G-SNV | - | 6 | 0.8082 | 0.1539 | 1 | 0.3603 | 0.2022 |
CARS | 15 | 0.9520 | 0.0770 | 14 | 0.9120 | 0.0750 | |
SPA | 22 | 0.7840 | 0.1633 | 22 | 0.8214 | 0.1068 | |
UVE | 15 | 0.7964 | 0.1586 | 15 | 0.8092 | 0.1104 | |
SiPLS | 20 | 0.9663 | 0.0645 | 20 | 0.9408 | 0.0615 | |
UVE-CARS | 15 | 0.8344 | 0.1430 | 9 | 0.8503 | 0.0978 | |
UVE-SPA | 7 | 0.7954 | 0.1590 | 7 | 0.6321 | 0.1533 | |
SiPLS-UVE | 20 | 0.9221 | 0.0981 | 20 | 0.9270 | 0.0683 | |
CARS-SPA | 15 | 0.9520 | 0.0770 | 15 | 0.8538 | 0.0967 | |
SiPLS-CARS | 18 | 0.9391 | 0.0867 | 12 | 0.8401 | 0.1011 | |
SiPLS-SPA | 23 | 0.8084 | 0.1538 | 30 | 0.8267 | 0.1052 | |
S-G-MSC | - | 6 | 0.8072 | 0.1543 | 1 | 0.3595 | 0.2023 |
CARS | 15 | 0.9524 | 0.0767 | 7 | 0.7914 | 0.1155 | |
SPA | 22 | 0.7896 | 0.1612 | 22 | 0.8588 | 0.0950 | |
UVE | 14 | 0.8173 | 0.1502 | 14 | 0.7580 | 0.1243 | |
SiPLS | 20 | 0.9659 | 0.0649 | 20 | 0.9442 | 0.0597 | |
UVE-CARS | 14 | 0.8735 | 0.1250 | 16 | 0.8990 | 0.0803 | |
UVE-SPA | 7 | 0.7879 | 0.1619 | 7 | 0.5838 | 0.1631 | |
SiPLS-UVE | 20 | 0.9081 | 0.1065 | 20 | 0.9117 | 0.0751 | |
CARS-SPA | 15 | 0.9524 | 0.0767 | 15 | 0.8281 | 0.1048 | |
SiPLS-CARS | 17 | 0.9250 | 0.0963 | 10 | 0.8509 | 0.0976 | |
SiPLS-SPA | 23 | 0.8414 | 0.1400 | 30 | 0.8848 | 0.0858 |
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Li, C.; Zhao, J.; Li, Y.; Meng, Y.; Zhang, Z. Modeling and Prediction of Soil Organic Matter Content Based on Visible-Near-Infrared Spectroscopy. Forests 2021, 12, 1809. https://doi.org/10.3390/f12121809
Li C, Zhao J, Li Y, Meng Y, Zhang Z. Modeling and Prediction of Soil Organic Matter Content Based on Visible-Near-Infrared Spectroscopy. Forests. 2021; 12(12):1809. https://doi.org/10.3390/f12121809
Chicago/Turabian StyleLi, Chunxu, Jinghan Zhao, Yaoxiang Li, Yongbin Meng, and Zheyu Zhang. 2021. "Modeling and Prediction of Soil Organic Matter Content Based on Visible-Near-Infrared Spectroscopy" Forests 12, no. 12: 1809. https://doi.org/10.3390/f12121809