Hyperspectral Modeling of Soil Organic Matter Based on Characteristic Wavelength in East China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Soil Sampling and Analysis
2.3. Soil Spectrum Collection and Preprocessing
2.4. Characteristic Wavelength Screening Algorithms
2.5. SOM Spectral Modeling
2.6. Model Evaluation
3. Results
3.1. Characteristic of Soil Spectral Curves
3.2. Results of Characteristic Wavelength Screening
3.3. PLSR Modeling Based on Characteristic Wavelengths
3.4. SVR and RF Modeling Based on Characteristic Wavelengths
4. Discussion
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Soil Type | Data Sets | n | Range (g/kg) | Mean (g/kg) | SD (a) | Skewness | Kurtosis | CV (b) (%) |
---|---|---|---|---|---|---|---|---|
Paddy soil | All samples | 111 | 15.43~58.22 | 32.13 | 7.21 | 0.50 | 0.92 | 22.44 |
Calibration sets | 74 | 15.43~52.49 | 31.93 | 7.03 | 0.23 | 0.16 | 22.01 | |
Validation sets | 37 | 18.43~58.22 | 32.52 | 7.64 | 0.95 | 2.20 | 23.50 | |
Shajiang black soil | All samples | 108 | 6.65~31.30 | 21.60 | 3.94 | −0.14 | 1.10 | 18.24 |
Calibration sets | 72 | 6.65~30.25 | 21.47 | 4.01 | −0.39 | 1.57 | 18.69 | |
Validation sets | 36 | 15.62~31.30 | 21.84 | 3.82 | 0.46 | −0.08 | 17.50 |
Soil Type | Model (a) | Number of Wavelengths | Calibration Sets | Validation Sets | RPD | LCCC | ||
---|---|---|---|---|---|---|---|---|
RMSEc (g/kg) | RMSEp (g/kg) | |||||||
Paddy soil | R-F-PLSR | Full spectra | 0.83 | 2.86 | 0.76 | 3.66 | 2.06 | 0.88 |
R-UVE-PLSR | 815 | 0.92 | 1.99 | 0.81 | 3.29 | 2.29 | 0.91 | |
R-CARS-PLSR | 61 | 0.91 | 2.09 | 0.87 | 2.68 | 2.81 | 0.93 | |
LR-F-PLSR | Full spectra | 0.95 | 1.62 | 0.80 | 3.37 | 2.24 | 0.90 | |
LR-UVE-PLSR | 884 | 0.90 | 2.16 | 0.86 | 2.77 | 2.72 | 0.92 | |
LR-CARS-PLSR | 125 | 0.95 | 1.58 | 0.90 | 2.43 | 3.01 | 0.95 | |
CR-F-PLSR | Full spectra | 0.70 | 3.81 | 0.62 | 4.66 | 1.62 | 0.79 | |
CR-UVE-PLSR | 268 | 0.88 | 2.38 | 0.87 | 2.77 | 2.72 | 0.92 | |
CR-CARS-PLSR | 70 | 0.97 | 1.21 | 0.95 | 1.72 | 4.31 | 0.97 | |
FDR-F-PLSR | Full spectra | 0.92 | 1.96 | 0.78 | 3.51 | 2.15 | 0.87 | |
FDR-UVE-PLSR | 300 | 0.88 | 2.38 | 0.83 | 3.09 | 2.44 | 0.91 | |
FDR-CARS-PLSR | 70 | 0.91 | 2.03 | 0.94 | 1.81 | 4.18 | 0.97 | |
Shajiang black soil | R-F-PLSR | Full spectra | 0.85 | 1.53 | 0.58 | 2.44 | 1.55 | 0.72 |
R-UVE-PLSR | 366 | 0.82 | 1.67 | 0.69 | 2.10 | 1.80 | 0.80 | |
R-CARS-PLSR | 40 | 0.94 | 0.98 | 0.79 | 1.72 | 2.19 | 0.87 | |
LR-F-PLSR | Full spectra | 0.89 | 1.31 | 0.61 | 2.37 | 1.59 | 0.74 | |
LR-UVE-PLSR | 461 | 0.84 | 1.57 | 0.62 | 2.34 | 1.61 | 0.76 | |
LR-CARS-PLSR | 40 | 0.95 | 0.93 | 0.86 | 1.44 | 2.62 | 0.92 | |
CR-F-PLSR | Full spectra | 0.92 | 1.10 | 0.28 | 3.21 | 1.17 | 0.54 | |
CR-UVE-PLSR | 257 | 0.81 | 1.72 | 0.64 | 2.26 | 1.61 | 0.79 | |
CR-CARS-PLSR | 53 | 0.92 | 1.11 | 0.73 | 1.96 | 1.93 | 0.84 | |
FDR-F-PLSR | Full spectra | 0.96 | 0.78 | 0.26 | 3.23 | 1.45 | 0.55 | |
FDR-UVE-PLSR | 105 | 0.82 | 1.67 | 0.63 | 2.30 | 1.94 | 0.79 | |
FDR-CARS-PLSR | 61 | 0.98 | 0.55 | 0.85 | 1.46 | 2.58 | 0.92 |
Soil Type | Model (a) | Best Parameters | Calibration Sets | Validation Sets | RPD | LCCC | ||
---|---|---|---|---|---|---|---|---|
(C) | RMSEc (g/kg) | RMSEp (g/kg) | ||||||
Paddy soil | R-F-SVR | 16 | 0.99 | 0.66 | 0.75 | 4.03 | 1.87 | 0.85 |
R-UVE-SVR | 2 | 0.81 | 3.09 | 0.77 | 3.93 | 1.92 | 0.85 | |
R-CARS-SVR | 16 | 0.89 | 2.57 | 0.88 | 2.85 | 2.70 | 0.92 | |
LR-F-SVR | 4 | 0.95 | 1.60 | 0.83 | 3.20 | 2.36 | 0.90 | |
LR-UVE-SVR | 16 | 0.93 | 1.91 | 0.89 | 2.57 | 2.93 | 0.93 | |
LR-CARS-SVR | 16 | 0.94 | 1.72 | 0.92 | 2.33 | 3.24 | 0.95 | |
CR-F-SVR | 0.0625 | 0.99 | 0.68 | 0.79 | 3.56 | 2.12 | 0.86 | |
CR-UVE-SVR | 0.0625 | 0.90 | 2.20 | 0.85 | 3.08 | 2.45 | 0.91 | |
CR-CARS-SVR | 8 | 0.98 | 1.04 | 0.91 | 2.24 | 3.37 | 0.96 | |
FDR-F-SVR | 0.0625 | 0.99 | 0.65 | 0.76 | 3.96 | 1.90 | 0.86 | |
FDR-UVE-SVR | 0.0625 | 0.95 | 1.56 | 0.78 | 3.61 | 2.09 | 0.88 | |
FDR-CARS-SVR | 0.0625 | 0.93 | 1.94 | 0.91 | 2.37 | 3.18 | 0.94 | |
Shajiang black soil | R-F-SVR | 1 | 0.91 | 1.23 | 0.63 | 2.30 | 1.64 | 0.76 |
R-UVE-SVR | 8 | 0.87 | 1.46 | 0.66 | 2.20 | 1.71 | 0.79 | |
R-CARS-SVR | 16 | 0.94 | 1.00 | 0.77 | 1.79 | 2.10 | 0.86 | |
LR-F-SVR | 1 | 0.92 | 1.17 | 0.61 | 2.36 | 1.60 | 0.75 | |
LR-UVE-SVR | 2 | 0.79 | 1.81 | 0.70 | 2.09 | 1.80 | 0.81 | |
LR-CARS-SVR | 16 | 0.94 | 1.00 | 0.82 | 1.58 | 2.38 | 0.90 | |
CR-F-SVR | 0.0625 | 0.99 | 0.37 | 0.29 | 3.20 | 1.18 | 0.48 | |
CR-UVE-SVR | 0.0625 | 0.95 | 0.91 | 0.63 | 2.34 | 1.61 | 0.77 | |
CR-CARS-SVR | 1 | 0.97 | 0.71 | 0.69 | 2.16 | 1.74 | 0.83 | |
FDR-F-SVR | 0.0625 | 0.99 | 0.38 | 0.36 | 3.07 | 1.23 | 0.58 | |
FDR-UVE-SVR | 0.0625 | 0.91 | 1.18 | 0.67 | 2.23 | 1.69 | 0.77 | |
FDR-CARS-SVR | 0.0625 | 0.99 | 0.45 | 0.83 | 1.58 | 2.38 | 0.91 |
Soil Type | Model (a) | Best Parameters | Calibration Sets | Validation Sets | RPD | LCCC | ||
---|---|---|---|---|---|---|---|---|
(mtry, ntree) | RMSEc (g/kg) | RMSEp (g/kg) | ||||||
Paddy soil | R-F-RF | 9, 200 | 0.43 | 5.28 | 0.51 | 5.44 | 1.39 | 0.63 |
R-UVE-RF | 10, 500 | 0.52 | 5.19 | 0.35 | 5.30 | 1.10 | 0.63 | |
R-CARS-RF | 6, 100 | 0.43 | 5.65 | 0.25 | 5.78 | 1.17 | 0.45 | |
LR-F-RF | 8, 100 | 0.40 | 5.45 | 0.48 | 5.60 | 1.35 | 0.60 | |
LR-UVE-RF | 10, 1500 | 0.43 | 5.26 | 0.48 | 5.50 | 1.37 | 0.63 | |
LR-CARS-RF | 10, 100 | 0.47 | 5.07 | 0.50 | 5.56 | 1.36 | 0.58 | |
CR-F-RF | 6, 200 | 0.47 | 5.16 | 0.64 | 5.05 | 1.49 | 0.65 | |
CR-UVE-RF | 10, 100 | 0.59 | 4.88 | 0.34 | 5.48 | 1.02 | 0.69 | |
CR-CARS-RF | 8, 100 | 0.52 | 5.22 | 0.50 | 4.69 | 1.01 | 0.70 | |
FDR-F-RF | 7, 100 | 0.52 | 4.86 | 0.65 | 4.80 | 1.57 | 0.70 | |
FDR-UVE-RF | 10, 100 | 0.53 | 4.80 | 0.68 | 4.70 | 1.60 | 0.71 | |
FDR-CARS-RF | 6, 100 | 0.51 | 4.87 | 0.66 | 4.76 | 1.57 | 0.70 | |
Shajiang black soil | R-F-RF | 1, 1000 | 0.07 | 4.05 | 0.30 | 3.14 | 1.20 | 0.46 |
R-UVE-RF | 5, 100 | 0.08 | 4.06 | 0.26 | 3.26 | 1.19 | 0.48 | |
R-CARS-RF | 2, 100 | 0.10 | 3.96 | 0.22 | 0.52 | 1.12 | 0.36 | |
LR-F-RF | 1, 500 | 0.06 | 4.08 | 0.28 | 3.19 | 1.18 | 0.45 | |
LR-UVE-RF | 7, 200 | 0.04 | 4.17 | 0.31 | 3.13 | 1.20 | 0.48 | |
LR-CARS-RF | 1, 200 | 0.08 | 3.95 | 0.20 | 3.37 | 1.12 | 0.34 | |
CR-F-RF | 10, 100 | 0.12 | 3.75 | 0.14 | 3.51 | 1.07 | 0.20 | |
CR-UVE-RF | 8, 100 | 0.21 | 3.56 | 0.28 | 3.28 | 1.15 | 0.33 | |
CR-CARS-RF | 8, 200 | 0.20 | 3.64 | 0.21 | 3.41 | 1.09 | 0.26 | |
FDR-F-RF | 10, 100 | 0.30 | 3.39 | 0.51 | 2.84 | 1.33 | 0.53 | |
FDR-UVE-RF | 4, 100 | 0.52 | 2.84 | 0.46 | 2.79 | 1.40 | 0.61 | |
FDR-CARS-RF | 10, 100 | 0.56 | 2.93 | 0.60 | 2.73 | 1.38 | 0.56 |
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Zhao, M.; Gao, Y.; Lu, Y.; Wang, S. Hyperspectral Modeling of Soil Organic Matter Based on Characteristic Wavelength in East China. Sustainability 2022, 14, 8455. https://doi.org/10.3390/su14148455
Zhao M, Gao Y, Lu Y, Wang S. Hyperspectral Modeling of Soil Organic Matter Based on Characteristic Wavelength in East China. Sustainability. 2022; 14(14):8455. https://doi.org/10.3390/su14148455
Chicago/Turabian StyleZhao, Mingsong, Yingfeng Gao, Yuanyuan Lu, and Shihang Wang. 2022. "Hyperspectral Modeling of Soil Organic Matter Based on Characteristic Wavelength in East China" Sustainability 14, no. 14: 8455. https://doi.org/10.3390/su14148455
APA StyleZhao, M., Gao, Y., Lu, Y., & Wang, S. (2022). Hyperspectral Modeling of Soil Organic Matter Based on Characteristic Wavelength in East China. Sustainability, 14(14), 8455. https://doi.org/10.3390/su14148455