# Estimation of Leaf Area Index and Above-Ground Biomass of Winter Wheat Based on Optimal Spectral Index

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## Abstract

**:**

^{2}of the LAI estimation model validation set was 0.830, the RMSE was 0.276, and the MRE was 6.920; the R

^{2}of the above-ground biomass estimation model validation set was 0.682, RMSE was 235.016, MRE was 4.336, and the accuracies of both models were high. The present research results can provide a theoretical basis for crop monitoring based on spectral technology and provide an application reference for the rapid estimation of crop growth parameters.

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Research Area and Test Design

^{2}(N1), 160 kg N/hm

^{2}(N2), 220 kg N/hm

^{2}(N3) and 280 kg N/hm

^{2}(N4), and four fertilization types of Urea (U), slow-release fertilizer (SRF), UNS1 (U/SRF = 3/7) and UNS2 (U/SRF = 2/8) were setup. No nitrogen fertilizer was used as the control treatment (CK). The experiment was arranged in a completely randomized design with two replicates. The N fertilizer, phosphate fertilizer (120 kg P

_{2}O

_{5}ha

^{–1}) and potassium fertilizer (100 kg K

_{2}O ha

^{–1}) were applied as basal fertilizers and were incorporated into the 0–15 cm soil layer before planting. Winter wheat (Xiaoyan 22) was planted at 180 kg/hm

^{2}.Wheat was seeded on October 15 in 2018 and October 15 in 2019, and harvested on June 25 in 2019 and 2020.

#### 2.2. Data Collection

#### 2.2.1. Measurement of LAI

#### 2.2.2. Above-Ground Biomass

#### 2.2.3. Acquisition of Spectral Data

^{®}3 Standard-Res, Inc., Longmont, CO, USA). The spectral range was 350–1830 nm, covering the wavebands of 350–1000 nm with 3 nm resolution and the other wavebands with 10 nm resolution. The sensor had a 25° field of view, and the handheld probe was pointed vertically downward, with the instrument held 80 cm above the crop canopy. A representative plant canopy was selected for each test plot, and spectral values were collected 10 times. After removing nonstandard values, an average value was used as the final spectrum of the test plot. Spectral measurements were conducted on sunny and windless days between 11:00 a.m. and 2:00 p.m. to ensure comparability. A white spectral on reference reflectance panel (Labsphere, Inc., Longmont, CO, USA) reading was taken every 5 min or whenever required considering the changes in illumination conditions and used to convert digital number readings to reflectance. To reduce or eliminate the influence of useless information such as background noise, baseline drift, and stray light on the spectral reflectance curve, we used Savitzky–Golay convolution smoothing (9 points and 4 times) to preprocess the spectral data [15].

#### 2.3. Techniques for Data Analysis

_{i}and R

_{j}represent the original wavelength positions of i and j; R

_{i}’ and R

_{j}’ represent the first-order differential spectrum at the i and j wavelength positions. All spectral index calculation results were obtained using MATLAB R2022a software(MathWorks, Inc. Natick, MA, USA) and all figures in the present article were obtained using Origin Pro 2021 software (OriginLab Corp., Northampton, MA, USA).

^{−5}. After the neural network was trained, the test data were entered into the training network simulation to obtain the simulated values. Average values of the data fitting results over the three periods were regarded as the model fitting results [20]. The 14 optimal spectral indices were divided into 3 groups as model input variables, the first group of variables was composed of 5 optimal spectral indices calculated from the original reflection spectrum, the second group of variables was composed of the 5 optimal spectral indices calculated from the first-order differential reflection spectrum and the third group of variables consisted of the top 5 spectral indices for all R

_{max}values in the table. A standard two-layer feed-forward network with sigmoid transfer functions was chosen for the BPNN method [21], and SVM with the kernel function of Gaussian function [22] was tested in the present study.

#### 2.4. Verify the Prediction Accuracy of the Models

^{2}), root mean square error (RMSE) and mean relative error (MRE), to evaluate the model accuracy [23]. The R

^{2}, RMSE and MRE were calculated as follows:

## 3. Results

#### 3.1. Extraction of Optimal Spectral Index Wavelength Combinations for LAI and Above-Ground Biomass

_{max}value from high to low were:

_{max}value from high to low were:

#### 3.2. Establishment of LAI and Above-Ground Biomass Inversion Model Based on Optimal Spectral Index

^{2}of the LAI and above-ground biomass estimation models’ modeling set and validation set were both higher than 0.6, indicating that the models had a good degree of linear fit accuracy and could be used to estimate the LAI and above-ground biomass of winter wheat. At the same time, a further observation can be made that under the same model, the R

^{2}of the modeling set and the validation set of Combination 2 in the LAI and above-ground biomass estimation models were higher than those of Combination 1 and Combination 3, and the RMSE and MRE were both lower. Under the same combination, the R

^{2}values of the RF model’s modeling set and validation set were higher than those of the SVM model and the BPNN model, and the RMSE and MRE were both lower. An observation can be made that Combination 2 was the optimal model input variable in the three modeling methods, indicating that the first-order differential spectral index contained more spectrally effective information related to LAI and above-ground biomass, and the predictive ability was higher for LAI and above-ground biomass. For the same input variable and different modeling methods, by comparing the model evaluation indicators, the order of accuracy of the models established by the three methods was: RF > BPNN > SVM. As such, the RF model was the best modeling method, which could extract the effective information of LAI and above-ground biomass to a greater extent. To summarize, the models established by the combination of the optimal input variables and the optimal modeling method for the LAI and above-ground biomass estimation models of winter wheat were the combination of input variable 2 and the RF model. The optimal winter wheat LAI estimation model was based on the combination of RF model and Combination 2. The R

^{2}of the optimal model modeling set was 0.794, that of the RMSE was 0.285, and that of the MRE was 7.701. Meanwhile, the R

^{2}of the validation set was 0.830, that of the RMSE was 0.276, and that of the MRE was 6.920. The optimal model for estimating the above-ground biomass of winter wheat was a model based on the combination of RF model and Combination 2. The R

^{2}of the optimal model modeling set was 0.721, that of the RMSE was 235.769, and that of the MRE was 4.312. At the same time, the R

^{2}of the validation set was 0.682, that of the RMSE was 235.016, and that of the MRE was 4.336. An observation can be made from the evaluation indicators in Table 5 that in the same spectral index combination and modeling method, the LAI estimation model based on winter wheat generally had higher accuracy than the above-ground biomass estimation model.

## 4. Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

- Chen, J.M.; Cihlar, J. Retrieving leaf area index of boreal conifer forests using Landsat TM images. Remote Sens. Environ.
**1996**, 55, 153–162. [Google Scholar] [CrossRef] - Sellers, P.J. Modeling the exchanges of energy, water, and carbon between continents and the atmosphere. Science
**1997**, 275, 502–509. [Google Scholar] [CrossRef] [Green Version] - Wu, F.; Li, Y.X.; Zhang, Y.Y.; Zhang, X.H.; Zou, X.C. Hyperspectral estimation of biomass of winter wheat at different growth stages based on machine learning algorithms. J. Triticeae Crops
**2019**, 39, 217–224, (In Chinese with English abstract). [Google Scholar] [CrossRef] - Zhang, L.X.; Chen, Y.Q.; Li, Y.X.; Ma, J.C.; Du, K.M.; Zheng, F.X.; Sun, Z.F. Estimating above ground biomass of winter wheat at early growth stages based on visual spectral. Spectrosc. Spect. Anal.
**2019**, 39, 2501–2506, (In Chinese with English abstract). [Google Scholar] [CrossRef] - Li, L.T.; Li, J.; Ming, J.; Wang, S.Q.; Ren, T.; Lu, J.W. Selection optimization of hyperspectral bandwidth and effective wavelength for predicting leaf area index in winter oilseed rape. Trans. Chin. Soc. Agric. Mach.
**2018**, 49, 156–165, (In Chinese with English abstract). [Google Scholar] [CrossRef] - Xie, Q.Y.; Huang, W.J.; Liang, D.; Peng, D.L.; Huang, L.S.; Song, X.Y.; Zhang, D.Y.; Yang, G.J. Research on universality of least squares support vector machine method for estimation leaf area index of winter wheat. Spectrosc. Spect. Anal.
**2014**, 34, 489–493, (In Chinese with English abstract). [Google Scholar] [CrossRef] - Zhang, J.J.; Cheng, T.; Guo, W.; Xu, X.; Ma, X.M.; Xie, Y.M.; Qiao, H.B. Leaf area index estimation model for UAV image hyperspectral data based on wavelength variable selection and machine learning methods. Plant Methods
**2021**, 17, 49. [Google Scholar] [CrossRef] - Oliveira, R.A.; Junior, J.M.; Costa, C.S.; Näsi, R.; Koivumäki, N.; Niemeläinen, O.; Kaivosoja, J.; Nyholm, L.; Pistori, H.; Honkavaara, E. Silage grass sward nitrogen concentration and dry matter yield estimation using deep regression and RGB images captured by UAV. Agronomy
**2022**, 12, 1352. [Google Scholar] [CrossRef] - Apolo-Apolo, O.E.; Pérez-Ruiz, M.; Martínez-Guanter, J.; Egea, G. A mixed data-based deep neural network to estimate leaf area index in wheat breeding trials. Agronomy
**2020**, 10, 175. [Google Scholar] [CrossRef] [Green Version] - Hama, A.; Tanaka, K.; Mochizuki, A.; Tsuruoka, Y.; Kondoh, A. Estimating the protein concentration in rice grain using UAV imagery together with agroclimatic data. Agronomy
**2020**, 10, 431. [Google Scholar] [CrossRef] [Green Version] - Tan, K.Z.; Wang, S.W.; Song, Y.Z.; Liu, Y.; Gong, Z.P. Estimating nitrogen status of rice canopy using hyperspectral reflectance combined with BPSO–SVR in cold region. Chemometr. Intell. Lab.
**2017**, 172, 68–79. [Google Scholar] [CrossRef] - Liu, S.; Yu, H.Y.; Zhang, J.H.; Zhou, H.G.; Kong, L.J.; Zhang, L.; Dang, J.M.; Sui, Y.Y. Study on Inversion Model of Chlorophyll Content in Soybean Leaf Based on Optimal Spectral Indices. Spectrosc. Spect. Anal.
**2021**, 41, 1912–1919, (In Chinese with English abstract). [Google Scholar] [CrossRef] - Bekele, F.; Korecha, D.; Negatu, L. Influence of rainfall features on barley yield in Sinana district of Ethiopia. J. Agrometeorol.
**2017**, 19, 125–128. [Google Scholar] [CrossRef] - Kuusk, A. Specular reflection in the signal of LAI–2000 plant canopy analyzer. Agr. Forest. Meteorol.
**2016**, 221, 242–247. [Google Scholar] [CrossRef] - Lu, J.S.; Chen, S.M.; Huang, W.M.; Hu, T.T. Estimating of aboveground biomass and leaf area index of summer maize using SEPLS_ELM model. Trans. Chin. Soc. Agric. Eng.
**2021**, 37, 128–135, (In Chinese with English abstract). [Google Scholar] [CrossRef] - Cortes, C.; Vapnik, V. Support–vector networks. Mach. Learn.
**1995**, 20, 273–297. [Google Scholar] [CrossRef] - Breiman, L. Random forest. Mach. Learn.
**2001**, 45, 5–32. [Google Scholar] [CrossRef] [Green Version] - Hecht–Nielsen, R. Theory of the backpropagation neural network. Neural Netw.
**1988**, 1, 445. [Google Scholar] [CrossRef] - Chen, Y.; Wang, L.; Bai, Y.L.; Lu, Y.L.; Ni, L.; Wang, Y.H.; Xu, M.Z. Quantitative relationship between effective accumulated temperature and summer maize plant height and leaf area index under different nitrogen, phosphorus and potassium treatments. Chin. Agric. Sci.
**2021**, 54, 4761–4777, (In Chinese with English abstract). [Google Scholar] [CrossRef] - Li, Y.Y.; Chang, Q.R.; Liu, X.Y.; Yan, L.; Luo, D.; Wang, S. Remote sensing estimation of SPAD value of maize leaves based on hyperspectral and BP neural network. Trans. Chin. Soc. Agric. Eng.
**2016**, 32, 135–142, (In Chinese with English abstract). [Google Scholar] [CrossRef] - Kira, O.; Nguy–Robertson, A.L.; Arkebauer, T.J.; Linker, R.; Gitelson, A.A. Informative spectral bands for remote green LAI estimation in C3 and C4 crops. Agr. Forest. Meteorol.
**2016**, 218–219, 243–249. [Google Scholar] [CrossRef] [Green Version] - Ancona, N.; Maglietta, R.; Stella, E. Data representations and generalization error in kernel based learning machines. Pattern Recognit.
**2006**, 39, 1588–1603. [Google Scholar] [CrossRef] - Chen, J.Y.; Wang, X.T.; Zhang, Z.T.; Han, J.; Yao, Z.H.; Wei, G.F. Soil salinization monitoring method based on uav-satellite remote sensing scale-up. Trans. Chin. Soc. Agric. Mach.
**2019**, 50, 161–169, (In Chinese with English abstract). [Google Scholar] [CrossRef] - Hong, Y.S.; Liu, Y.L.; Chen, Y.Y.; Liu, Y.F.; Yu, L.; Liu, Y.; Cheng, H. Application of fractional–order derivative in the quantitative estimation of soil organic matter content through visible and near–infrared spectroscopy. Geoderma
**2018**, 337, 758–769. [Google Scholar] [CrossRef] - Zhang, Y.K.; Luo, B.; Pan, D.Y.; Song, P.; Lu, W.C.; Wang, C.; Zhao, C.J. Estimation of Canopy Nitrogen Content of Soybean Crops Based on Fractional Differential Algorithm. Spectrosc. Spect. Anal.
**2018**, 38, 3221–3230, (In Chinese with English abstract). [Google Scholar] [CrossRef] - Zhang, Y.M.; Ta, N.; Guo, S.; Chen, Q.; Zhao, L.C.; Li, F.L.; Chang, Q.R. Combining spectral and textural information from UAV RGB images for leaf area index monitoring in kiwifruit orchard. Remote Sens.
**2022**, 14, 1063. [Google Scholar] [CrossRef] - Wang, F.L.; Yang, M.; Ma, L.F.; Zhang, T.; Qin, W.L.; Li, W.; Zhang, Y.H.; Sun, Z.C.; Wang, Z.M.; Li, F.; et al. Estimation of above-ground biomass of winter wheat based on consumer-grade multi-spectral UAV. Remote Sens.
**2022**, 14, 1251. [Google Scholar] [CrossRef] - Xia, T.; Wu, W.B.; Zhou, Q.B.; Zhou, Y. Comparison of two inversion methods for winter wheat leaf area index based on hyperspectral remote sensing. Trans. Chin. Soc. Agric. Eng.
**2013**, 29, 139–147, (In Chinese with English abstract). [Google Scholar] [CrossRef] - Azarmdel, H.; Jahanbakhshi, A.; Mohtasebi, S.S.; Muñozc, A.R. Evaluation of image processing technique as an expert system in mulberry fruit grading based on ripeness level using artificial neural networks (ANNs) and support vector machine (SVM). Postharvest Biol. Technol.
**2020**, 166, 111201. [Google Scholar] [CrossRef] - Zhang, Y.; Zhou, M.R. Application of Artificial Neural Network BP Algorithm in Near Infrared Spectroscopy. Infrared
**2006**, 27, 1–4, (In Chinese with English abstract). [Google Scholar] [CrossRef] - Fu, Z.P.; Jiang, J.; Gao, Y.; Krienke, B.; Wang, M.; Zhong, K.T.; Cao, Q.; Tian, Y.C.; Zhu, Y.; Cao, W.X.; et al. Wheat Growth Monitoring and Yield Estimation based on Multi-Rotor Unmanned Aerial Vehicle. Remote Sens.
**2020**, 12, 508. [Google Scholar] [CrossRef] [Green Version]

**Figure 1.**Aerial photograph of winter wheat research area and some sampling plots in Yangling, Shaanxi.

**Figure 3.**Correlation matrix diagram of spectral indices and leaf area index(LAI). (

**a**) DI and LAI; (

**b**) FDDI and LAI; (

**c**) RI and LAI; (

**d**) FDRI and LAI; (

**e**) NDVI and LAI; (

**f**) FDNDVI and LAI; (

**g**) SAVI and LAI; (

**h**) FDSAVI and LAI; (

**i**) TVI and LAI; (

**j**) FDTVI and LAI; (

**k**) mSR and LAI; (

**l**) FDmSR and LAI; (

**m**) mNDI and LAI; (

**n**) FDmNDI and LAI.

**Figure 4.**Correlation matrix diagram of spectral indices and above-ground biomass. (

**a**) DI and above-ground biomass; (

**b**) FDDI and above-ground biomass; (

**c**) RI and above-ground biomass; (

**d**) FDRI and above-ground biomass; (

**e**) NDVI and above-ground biomass; (

**f**) FDNDVI and above-ground biomass; (

**g**) SAVI and above-ground biomass; (

**h**) FDSAVI and above-ground biomass; (

**i**) TVI and above-ground biomass; (

**j**) FDTVI and above-ground biomass; (

**k**) mSR and above-ground biomass; (

**l**) FDmSR and above-ground biomass; (

**m**) mNDI and above-ground biomass; (

**n**) FDmNDI and above-ground biomass.

**Figure 5.**Prediction results of modeling set and validation set of winter wheat leaf area index inversion model with different input variables and modeling methods. (

**a**) SVM Model input variable is combination 1; (

**b**) SVM Model input variable is combination 2; (

**c**) SVM Model input variable is combination 3; (

**d**) RF Model input variable is combination 1; (

**e**) RF Model input variable is combination 2; (

**f**) RF Model input variable is combination 3; (

**g**) BPNN Model input variable is combination 1; (

**h**) BPNN Model input variable is combination 2; (

**i**) BPNN Model input variable is combination 3.

**Figure 6.**Prediction results of modeling set and validation set of winter wheat above-ground biomass inversion model with different input variables and modeling methods (

**a**) SVM Model input variable is combination 1; (

**b**) SVM Model input variable is combination 2; (

**c**) SVM Model input variable is combination 3; (

**d**) RF Model input variable is combination 1; (

**e**) RF Model input variable is combination 2; (

**f**) RF Model input variable is combination 3; (

**g**) BPNN Model input variable is combination 1; (

**h**) BPNN Model input variable is combination 2; (

**i**) BPNN Model input variable is combination 3.

**Table 1.**Statistics of leaf area index and above-ground biomass in summer maize vegetation growth stage.

Indexes | Leaf Area Index/(cm^{2}·cm^{−2}) | Above-Ground Biomass/(kg·hm^{−2}) | ||
---|---|---|---|---|

Modeling Set | Validation Set | Modeling Set | Validation Set | |

Sample size | 44 | 22 | 44 | 22 |

Minimum values | 2.28 | 2.30 | 3247.20 | 3251.81 |

Maximum values | 4.23 | 4.23 | 5073.49 | 4795.8 |

Mean | 3.17 | 3.24 | 4159.26 | 4164.51 |

Standard deviation | 0.59 | 1.62 | 442.53 | 405.87 |

Coefficient of variation/% | 0.19 | 0.50 | 0.11 | 0.10 |

**Table 2.**Spectral index in this study. All indices were calculated from reflectance collected by spectroradiometer. i and j represent arbitrary wavelength positions, $R$ and $R$ represent the original wavelength positions of i and j. The initial spectral reflectance, $R$’ and $R$’ represent the first-order differential spectrum at the i and j wavelength positions Spectral reflectance, $R$

_{445}and $R$

_{550}represent the original spectrum at 445 and 550 nm wavelength positions spectral reflectance, $R$’

_{445}and $R$’

_{550}represent the first order at the 445 and 550 nm wavelength positions differential spectral reflectance.

Spectral Index | Formula | Reference | |
---|---|---|---|

Original | First-Order Differential | ||

difference index (DI) | ${R}_{i}-{R}_{j}$ | ${R}_{i}^{\prime}-{R}_{j}^{\prime}$ | [12] |

ratio index (RI) | $\frac{{R}_{i}}{{R}_{j}}$ | $\frac{{R}_{i}^{\prime}}{{R}_{j}^{\prime}}$ | [12] |

normalized difference vegetation index (NDVI) | $\frac{{R}_{i}-{R}_{j}}{{R}_{i}+{R}_{j}}$ | $\frac{{R}_{i}^{\prime}-{R}_{j}^{\prime}}{{R}_{i}^{\prime}+{R}_{j}^{\prime}}$ | [12] |

soil-adjusted vegetation index (SAVI) | $\left(1+0.16\right)\frac{{R}_{i}-{R}_{j}}{{R}_{i}+{R}_{j}+0.16}$ | $\left(1+0.16\right)\frac{{R}_{i}^{\prime}-{R}_{j}^{\prime}}{{R}_{i}^{\prime}+{R}_{j}^{\prime}+0.16}$ | [12] |

triangular vegetation index (TVI) | $0.5\times \left(120\times \left({R}_{i}-{R}_{550}\right)-200\times \left({R}_{j}-{R}_{550}\right)\right)$ | $0.5\times \left(120\times \left({R}_{i}^{\prime}-{R}_{550}^{\prime}\right)-200\times \left({R}_{j}^{\prime}-{R}_{550}^{\prime}\right)\right)$ | [12] |

modified simple ratio (mSR) | $\frac{{R}_{i}-{R}_{445}}{{R}_{j}-{R}_{445}}$ | $\frac{{R}_{i}^{\prime}-{R}_{445}^{\prime}}{{R}_{j}^{\prime}-{R}_{445}^{\prime}}$ | [12] |

modified normalized difference index (mNDI) | $\frac{{R}_{i}-{R}_{j}}{{R}_{i}+{R}_{j}-2{R}_{445}}$ | $\frac{{R}_{i}^{\prime}-{R}_{j}^{\prime}}{{R}_{i}^{\prime}+{R}_{j}^{\prime}-2{R}_{445}}$ | [12] |

**Table 3.**The maximum value and wavelength position of correlation coefficient between spectral index and leaf area index.

Spectral Index | Maximum Correlation Coefficient | Spectral Index | Maximum Correlation Coefficient | ||
---|---|---|---|---|---|

r_{max} | Wavelength Position (i,j)/nm | r_{max} | Wavelength Position (i,j)/nm | ||

DI | 0.659 | 759,758 | FDDI | 0.716 | 736,733 |

RI | 0.669 | 759,758 | FDRI | 0.613 | 742,740 |

NDVI | 0.670 | 758,753 | FDNDVI | 0.612 | 741,739 |

SAVI | 0.661 | 757,755 | FDSAVI | 0.715 | 740,732 |

TVI | 0.704 | 712,685 | FDTVI | 0.659 | 685,758 |

mSR | 0.607 | 758,754 | FDmSR | 0.602 | 738,748 |

mNDI | 0.607 | 759,756 | FDmNDI | 0.609 | 738,747 |

**Table 4.**The maximum value and wavelength position of correlation coefficient between spectral index and above-ground biomass.

Spectral Index | Maximum Correlation Coefficient | Spectral Index | Maximum Correlation Coefficient | ||
---|---|---|---|---|---|

r_{max} | Wavelength Position (i,j)/nm | r_{max} | Wavelength Position (i,j)/nm | ||

DI | 0.669 | 758,757 | FDDI | 0.698 | 743,721 |

RI | 0.637 | 755,754 | FDRI | 0.534 | 757,688 |

NDVI | 0.626 | 753,750 | FDNDVI | 0.517 | 743,738 |

SAVI | 0.634 | 757,753 | FDSAVI | 0.697 | 758,697 |

TVI | 0.693 | 739,720 | FDTVI | 0.588 | 685,758 |

mSR | 0.571 | 714,717 | FDmSR | 0.540 | 726,739 |

mNDI | 0.571 | 692,721 | FDmNDI | 0.558 | 680,695 |

Model | Combination | Modeling Set R^{2} | Validation Set R^{2} | Modeling Set RMSE | Validation Set RMSE | Modeling Set MRE | Validation Set MRE | |
---|---|---|---|---|---|---|---|---|

LAI | SVM | 1 | 0.466 | 0.478 | 0.515 | 0.554 | 11.645 | 15.073 |

2 | 0.633 | 0.694 | 0.386 | 0.369 | 9.106 | 8.474 | ||

3 | 0.589 | 0.517 | 0.391 | 0.456 | 9.277 | 11.250 | ||

RF | 1 | 0.725 | 0.565 | 0.316 | 0.412 | 7.919 | 12.185 | |

2 | 0.794 | 0.830 | 0.285 | 0.276 | 7.701 | 6.920 | ||

3 | 0.722 | 0.666 | 0.358 | 0.388 | 9.026 | 10.477 | ||

BPNN | 1 | 0.600 | 0.602 | 0.374 | 0.547 | 10.525 | 12.617 | |

2 | 0.634 | 0.707 | 0.365 | 0.328 | 8.903 | 8.801 | ||

3 | 0.597 | 0.644 | 0.521 | 0.450 | 13.978 | 11.624 | ||

Above-ground biomass | SVM | 1 | 0.562 | 0.384 | 301.367 | 337.496 | 4.851 | 7.031 |

2 | 0.567 | 0.526 | 300.911 | 300.725 | 4.838 | 5.907 | ||

3 | 0.554 | 0.472 | 344.029 | 329.219 | 5.922 | 7.058 | ||

RF | 1 | 0.692 | 0.601 | 246.184 | 268.773 | 4.436 | 5.142 | |

2 | 0.721 | 0.682 | 235.769 | 235.016 | 4.312 | 4.336 | ||

3 | 0.710 | 0.633 | 246.789 | 252.856 | 4.351 | 4.693 | ||

BPNN | 1 | 0.626 | 0.609 | 275.681 | 274.168 | 4.851 | 5.633 | |

2 | 0.637 | 0.617 | 267.066 | 259.932 | 4.838 | 4.837 | ||

3 | 0.608 | 0.607 | 302.115 | 268.281 | 5.558 | 5.068 |

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**MDPI and ACS Style**

Tang, Z.; Guo, J.; Xiang, Y.; Lu, X.; Wang, Q.; Wang, H.; Cheng, M.; Wang, H.; Wang, X.; An, J.;
et al. Estimation of Leaf Area Index and Above-Ground Biomass of Winter Wheat Based on Optimal Spectral Index. *Agronomy* **2022**, *12*, 1729.
https://doi.org/10.3390/agronomy12071729

**AMA Style**

Tang Z, Guo J, Xiang Y, Lu X, Wang Q, Wang H, Cheng M, Wang H, Wang X, An J,
et al. Estimation of Leaf Area Index and Above-Ground Biomass of Winter Wheat Based on Optimal Spectral Index. *Agronomy*. 2022; 12(7):1729.
https://doi.org/10.3390/agronomy12071729

**Chicago/Turabian Style**

Tang, Zijun, Jinjin Guo, Youzhen Xiang, Xianghui Lu, Qian Wang, Haidong Wang, Minghui Cheng, Han Wang, Xin Wang, Jiaqi An,
and et al. 2022. "Estimation of Leaf Area Index and Above-Ground Biomass of Winter Wheat Based on Optimal Spectral Index" *Agronomy* 12, no. 7: 1729.
https://doi.org/10.3390/agronomy12071729