Estimation Model of Rice Aboveground Dry Biomass Based on the Machine Learning and Hyperspectral Characteristic Parameters of the Canopy
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Site
2.2. Experimental Design
2.3. Measurement Methods
2.3.1. Canopy Spectral Reflectance Measurement
2.3.2. Plant Sampling and Measurements
2.4. Data Analysis
2.4.1. Data Preprocessing of Hyperspectral Data
2.4.2. Extraction of HCPs
2.4.3. Sample Division
2.5. The Construction Methods of an ADB Model
2.5.1. Variable Screening
2.5.2. Regression Methods
- (1)
- Traditional linear methods
- (2)
- Machine learning methods
2.6. The Evaluation of the Hyperspectral Model
3. Results and Analysis
3.1. Variations in Rice ADB
3.2. The Relationship between HCPs and ADB
3.3. Screening of HCPs
3.3.1. Variable Screening Based on the RC
3.3.2. Variable Screening Based on vip
3.3.3. Variable Screening Based on SR
3.3.4. Variable Screening Based on RF
3.4. Construction and Application of the ADB Model Based on Parameters
3.4.1. The Performance Evaluation Results of the ADB Model on the Training Set
3.4.2. The Performance Evaluation Results of the ADB Model on the Test Set
3.4.3. The Determination of the Appropriate Model Based on HCPs
4. Discussion
4.1. Relationship between Hyperspectral Characteristic Parameters and ADB
4.2. HCP Screening for ADB Estimation
4.3. Evaluation of the ADB Estimation Model
5. Conclusions
- (1)
- At each growth stage, the hyperspectral characteristic parameters that were significantly related to ADB contained elements in the red edge region, including SDr, Rrb, and Nrb.
- (2)
- The Rrb and Nrb appeared frequently in the variable screening results, indicating that they played an important role in the estimation of rice ADB.
- (3)
- The RF modeling method based on vip screening variables was found to be the best modeling method for estimating ADB in rice. The independent variables of the RF-vip model involved Nrb at each growth stage.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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HCP Identifier | HCP Symbol | Name | Definition |
---|---|---|---|
1 | Db | Blue edge amplitude | Maximum value of the first-derivative spectral reflectance in blue edge (490~530 nm) |
2 | λb | Blue edge position | Corresponding wavelength of the maximum value of the first-derivative spectral reflectance in the blue edge (490~530 nm) |
3 | SDb | Blue edge area | Sum of first-derivative spectral reflectance in blue edge (490~530 nm) |
4 | Dy | Yellow edge amplitude | Maximum value of first-derivative spectral reflectance in the yellow edge (560~640 nm) |
5 | λy | Yellow edge position | Corresponding wavelength of the maximum value of the first-derivative spectral reflectance in the yellow edge (560~640 nm) |
6 | SDy | Yellow edge area | Sum of first differential spectra in the yellow edge (560~640 nm) |
7 | Dr | Red edge amplitude | Maximum value of the first-derivative spectral reflectance in the red edge (680~760 nm) |
8 | λr | Red edge position | Corresponding wavelength of the maximum value of the first-derivative spectral reflectance in the red edge (680~760 nm) |
9 | SDr | Red edge area | Sum of first-derivative spectral reflectance in red edge (680~760 nm) |
10 | ρg | Green peak reflectance | Maximum raw spectral reflectance within the green peak (510~560 nm) |
11 | λg | Green peak position | Wavelength corresponding to the maximum raw spectral reflectance in the green peak (510~560 nm) |
12 | SDg | Green peak area | Sum of the raw spectral reflectance in the green peak (510~560 nm) |
13 | ρr | Red valley reflectance | Minimum raw spectral reflectance in red valley (650~690 nm) |
14 | λo | Red valley position | Wavelength corresponding to minimum raw spectral reflectance in red valley (650~690 nm) |
15 | Rrb | SDr/SDb | Ratio of red edge area to blue edge area |
16 | Rry | SDr/SDy | Ratio of red edge area to yellow edge area |
17 | Nrb | (SDr − SDb)/(SDr + SDb) | Normalized value of red edge area and blue edge area |
18 | Nry | (SDr − Sdy)/(SDr + Sdy) | Normalized value of red edge area and yellow edge area |
19 | Rgr | ρg/ρr | Ratio of green peak reflectance to red valley reflectance |
20 | Ngr | (ρg − ρr)/(ρg + ρr) | Normalized value of green peak reflectance and red valley reflectance |
Stage | Data Set | n | Min | Max | Mean | SD | CV |
---|---|---|---|---|---|---|---|
JS | All | 90 | 1.63 | 5.85 | 3.20 | 1.00 | 31.25 |
Train | 60 | 1.63 | 5.85 | 3.21 | 1.01 | 31.48 | |
Test | 30 | 1.72 | 5.43 | 3.20 | 1.00 | 31.33 | |
BS | All | 90 | 4.41 | 9.00 | 6.66 | 0.95 | 14.28 |
Train | 60 | 4.41 | 9.00 | 6.67 | 0.95 | 14.30 | |
Test | 30 | 4.68 | 8.92 | 6.66 | 0.97 | 14.49 | |
HS | All | 90 | 6.56 | 12.41 | 9.06 | 1.35 | 14.85 |
Train | 60 | 6.56 | 12.41 | 9.06 | 1.34 | 14.78 | |
Test | 30 | 6.69 | 12.31 | 9.07 | 1.38 | 15.26 | |
MS | All | 90 | 9.91 | 24.07 | 15.77 | 2.56 | 16.23 |
Train | 60 | 9.91 | 24.07 | 15.77 | 2.61 | 16.57 | |
Test | 30 | 11.28 | 21.47 | 15.78 | 2.50 | 15.82 | |
AS | All | 360 | 1.63 | 24.07 | 8.68 | 4.87 | 56.16 |
Train | 240 | 1.63 | 24.07 | 8.68 | 4.88 | 56.24 | |
Test | 120 | 1.72 | 21.47 | 8.67 | 4.88 | 56.22 |
Growth Stage | Number of Variables | Variable Names |
---|---|---|
JS | 20 | SDr Dr Rrb ρr Dy ρg Nrb Rry SDy SDg Db Nry λg SDb Rgr λo λb λy λr Ngr |
BS | 11 | ρg Db SDg ρr Nry Nrb SDr Dr Dy Rrb λo |
HS | 17 | SDy ρg Dy SDg SDr Dr Db Ngr Nry Rry Rgr Nrb Rrb ρr λg λo λr |
MS | 15 | Rrb Nrb Nry SDr λo Dr λb λy λr Db λg SDb ρr Rgr Rry |
AS | 20 | SDg ρg Db SDb SDr Dr SDy Ngr ρr Rrb Rgr Dy λg Nrb Nry λo λb λy λr Rry |
Growth Stage | Number of Variables | Variable Names |
---|---|---|
JS | 8 | Rrb Nrb λg Dr SDr ρr Dy λo |
BS | 4 | λo λg Nrb Nry |
HS | 9 | Rry ρr Nrb Rgr Dy Ngr λo Rrb SDy |
MS | 7 | Rrb Nrb SDr Nry SDy Ngr Rgr |
AS | 9 | λg Rrb Nrb ρr SDg λb λy λr ρg |
Growth Stage | Number of Variables | Variable Names |
---|---|---|
JS | 5 | Dy SDy ρr Rrb Rry |
BS | 1 | λo |
HS | 1 | ρr |
MS | 2 | SDb Rrb |
AS | 10 | Db SDb Dr SDr ρg λg SDg Rrb Rgr (ρg − ρr)/(ρg + ρr) |
Growth Stage | Number of Variables | Variable Names |
---|---|---|
JS | 17 | Rrb Nrb ρr Dr λg ρg SDr SDb Ngr SDg Rgr Dy Db Rry λo Nry λr |
BS | 19 | Rry Nrb Rrb Dy Nry SDy Dr SDb Db ρr Ngr SDr ρg λo Rgr SDg λg λr λb |
HS | 16 | ρr Rrb Nrb Ngr Nry Rgr SDy Db Rry SDb ρg Dy SDr Dr SDg λo |
MS | 18 | Rrb Nrb SDr SDy Nry ρr λg Rgr Dr Ngr ρg Dy λo SDg Rry SDb Db λr |
AS | 15 | Nrb Rrb λg ρr Rgr Ngr λo Dy SDy Nry ρg SDg λy λb SDr |
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Wang, X.; Xu, G.; Feng, Y.; Peng, J.; Gao, Y.; Li, J.; Han, Z.; Luo, Q.; Ren, H.; You, X.; et al. Estimation Model of Rice Aboveground Dry Biomass Based on the Machine Learning and Hyperspectral Characteristic Parameters of the Canopy. Agronomy 2023, 13, 1940. https://doi.org/10.3390/agronomy13071940
Wang X, Xu G, Feng Y, Peng J, Gao Y, Li J, Han Z, Luo Q, Ren H, You X, et al. Estimation Model of Rice Aboveground Dry Biomass Based on the Machine Learning and Hyperspectral Characteristic Parameters of the Canopy. Agronomy. 2023; 13(7):1940. https://doi.org/10.3390/agronomy13071940
Chicago/Turabian StyleWang, Xiaoke, Guiling Xu, Yuehua Feng, Jinfeng Peng, Yuqi Gao, Jie Li, Zhili Han, Qiangxin Luo, Hongjun Ren, Xiaoxuan You, and et al. 2023. "Estimation Model of Rice Aboveground Dry Biomass Based on the Machine Learning and Hyperspectral Characteristic Parameters of the Canopy" Agronomy 13, no. 7: 1940. https://doi.org/10.3390/agronomy13071940
APA StyleWang, X., Xu, G., Feng, Y., Peng, J., Gao, Y., Li, J., Han, Z., Luo, Q., Ren, H., You, X., & Lu, W. (2023). Estimation Model of Rice Aboveground Dry Biomass Based on the Machine Learning and Hyperspectral Characteristic Parameters of the Canopy. Agronomy, 13(7), 1940. https://doi.org/10.3390/agronomy13071940