# Multi-Model Rice Canopy Chlorophyll Content Inversion Based on UAV Hyperspectral Images

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## Abstract

**:**

^{2}= 0.6258), and the model with the highest decision coefficient among the four dairy maturity models is “milk-ripe stage-BP” (R

^{2}= 0.6716), all of which can meet the requirement of accurately retrieving the SPAD value of rice canopy. The above results can provide a technical reference for the accurate, rapid and non-destructive monitoring of chlorophyll content in rice leaves and provide a core band selection basis for large-scale hyperspectral remote sensing monitoring of rice.

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Overview of the Study Area

#### 2.2. Composition of Remote Sensing System

#### 2.3. Image and Data Acquisition

#### 2.3.1. Hyperspectral Image Acquisition

#### 2.3.2. Determination of SPAD Values of Rice Canopy in the Field

#### 2.3.3. Hyperspectral Image Processing

#### 2.3.4. Spectral Feature Analysis and Extraction of Sensitive Bands

#### 2.4. Methods

#### 2.4.1. Univariate Regression

#### 2.4.2. Partial Least Squares Regression (PLSR)

#### 2.4.3. Support Vector Machine Regression (SVR)

_{i}is the independent variable, yi is the dependent variable, and n is the number of samples. The idea of SVR is to find a function $f\in F$ to solve the problem of minimizing the nonlinear regression expectation risk R(f) by partitioning the hyperplane. F denotes the set of distributed functions, and the precision of the fitted sum is denoted by the error function coefficient ε. SVR solves the linear regression with a fitted function: f(x) = wx + b. x, w, and b represent the sample vector, normal vector, and offset of the regression function, respectively. This transforms the problem of solving the regression function into a convex quadratic linear planning problem according to the idea of categorically optimal hyperplanes. When it is difficult to partition the hyperplane in the linear case, sample points whose distance to the desired hyperplane is greater than 0 are introduced into the relaxation variable. The degree of penalty for sample points with deviations greater than ε depends on whether the constant C is greater than 0 and then introduces a Lagrangian function. The optimal programming problem is solved by solving its saddle point, transforming the original problem into a dual problem. From this, the optimal regression function (Equation (6)) can be obtained:

_{i}, x) so as to obtain the normal vector (Equation (7)) and the optimal regression function of the following regression function (Equation (8)):

#### 2.4.4. The BP Neural Network Regression

## 3. Results and Discussion

#### 3.1. Model Construction

#### 3.1.1. Model Construction Based on Univariate Regression

^{2}) of canopy SPAD values and characteristic parameters are higher than 0.4 from 90 modeling samples selected from two growth periods. In Table 3, the characteristic parameters at the booting stage (DVI (R

_{Nir}, R

_{re}), RVI (S

_{Dr}, S

_{Db})) and milk-ripe stage (NDVI (R

_{818}, R

_{686}), DVI (R

_{Nir}, Rre), CI (RNir, Rg), S

_{Dr}) and the SPAD value of rice canopy R

^{2}reached more than 0.6, showing a very significant correlation.

^{2}above 0.6 are more in the milk-ripe stage than in the booting stage. We found that the rice plant just entered the growth and development stage at the booting stage; at this time, the single leaf area is small, and the chlorophyll content in the leaf is low. Therefore, this time the UAV hyperspectral instrument has a weak ability to capture canopy information. While the milk-ripe stage is the peak development period of rice plants, when the area of single leaf increases and the coverage is high, which is easy to be identified by the UAV hyperspectral instrument. Through the analysis and comparison of the results of eight groups of characteristic parameters, it is found that: in terms of weakening soil and environmental impact, the first-order micro classification parameters (S

_{Dr}, RVI(S

_{Dr}, S

_{Db})) show a higher correlation than the soil regulation parameters (SAVI), indicating that the original spectrum can effectively weaken the impact of environmental noise after the first-order derivative transformation.

#### 3.1.2. Model Construction Based on Partial Least Squares Regression (PLSR)

_{818}, R

_{686}), DVI (R

_{Nir}, R

_{re}), SAVI (R

_{770}, R

_{647}), GRVI (R

_{818}, R

_{518}), CI (R

_{Nir}, R

_{g}), S

_{Dr}and RVI (S

_{Dr}, S

_{Db})) in Table 3 and the sensitive bands in Table 1 were used as independent variables for the models of different parameter types to construct the PLSR-based inversion model of rice canopy SPAD values.

^{2}models were all greater than 0.6. Among them, the highest value is based on the characteristic parameter model R

^{2}of the milk-ripe stage, which reaches 0.6854. At the same time, the overall accuracy of the characteristic parameter model is higher than that of the sensitive band model. It can be seen that the optimal model at the booting stage is “booting stage-PLSR”, and its model equation is the corresponding characteristic parameter model at the booting stage in Table 4. The optimal model selection at the milk-ripe stage is consistent with the booting stage.

#### 3.1.3. Model Construction Based on Support Vector Machine Regression (SVR)

_{818}, R

_{686}), DVI (R

_{Nir}, R

_{re}), SAVI (R

_{770}, R

_{647}), GRVI (R

_{818}, R

_{518}), CI (R

_{Nir}, R

_{g}), SDr and RVI (SDr, S

_{Db})) in Table 3 and the sensitive bands corresponding to each fertility stage in Table 1 were selected as input parameters to evaluate the prediction effect of different combinations using root mean squared error (RMSE). The input parameters of the final booting stage are “SAVI, GRVI and S

_{Dr}”, and the input parameters of the milk-ripe period are “DVI, SAVI, CI and S

_{Dr}”. Combined with previous experience, using Gaussian radial basis (RBF) as a nuclear function, the cross-validation method was used to generate in MATLAB software Optimal model to obtain the optimal parameters determined by the system (penalty factor C = 100, width factor = 0.1).

_{Dr}) and the milk-ripe stage input parameters (DVI, SAVI, CI, S

_{Dr}). The parameters of the inversion model are shown in Table 5.

#### 3.1.4. Model Construction Based on BP Neural Network Regression

_{Dr}and RVI) and five at the milk-ripe stage (NDVI, DVI, SAVI, CI and S

_{Dr}) were selected according to the cumulative cross-validation value ${Q}_{{h}^{2}}$ > 0.5. The following feature parameters are used as input layer data. The number of nodes in the implied layer is determined as five according to the formula $l=\sqrt{n+m}+a$, and the number of nodes in the output layer is one. Therefore, in this experiment, the BP neural network model for retrieving the SPAD value of the canopy at the booting stage and the milk-ripe stage of the experiment is that the input layer node is five, the hidden layer node is eight, the output layer node is one, and the training method is Trainlm training function, the training rate is 0.05.

#### 3.2. Model Accuracy Analysis

#### 3.2.1. Univariate Model Accuracy Analysis

^{2}above 0.6 in Table 3 were selected to construct an inversion model of the SPAD value of the rice canopy. The spectral values of 30 test sample points at each fertility stage in the experimental field were substituted into the above model as independent variables and canopy SPAD values as dependent variables. The prediction accuracy of the model was checked in SPSS software using three statistical indicators: coefficient of determination (R

^{2}), root mean square error (RMSE) and mean relative error (RE). The closer the R

^{2}is to one, the smaller the RMSE and RE, indicating higher model accuracy, and the results of the analysis are shown in Figure 6 and Table 10.

_{Dr}, S

_{Db})” (R

^{2}= 0.5576) is closer to one. The root mean square error (RMSE = 5.29) and the relative error (RE = 14.6) are smaller, which indicates that the model “booting stage-RVI (S

_{Dr}, S

_{Db})” with greater predictive power and accuracy. In the same analysis, the corresponding model “milk-ripe stage-DVI (R

_{Nir}, R

_{re})” has a higher predictive power and accuracy during the milk-ripe stage and therefore can be used as an inverse model for the respective fertility period.

#### 3.2.2. PLSR Model Accuracy Analysis

^{2}, RMSE, and RE, the results of which are shown in Figure 7 and Table 11.

^{2}and RMSE results of the modeled samples are similar to those of the test samples. This indicates that PLSR-based multiple regression models have good stability and generalizability. This shows that when inverting rice canopy SPAD values, the PLSR model is effective in inverting rice canopy SPAD values based on the structural robustness of the PLSR model; even if a certain input parameter changes, it is not easy to affect the overall inversion effect.

#### 3.2.3. SVR Model Accuracy Analysis

^{2}, RMSE, and RE, with the results shown in Figure 8 and Table 12.

#### 3.2.4. BP Neural Network Model Accuracy Analysis

^{2}, RMSE, and RE, and the results are shown in Figure 9 and Table 13.

^{2}reached above 0.7 for the first time, reflecting a very good inversion of the Effect. In addition, the RMSE value is smaller while obtaining a higher R

^{2}, and the distribution of test sample point values is more uniform in the figure, which shows that the BP neural network can be effectively used in the rice canopy SPAD value inversion test.

#### 3.3. Optimal Inversion Model Selection

^{2}are closest to one, and the closer the fitting result is to the 1:1 line (dashed line in the above figures), the more accurate the estimation result is. It can be seen that among the four test models at the booting stage, the regression slope (1.2626) and the determination coefficient (R

^{2}= 0.6258) of the “booting stage-SVR” model are closest to one, and the root mean square error is the smallest (RMSE = 7.3651). The inversion estimates the highest accuracy of the mapping results. In the four models of the milk-ripe stage test, the regression slope (1.0868) and the coefficient of determination (R

^{2}= 0.6717) of the “milk-ripe stage-BP” model are closest to one, and the root mean square error is the smallest (RMSE = 6.3266). The inversion estimates the highest accuracy of the mapping results.

## 4. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

- Hu, T.; Chen, Y.; Li, D.; Long, C.; Wen, Z.; Hu, R.; Chen, G. Rice Variety Identification Based on the Leaf Hyperspectral Feature via LPP-SVM. Int. J. Pattern Recognit. Artif. Intell.
**2022**, 36, 17. [Google Scholar] [CrossRef] - Luo, Y.; Huang, D.; Li, D.; Wu, L. On farm storage, storage losses and the effects of loss reduction in China. Resour. Conserv. Recycl.
**2020**, 162, 9. [Google Scholar] [CrossRef] - Xiao, X.M.; Boles, S.; Liu, J.Y. Mapping paddy rice agriculture in southern China using multi-temporal MODIS images. Remote Sens. Environ.
**2005**, 95, 480–492. [Google Scholar] [CrossRef] - Xu, Y.B.; Li, J.Y.; Wan, J.M. Agriculture and crop science in China: Innovation and sustainability. Crop J.
**2017**, 5, 95–99. [Google Scholar] [CrossRef] - Chen, T.; Fu, W.; Yu, J.; Feng, B.; Li, G.; Fu, G.; Tao, L. The Photosynthesis Characteristics of Colored Rice Leaves and Its Relation with Antioxidant Capacity andAnthocyanin Content. Sci. Agric. Sin.
**2022**, 55, 12. [Google Scholar] - Li, X.; Lu, X.; Xi, J.; Zhang, Y.; Zhang, M. Univeisal method to detect the chlorophyll content in plant leaves with RGB images captured by smart phones. Trans. Chin. Soc. Agric. Eng.
**2021**, 37, 7. [Google Scholar] - Ravier, C.; Quemada, M.; Jeuffroy, M.-H. Use of a chlorophyll meter to assess nitrogen nutrition index during the growth cycle in winter wheat. Field Crops Res.
**2017**, 214, 73–82. [Google Scholar] [CrossRef] - Ganeva, D.; Roumenina, E.; Dimitrov, P. Phenotypic Traits Estimation and Preliminary Yield Assessment in Different Phenophases of Wheat Breeding Experiment Based on UAV Multispectral Images. Remote Sens.
**2022**, 14, 1019. [Google Scholar] [CrossRef] - Filion, R.; Bernier, M.; Paniconi, C. Remote sensing for mapping soil moisture and drainage potential in semi-arid regions: Applications to the Campidano plain of Sardinia, Italy. Sci. Total Environ.
**2016**, 543, 862–876. [Google Scholar] [CrossRef] - Mulla, D.J. Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosyst. Eng.
**2013**, 114, 358–371. [Google Scholar] [CrossRef] - Weiss, M.; Jacob, F.; Duveiller, G. Remote sensing for agricultural applications: A meta-review. Remote Sens. Environ.
**2020**, 236, 111402. [Google Scholar] [CrossRef] - Tao, H.; Feng, H.; Xu, L.; Miao, M.; Long, H.; Yue, J.; Li, Z.; Yang, G.; Yang, X.; Fan, L. Estimation of Crop Growth Parameters Using UAV-Based Hyperspectral Remote Sensing Data. Sensors
**2020**, 20, 1296. [Google Scholar] [CrossRef] [PubMed] - Burkart, A.; Cogliati, S.; Schickling, A.; Rascher, U. A Novel UAV-Based Ultra-Light Weight Spectrometer for Field Spectroscopy. IEEE Sens. J.
**2014**, 14, 62–67. [Google Scholar] [CrossRef] - Wei, Z.; Han, Y.; Li, M.; Yang, K.; Yang, Y.; Luo, Y.; Ong, S.-H. A Small UAV Based Multi-Temporal Image Registration for Dynamic Agricultural Terrace Monitoring. Remote Sens.
**2017**, 9, 904. [Google Scholar] [CrossRef] - Huang, Y.; Ma, Q.; Wu, X.; Li, H.; Xu, K.; Ji, G.; Qian, F.; Li, L.; Huang, Q.; Long, Y.; et al. Estimation of chlorophyll content in Brassica napus based on unmanned aerial vehicle images. Oil Crop Sci.
**2022**, 7, 149–155. [Google Scholar] [CrossRef] - Kim, E.-J.; Nam, S.-H.; Koo, J.-W.; Hwang, T.-M. Hybrid Approach of Unmanned Aerial Vehicle and Unmanned Surface Vehicle for Assessment of Chlorophyll-a Imagery Using Spectral Indices in Stream, South Korea. Water
**2021**, 13, 1930. [Google Scholar] [CrossRef] - Ballesteros, R.; Ortega, J.F.; Hernández, D.; Moreno, M.A. Applications of georeferenced high-resolution images obtained with unmanned aerial vehicles. Part II: Application to maize and onion crops of a semi-arid region in Spain. Precis. Agric.
**2014**, 15, 593–614. [Google Scholar] [CrossRef] - Calderón, R.; Navas-Cortés, J.A.; Lucena, C.; Zarco-Tejada, P.J. High-resolution airborne hyperspectral and thermal imagery for early, detection of Verticillium wilt of olive using fluorescence, temperature and narrow-band spectral indices. Remote Sens. Environ.
**2013**, 139, 231–245. [Google Scholar] [CrossRef] - Panigrahi, N.; Das, B.S. Canopy Spectral Reflectance as a Predictor of Soil Water Potential in Rice. Water Resour. Res.
**2018**, 54, 2544–2560. [Google Scholar] [CrossRef] - Poblete-Echeverría, C.; Olmedo, G.F.; Ingram, B.; Bardeen, M. Detection and Segmentation of Vine Canopy in Ultra-High Spatial Resolution RGB Imagery Obtained from Unmanned Aerial Vehicle (UAV): A Case Study in a Commercial Vineyard. Remote Sens.
**2017**, 9, 268. [Google Scholar] [CrossRef] - Gitelson, A.A.; Peng, Y.; Arkebauer, T.J. Relationships between gross primary production, green LAI, and canopy chlorophyll content in maize: Implications for remote sensing of primary production. Remote Sens. Environ.
**2014**, 144, 65–72. [Google Scholar] [CrossRef] - He, Z.; Wu, K.; Wang, F.; Jin, L.; Zhang, R.; Tian, S.; Wu, W.; He, Y.; Huang, R.; Yuan, L.; et al. Fresh Yield Estimation of Spring Tea via Spectral Differences in UAV Hyperspectral Images from Unpicked and Picked Canopies. Remote. Sens.
**2023**, 15, 1100. [Google Scholar] [CrossRef] - Padalia, H.; Sinha, S.K.; Bhave, V.; Trivedi, N.K.; Kumar, A.S. Estimating canopy LAI and chlorophyll of tropical forest plantation (North India) using Sentinel-2 data. Adv. Space Res.
**2020**, 65, 458–469. [Google Scholar] [CrossRef] - Thomas, V.; Treitz, P.; McCaughey, J.H.; Noland, T.; Rich, L. Canopy chlorophyll concentration estimation using hyperspectral and lidar data for a boreal mixedwood forest in northern Ontario, Canada. Int. J. Remote Sens.
**2008**, 29, 1029–1052. [Google Scholar] [CrossRef] - Feng, M.C.; Guo, X.L.; Wang, C. Monitoring and evaluation in freeze stress of winter wheat (Triticum aestivum L.) through canopy hyperspectrum reflectance and multiple statistical analysis. Ecol. Indic.
**2018**, 84, 290–297. [Google Scholar] [CrossRef] - Shi, H.; Guo, J.; An, J.; Tang, Z.; Wang, X.; Li, W.; Zhao, X.; Jin, L.; Xiang, Y.; Li, Z.; et al. Estimation of Chlorophyll Content in Soybean Crop at Different Growth Stages Based on Optimal Spectral Index. Agronomy
**2023**, 13, 663. [Google Scholar] [CrossRef] - Liang, L.; Qin, Z.; Zhao, S.; Di, L.; Zhang, C.; Deng, M.; Lin, H.; Zhang, L.; Wang, L.; Liu, Z. Estimating crop chlorophyll content with hyperspectral vegetation indices and the hybrid inversion method. Int. J. Remote Sens.
**2016**, 37, 2923–2949. [Google Scholar] [CrossRef] - Milas, A.S.; Romanko, M.; Reil, P.; Abeysinghe, T.; Marambe, A. The importance of leaf area index in mapping chlorophyll content of corn under different agricultural treatments using UAV images. Int. J. Remote Sens.
**2018**, 39, 5415–5431. [Google Scholar] [CrossRef] - Wu, C.; Han, X.; Niu, Z.; Dong, J. An evaluation of EO-1 hyperspectral Hyperion data for chlorophyll content and leaf area index estimation. Int. J. Remote Sens.
**2010**, 31, 1079–1086. [Google Scholar] [CrossRef] - Fageria, N.K. Yield physiology of rice. J. Plant Nutr.
**2007**, 30, 843–879. [Google Scholar] [CrossRef] - Hlaváčová, M.; Klem, K.; Rapantová, B.; Novotná, K.; Urban, O.; Hlavinka, P.; Smutná, P.; Horáková, V.; Škarpa, P.; Pohanková, E.; et al. Interactive effects of high temperature and drought stress during stem elongation, anthesis and early grain filling on the yield formation and photosynthesis of winter wheat. Field Crops Res.
**2018**, 221, 182–195. [Google Scholar] [CrossRef] - Guo, C.; Guo, X. Estimation of wetland plant leaf chlorophyll content based on continuum removal in the visible domain. Acta Ecol. Sin.
**2016**, 36, 6538–6546. [Google Scholar] - Guo, Q.; Wu, X.; Bing, Q.; Pan, Y.; Wang, Z.; Fu, Y.; Wang, D.; Liu, J. Study on Retrieval of Chlorophyll-a Concentration Based on Landsat OLI Imagery in the Haihe River, China. Sustainability
**2016**, 8, 758. [Google Scholar] [CrossRef] - Matus-Hernández, M.; Hernández-Saavedra, N.Y.; Martínez-Rincón, R.O. Predictive performance of regression models to estimate Chlorophyll-a concentration based on Landsat imagery. PLoS ONE
**2018**, 13, e0205682. [Google Scholar] [CrossRef] - Ryan, K.; Ali, K. Application of a partial least-squares regression model to retrieve chlorophyll-aconcentrations in coastal waters using hyper-spectral data. Ocean Ence J.
**2016**, 51, 209–221. [Google Scholar] - Song, K.; Lu, D.; Li, L.; Li, S.; Wang, Z.; Du, J. Remote sensing of chlorophyll-a concentration for drinking water source using genetic algorithms (GA)-partial least square (PLS) modeling. Ecol. Inform.
**2012**, 10, 25–36. [Google Scholar] [CrossRef] - Kown, Y.; Baek, S.H.; Lim, Y.K.; Pyo, J.; Ligaray, M.; Park, Y.; Cho, K.H. Monitoring Coastal Chlorophyll-a Concentrations in Coastal Areas Using Machine Learning Models. Water
**2018**, 10, 1020. [Google Scholar] [CrossRef] - Shao, Y.-H.; Chen, W.-J.; Deng, N.-Y. Nonparallel hyperplane support vector machine for binary classification problems. Inf. Sci.
**2014**, 263, 22–35. [Google Scholar] [CrossRef] - Liu, M.; Liu, X.; Li, M.; Fang, M.; Chi, W. Neural-network model for estimating leaf chlorophyll concentration in rice under stress from heavy metals using four spectral indices. Biosyst. Eng.
**2010**, 106, 223–233. [Google Scholar] [CrossRef] - Lu, F.; Chen, Z.; Liu, W.; Shao, H. Modeling chlorophyll-a concentrations using an artificial neural network for precisely eco-restoring lake basin. Ecol. Eng.
**2016**, 95, 422–429. [Google Scholar] [CrossRef] - Buma, B. Evaluating the utility and seasonality of NDVI values for assessing post-disturbance recovery in a subalpine forest. Environ. Monit. Assess.
**2012**, 184, 3849–3860. [Google Scholar] [CrossRef] [PubMed] - Gitelson, A.A.; Kaufman, Y.J.; Merzlyak, M.N. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens. Environ.
**1996**, 58, 289–298. [Google Scholar] [CrossRef] - Aasen, H.; Burkart, A.; Bolten, A. Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: From camera calibration to quality assurance. ISPRS J. Photogramm. Remote Sens.
**2015**, 108, 245–259. [Google Scholar] [CrossRef] - Haboudane, D.; Miller, J.R.; Tremblay, N.; Zarco-Tejada, P.J.; Dextraze, L. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sens. Environ.
**2002**, 81, 416–426. [Google Scholar] [CrossRef] - Daughtry, C.S.T.; Walthall, C.L.; Kim, M.S. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sens. Environ.
**2000**, 74, 229–239. [Google Scholar] [CrossRef] - Rodger, A.; Laukamp, C.; Haest, M.; Cudahy, T. A simple quadratic method of absorption feature wavelength estimation in continuum removed spectra. Remote Sens. Environ.
**2012**, 118, 273–283. [Google Scholar] [CrossRef] - Dash, J.; Curran, P.J. Evaluation of the MERIS terrestrial chlorophyll index (MTCI). Adv. Space Res.
**2007**, 39, 100–104. [Google Scholar] [CrossRef] - Cao, Q.; Miao, Y.X.; Shen, J.N. Improving in-season estimation of rice yield potential and responsiveness to topdressing nitrogen application with Crop Circle active crop canopy sensor. Precis. Agric.
**2016**, 17, 136–154. [Google Scholar] [CrossRef] - Wang, F.-m.; Huang, J.-f.; Tang, Y.-l.; Wang, X.-z. New Vegetation Index and Its Application in Estimating Leaf Area Index of Rice. Rice Sci.
**2007**, 14, 195–203. [Google Scholar] [CrossRef] - Gitelson, A.A.; Viña, A.; Ciganda, V.; Rundquist, D.C.; Arkebauer, T.J. Remote estimation of canopy chlorophyll content in crops. Geophys. Res. Lett.
**2005**, 32, L08403. [Google Scholar] [CrossRef] - Kanke, Y.; Tubaña, B.; Dalen, M.; Harrell, D. Evaluation of red and red-edge reflectance-based vegetation indices for rice biomass and grain yield prediction models in paddy fields. Precis. Agric.
**2016**, 17, 507–530. [Google Scholar] [CrossRef] - Ma, M.; Xu, T.; Zhou, Y. UAV HD Image Detection Method for SPAD in Northeast Japonica Rice. J. Shenyang Agric. Univ.
**2017**, 48, 757–762. [Google Scholar] - Ban, S.; Liu, W.; Tian, M.; Wang, Q.; Yuan, T.; Chang, Q.; Li, L. Rice Leaf Chlorophyll Content Estimation Using UAV-Based Spectral Images in Different Regions. Agronomy
**2022**, 12, 2832. [Google Scholar] [CrossRef] - Yuan, W.; Xu, T.; Cao, Y. Estimation of chlorophyll content in rice canopy leaves based on main base analysis and dimensionality reduction method. J. Zhejiang Univ. Agric. Life Sci.
**2018**, 44, 423–430. [Google Scholar]

**Figure 2.**(

**a**) Unmanned hyperspectral systems and (

**b**) Sampling of SPAD value information in the field.

**Figure 3.**(

**a**) Correlation of rice canopy SPAD values with original spectra, (

**b**) first-order derivative spectra and (

**c**) de-enveloped line spectra.

**Figure 6.**Correlation analysis between measured and predicted SPAD values in the rice canopy based on univariate regression (

**a**) Booting stage-DVI(R

_{Nir},R

_{re}); (

**b**) Booting stage-RVI(S

_{Dr},S

_{Db}); (

**c**) Milk-ripe stage-NDVI(R

_{818},R

_{686}); (

**d**) Milk-ripe stage-DVI(R

_{Nir},R

_{re}); (

**e**): Milk-ripe stage-CI(R

_{Nir},R

_{g}); (

**f**) Milk-ripe stage-S

_{Dr}).

**Figure 7.**Correlation analysis between measured and predicted SPAD values in rice canopy based on the PLSR model (

**a**) Booting stage-PLSR; (

**b**) Milk-ripe stage-PLSR.

**Figure 8.**Correlation analysis between measured and predicted SPAD values in the rice canopy based on SVR models (

**a**) Booting stage-SVR; (

**b**) Milk-ripe stage-SVR.

**Figure 9.**Correlation analysis between measured and predicted SPAD values in rice canopy based on BP neural network (

**a**) Booting stage-BP; (

**b**) Milk-ripe stage-BP.

**Figure 10.**Mapping sample areas of booting and milk-ripe stage (

**a**) Booting stage; (

**b**) Milk-ripe stage.

**Figure 11.**Distribution of SPAD value in rice canopy at different growth stages (

**a**) Regression mapping of Booting-RVI(S

_{Dr},S

_{Db})model; (

**b**) Regression mapping of Booting-PLSR model; (

**c**) Regression mapping of Booting-SVR model; (

**d**) Regression mapping of Booting-BP model; (

**e**) Regression mapping of Milk-DVI(R

_{Nir},R

_{re}) model; (

**f**) Regression mapping of Milk-PLSR model; (

**g**) Regression mapping of Milk-SVR model; (

**h**) Regression mapping of Milk-BP model.

**Figure 12.**Mapping inspection of SPAD value of rice canopy at booting stage (

**a**) Booting stage-RVI(S

_{Dr},S

_{Db}); (

**b**) Booting stage-PLSR; (

**c**) Booting stage-SVR; (

**d**) Booting stage-BP.

**Figure 13.**Mapping inspection of SPAD value of rice canopy at milk-ripe stage (

**a**) Milk-ripe stage--DVI(R

_{Nir},R

_{re}); (

**b**) Milk-ripe stage-PLSR; (

**c**) Milk-ripe stage-SVR; (

**d**) Milk-ripe stage-BP.

Growth Periods | Spectral Types | Sensitive Bands (nm) |
---|---|---|

Booting stage | Original spectrum | 567,686,770,818 |

First derivative spectrum | 539,560,728,755 | |

De-envelope spectrum | 525,686,735 | |

milk-ripe stage | Original spectrum | 553,560,763 |

First derivative spectrum | 518,546,728 | |

De-envelope spectrum | 647,728,818 |

Vegetation Index | Calculation Formulae or Definitions | Source of Formula |
---|---|---|

NDVI | (R_{λi} − R_{λj})/(R_{λi} + R_{λj}) | [41] |

DVI | R_{Nir} − R_{re} | [42] |

SAVI | 1.5 $\times $ (R_{λi} − R_{λj})/(R_{λi} − R_{λj} + 0.5) | [43] |

OSAVI | (1 + 0.16) (R_{λi} − R_{λj})/(R_{λi} + R_{λj} + 0.16) | [43] |

TCARI | 3[(R_{λi} − R_{λj}) − 0.2(R_{λi} − R_{g})(R_{λi}/R_{λj})] | [44] |

MCARI | [(R_{λi}-R_{λj}) − 0.2(R_{λi} − R_{g})](R_{λi}/R_{λj}) | [45] |

RDVI | $\sqrt{\mathrm{N}\mathrm{D}\mathrm{V}\mathrm{I}\times \mathrm{D}\mathrm{V}\mathrm{I}}$ | [46] |

MTCI | (R_{λj} − R_{λi})/(R_{λi} − R_{λk}) | [47] |

GRVI | R_{λi}/R_{g} | [48] |

RNDVI | (R_{λi} − R_{λj})/$\sqrt{{R}_{\mathsf{\lambda}\mathrm{i}}+{R}_{\mathsf{\lambda}\mathrm{j}}}$ | [49] |

CI | R_{Nir}/R_{g} − 1 | [50] |

RERDVI | (R_{λi} − R_{re})/$\sqrt{{\mathrm{R}}_{\mathsf{\lambda}\mathrm{i}}+{\mathrm{R}}_{\mathrm{r}\mathrm{e}}}$ | [51] |

_{Nir}, R

_{re}, R

_{g}, and R

_{λ}represent the mean of the spectral band reflectance at NIR, red, green, and wavelength λ, respectively.

Characteristic Parameters | R^{2} | |
---|---|---|

Booting Stage | Milk-Ripe Stage | |

NDVI (R_{818},R_{686}) | 0.5216 | 0.6015 |

DVI (R_{Nir},R_{re}) | 0.6252 | 0.6126 |

SAVI (R_{770},R_{647}) | 0.5317 | 0.5791 |

GRVI (R_{818},R_{518}) | 0.5864 | 0.5215 |

CI (R_{Nir},R_{g}) | 0.5254 | 0.6136 |

S_{Dr} | 0.5571 | 0.6042 |

RVI (S_{Dr},S_{Db}) | 0.6158 | 0.5181 |

Growth Stages | Parameters | Model Equations | R^{2} | RMSE |
---|---|---|---|---|

Booting stage | Feature parameters | y = −0.551x_{2} + 38.540x_{3} − 0.103x_{4} + 0.036x_{6} + 2.846x_{7} − 6.505 | 0.6341 | 9.9688 |

Original spectrum | y = −0.515R_{567} + 1.445R_{686} + 0.585R_{770} + 1.017R_{818} − 44.006 | 0.6115 | 19.528 | |

First derivative | y = 3.713R_{539} − 0.578R_{560} + 1.082R_{728} − 0.577R_{755} + 7.575 | 0.6150 | 16.0587 | |

De-envelope | y = −0.455R_{525} + 1.650R_{686} + 1.709R_{735} − 24.809 | 0.6176 | 7.5396 | |

Milk-ripe stage | Feature parameters | y = −32.968x_{1} − 1.716x_{2} − 4.567x_{3} + 6.608x_{4} + 0.925x_{5} − 0.501 | 0.6854 | 6.3586 |

Original spectrum | y = 1.536R_{553} − 0.190R_{560} + 0.625R_{763} + 0.941 | 0.6181 | 22.5404 | |

First derivative | y = 0.255R_{518} − 1.329R_{546} + 1.468R_{72}8 − 1.108 | 0.6029 | 5.0406 | |

De-envelope | y = −0.551x_{2} + 38.540x_{3} − 0.103x_{4} + 0.036x_{6} + 2.846x_{7} − 6.505 | 0.6011 | 26.8097 |

_{1}, x

_{2}, x

_{3}, x

_{4}, x

_{5}, x

_{6}, x

_{7}are NDVI (R

_{818}, R

_{686}), DVI (R

_{Nir}, R

_{re}), SAVI (R

_{770}, R

_{647}), GRVI (R

_{818}, R

_{518}), CI (R

_{Nir}, R

_{g}), S

_{Dr}and RVI (S

_{Dr}, S

_{Db}) at the corresponding values of each growth stage, R

_{λ}represents the reflectance at the wavelength λ.

Growth Stages | Input Parameters | Kernel Function | C | ${\mathit{\sigma}}^{2}$ |
---|---|---|---|---|

Booting stage | SAVI, GRVI, S_{Dr} | RBF | 100 | 0.1 |

Milk-ripe stage | DVI, SAVI, CI, S_{Dr} | RBF | 100 | 0.1 |

Hidden Layer Weights | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | |
---|---|---|---|---|---|---|---|---|---|

Input Layer Weights | |||||||||

1 | 0.2165 | 1.2019 | 1.2364 | −1.1247 | −1.1021 | 0.5656 | 0.3653 | 1.0321 | |

2 | −1.8763 | 0.3487 | 0.7825 | −0.9571 | −1.9274 | 1.1894 | 0.7453 | 0.7985 | |

3 | 1.0912 | −0.9875 | −0.6873 | 0.2094 | 1.3595 | −0.1209 | −1.2673 | 1.8632 | |

4 | 0.2317 | 0.5423 | −0.4501 | 1.7389 | 1.6120 | −0.1075 | 1.2984 | −0.5417 | |

5 | −0.7612 | 1.3211 | 1.2938 | −0.5210 | −1.9127 | −1.0836 | −0.1279 | −1.8237 | |

Threshold (b) | 2.1835 | −1.5408 | 1.7225 | 0.7613 | 0.9187 | −0.6521 | 1.2013 | −1.6091 |

Hidden Layer Nodes | Weights | Threshold | Hidden Layer Nodes | Weights | Threshold |
---|---|---|---|---|---|

1 | 1.9812 | 0.472 | 5 | −0.3017 | 0.472 |

2 | 0.4709 | 6 | 1.0145 | ||

3 | −1.3747 | 7 | 0.6430 | ||

4 | −0.5862 | 8 | −1.5436 |

Hidden Layer Weights | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | |
---|---|---|---|---|---|---|---|---|---|

Input Layer Weights | |||||||||

1 | 1.1904 | −0.8730 | 0.5134 | −0.8107 | 1.3467 | 0.7564 | 1.3875 | −1.3108 | |

2 | 0.7237 | 1.5719 | −1.7891 | 1.3416 | 0.9134 | −1.1876 | 0.1283 | 1.0234 | |

3 | −1.5064 | 0.3781 | −0.3475 | −1.9871 | −0.3453 | −0.6034 | −0.8967 | 0.3246 | |

4 | 0.3984 | −1.0137 | −0.4501 | 0.3401 | 1.7486 | 1.8793 | −1.4113 | 1.2803 | |

5 | −1.1350 | 1.4503 | 1.2938 | −0.0519 | −0.5348 | −1.0434 | 0.1987 | −1.3146 | |

Threshold (b) | 1.1701 | −1.1746 | −0.3114 | 0.3260 | −0.7408 | −1.8430 | −0.7634 | 0.8753 |

Hidden Layer Nodes | Weights | Threshold | Hidden Layer Nodes | Weights | Threshold |
---|---|---|---|---|---|

1 | −1.4578 | 0.5015 | 5 | 1.3014 | 0.5015 |

2 | −0.3418 | 6 | 0.4357 | ||

3 | 2.0134 | 7 | −1.5834 | ||

4 | 1.3619 | 8 | 0.3101 |

Models | Model Equations | R^{2} | RMSE | RE | |
---|---|---|---|---|---|

Booting stage | DVI(R_{Nir},R_{re}) | y = 74.486x^{0.8695} | 0.5296 | 5.29 | 14.6 |

RVI(S_{Dr},S_{Db}) | y = −0.0629x^{2} + 4.3705x + 9.9008 | 0.5576 | 5.15 | 14.3 | |

Milk-ripe stage | NDVI(R_{818},R_{686}) | y = 70.601x + 6.1291 | 0.4857 | 7.73 | 15.3 |

DVI(R_{Nir},R_{re}) | y = −10.817x^{2} + 101.17x + 17.075 | 0.5828 | 6.99 | 15.1 | |

CI(R_{Nir},R_{g}) | y = 0.1896x^{2} + 5.0079x + 25.901 | 0.5341 | 7.35 | 16.3 | |

S_{Dr} | y = 0.0055x^{2} + 1.7285x − 13.78 | 0.5641 | 8.53 | 14.5 |

Growth Stages | Models | R^{2} | RMSE | RE |
---|---|---|---|---|

Booting stage | Booting stage-PLSR | 0.6228 | 7.17 | 21.2 |

Milk-ripe stage | Milk-ripe stage-PLSR | 0.6757 | 9.12 | 17.9 |

Growth Stages | Models | R^{2} | RMSE | RE |
---|---|---|---|---|

Booting stage | Booting stage-SVR | 0.6399 | 6.56 | 16.3 |

Milk-ripe stage | Milk-ripe stage-SVR | 0.6825 | 8.11 | 14.7 |

Growth Stages | Models | R^{2} | RMSE | RE |
---|---|---|---|---|

Booting stage | Booting stage-BP | 0.6537 | 5.68 | 15.2 |

Milk-ripe stage | Milk-ripe stage-BP | 0.7076 | 8.22 | 17.6 |

Growth Stages | Models | Gradient | R^{2} | RMSE | RE |
---|---|---|---|---|---|

Booting stage | RVI (S_{Dr},S_{Db}) | 1.2596 | 0.5511 | 9.1527 | 21.1 |

PLSR | 1.2584 | 0.6187 | 7.9526 | 18.6 | |

SVR | 1.2551 | 0.6258 | 7.8599 | 20.6 | |

BP | 1.2626 | 0.6206 | 7.9001 | 20.6 | |

Milk-ripe stage | DVI (R_{Nir},R_{re}) | 1.1193 | 0.5752 | 11.1030 | 20.1 |

PLSR | 1.1805 | 0.6509 | 9.9778 | 19.6 | |

SVR | 1.17 | 0.6611 | 9.6688 | 16.5 | |

BP | 1.0868 | 0.6716 | 8.7710 | 15.8 |

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## Share and Cite

**MDPI and ACS Style**

Liu, H.; Lei, X.; Liang, H.; Wang, X.
Multi-Model Rice Canopy Chlorophyll Content Inversion Based on UAV Hyperspectral Images. *Sustainability* **2023**, *15*, 7038.
https://doi.org/10.3390/su15097038

**AMA Style**

Liu H, Lei X, Liang H, Wang X.
Multi-Model Rice Canopy Chlorophyll Content Inversion Based on UAV Hyperspectral Images. *Sustainability*. 2023; 15(9):7038.
https://doi.org/10.3390/su15097038

**Chicago/Turabian Style**

Liu, Hanhu, Xiangqi Lei, Hui Liang, and Xiao Wang.
2023. "Multi-Model Rice Canopy Chlorophyll Content Inversion Based on UAV Hyperspectral Images" *Sustainability* 15, no. 9: 7038.
https://doi.org/10.3390/su15097038