# Quantifying Chlorophyll Fluorescence Parameters from Hyperspectral Reflectance at the Leaf Scale under Various Nitrogen Treatment Regimes in Winter Wheat

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## Abstract

**:**

## 1. Introduction

^{2}(DPi), D705/D722, and D730/D706, have been developed to detect the subtle ChlF signal, because it has been found that the double-peak feature at 690 to 710 nm in the derivative reflectance was related to the natural fluorescence in both short-term and long-term stress experiments [14,17]. Although the photochemical reflectance index (PRI) has been used as an interspecific index of photosynthetic radiation use efficiency for foliar and canopy leaves [18], there has been some predicted bias when estimating Fv/Fm using PRI [13].

## 2. Materials and Methods

#### 2.1. Experimental Design

^{−1}P

_{2}O

_{5}and 135 kg ha

^{−1}KCl were applied for all treatments. For two winter wheat cultivars, the plant type of Shengxuan 6 is dispersed, and that of Yangmai 18 is erect. Then, 50% nitrogen (N) fertilizer was applied at the pre-planting stage and jointing period. Additional details about the experiments are provided in Table 1. The plot size was 6.5 × 5 m

^{2}for two experiments. The management of disease, pests, and weeds followed standard practices for chemical control in the local region.

#### 2.2. Data Acquisition

#### 2.2.1. Measurement of ChlF Parameters

#### 2.2.2. Measurement of Leaf Physiological Parameters

^{−2}∙s

^{−1}, which corresponds to saturation in the field. The temperature and concentration of CO

_{2}in the chamber were maintained at 25 °C and 380 μmol∙mol

^{−1}, respectively. The airflow rate through the leaf chamber was held constant at 500 μmol∙s

^{−1}. Air pressure and relative humidity were adjusted to near ambient levels. Finally, the plant leaves to be measured were transported to the laboratory for measuring the reflectance spectra.

#### 2.2.3. Measurement of Leaf Reflectance

#### 2.2.4. Optical Measurements of Pigments in Laboratory

_{ab}(Chl a + Chl b, ug/cm

^{2}) and Car (ug/cm

^{2}) were calculated.

#### 2.3. Data Analysis and Utilization

#### 2.3.1. Calculation of VIs

#### 2.3.2. Methods for REP Extractions

_{re}is the reflectance average of 670 nm and 780 nm.

_{1}λ + c

_{1}

_{2}λ + c

_{2}

#### 2.3.3. Continuous Wavelet Analysis (CWA)

^{1}, 2

^{2}, …, 2

^{8}) and are called by their respective power numbers (i.e., scale 1, scale 2, … scale 8). Random noise due to reflectance measurement can be contained in the low scale components [32], and hence only the wavelet features at scales 3 to 8 were analyzed to produce a correlation scalogram (coefficient of determination, R

^{2}). The regions with the highest 1% of R

^{2}for wavelet feature and ChlF parameters (Fv/Fm and Fv’/Fm’) were highlighted in red, and the wavelet features with the highest R

^{2}in each region were extracted.

#### 2.4. Statistical Analysis

^{2}(R

^{2}

_{C}), see Equation (8), and the root mean square error of calibration (RMSE

_{C}), see Equation (9), were statistical parameters used to evaluate the performances of all models in calibration. For validation, they are validation R

^{2}(R

^{2}

_{V}), see Equation (10); bias (Bias), see Equation (11); the root mean square error of prediction (RMSEv), see Equation (12); and the relative root mean square error of prediction (RRMSEv), see Equation (13). Higher values of R

^{2}

_{C}, R

^{2}

_{V}, and lower values of RMSE

_{C}, Bias, RMSEv, and RRMSEv indicate higher accuracy of the model.

## 3. Results

#### 3.1. Relationships among Leaf Pigments, Physiological Indices, and ChlF Parameters

_{ab}), physiological measurements (An and gs), dark-adapted ChlF parameters (Fo, Fm, and Fv/Fm), and steady-state ChlF parameters (Fo’, Fs, Fm’, Fv’/Fm’, NPQ, and Y(Ⅱ), ETR) acquired during the 2014 to 2015 growing season. The results showed that Fv/Fm and Fv’/Fm’ have better relationships with the majority of items listed in Table 4 than other ChlF parameters. For instance, among the dark-adapted ChlF parameters, Fv/Fm was significantly related to Car, Chl

_{ab}, An, gs, Fm, Fm’, Fv’/Fm’, and Y(Ⅱ) with R

^{2}> 0.5 and p value < 0.001. Other crop traits which showed moderately significant relationships with Fv/Fm (at p < 0.01) were Fs (R

^{2}= 0.20). Fo’ and ETR had a remarkable relationship with Fv/Fm at the p < 0.05 level (R

^{2}= 0.13 and 0.15). For the steady-state ChlF parameters, Fv’/Fm’ was significantly related with Car, Chl

_{ab}, An, gs, Fv’/Fm’, Fm’, Y(Ⅱ), and ETR (R

^{2}= 0.34, 0.35, 0.47, 0.29, 0.58, 0.59, 0.45, and 0.41, respectively; p < 0.001). Fm and NPQ also had significant relationships with Fv’/Fm’. In general, Fv/Fm and Fv’/Fm’ were the representative ChlF parameters in dark- and light-adapted conditions. Due to their fundamental significance and practical utility, they are the subjects of our research, and discussed in later sections.

#### 3.2. Dynamic Changes of Fv/Fm And Fv’/Fm’ under Different N Treatments during the Growing Season

#### 3.3. Semi-empirical Models for Estimating the Leaf Fv/Fm And Fv’/Fm’ Using Spectral Features

#### 3.3.1. Performance of VIs-based Fv/Fm and Fv’/Fm’ Models in Calibration and Validation Datasets

^{2}values ranging from 0.22 to 0.67 for Fv/Fm, and 0.35 to 0.56 for Fv’/Fm’. Only the R685/R655 showed logarithmic relationships with Fv/Fm. For both Fv/Fm and Fv’/Fm’, the CUR exhibited the best goodness of fit among all the reflectance ratio VIs in the calibration dataset (Figure 2A,F), superior to R680/R630, followed by R685/R655 and R750/R800. When applying the reflectance ratio VIs regression models to the validation dataset, the RRMSEv produced ranged from 1.95% to 3.43% and from 4.28% to 5.51% for predictive Fv/Fm and Fv’/Fm’, separately. The CUR yielded the best performance in validation for predicting Fv/Fm (Rv

^{2}= 0.50, Bias = 0.0056, RMSEv = 0.012, and RRMSEv = 1.95%) shown in Figure 3A and for Fv’/Fm’ (Rv

^{2}= 0.38, Bias = 0.0072, RMSEv = 0.030, and RRMSEv = 4.28%) exhibited in Figure 3F.

^{2}values of the derivative VIs and leaf Fv/Fm ranged from 0.38 to 0.64. D705/D722, D730/D706, and Dλρ/D720 gave similar performances in the calibration dataset (R

_{C}

^{2}= 0.63) (Table 5). When detecting Fv’/Fm’ using derivative VIs, all the best fitting lines were linear, with Rc

^{2}values from 0.22 to 0.60. D730/D706 and Dλρ/D720 performed similar, which were inferior to D705/D722. When the regression models of the calibration dataset were applied to the datasets acquired during the 2015 to 2016 growing season, the RRMSEv ranged from 1.87% to 3.69% for estimating Fv/Fm, and 3.96% to 6.41% for Fv’/Fm’. The D705/D722 produced the best prediction accuracy for Fv/Fm (R

_{V}

^{2}= 0.50, Bias = 0.0037, RMSEv = 0.015, and RRMSEv = 1.87%) shown in Table 5 and for Fv’/Fm’ (R

_{V}

^{2}= 0.40, Bias = 0.0031, RMSEv = 0.028, and RRMSEv = 3.96%) depicted in Table 6, significantly better than that of the other derivative VIs.

^{2}around 0.61 and 0.55, respectively. But with regards to the predictive accuracy of leaf Fv/Fm inversion, the best validation results were obtained by CIred edge (R

_{V}

^{2}= 0.39, Bias = −0.013, RMSEv = 0.022, and RRMSEv = 2.79%), which also was the most accurate Chl VIs to derive leaf Fv’/Fm’ (R

_{V}

^{2}= 0.32, Bias = −0.011, RMSEv = 0.030, and RRMSEv = 4.07%). In addition, the VIs related to structure properties and water content were proven to be invalid to estimate Fv/Fm and Fv’/Fm’ in the validation dataset.

#### 3.3.2. Performance of REP for Predicting Leaf Fv/Fm and Fv’/Fm’ in Calibration and Validation

_{V}

^{2}= 0.51 for Fv/Fm and R

_{V}

^{2}= 0.43 for Fv’/Fm’) and the lowest error (RRMSEv = 1.80% for Fv’/Fm’ and RRMSEv = 3.74% for Fv’/Fm’) as shown in Figure 2C,H, followed by WREP-S3.

#### 3.3.3. Models for Estimating the Leaf Fv/Fm and Fv’/Fm’ Based on Wavelet Features

^{2}values for the linear regressions ranging from 0.68 to 0.70 (Table 5). According to the Rc

^{2}values, the wavelet features sensitive to Fv/Fm were WF (702 nm, scale 3), WF (637 nm, scale 3), WF (704 nm, scale 4), WF (592 nm, scale 5), and WF (575 nm, scale 6) in sequence, which gave similar performances for the estimation of the Fv/Fm of winter wheat. When the regression models of the calibration dataset were used as the validation dataset, WF (704 nm, scale 4) showed the best transferability yielding the highest precision (Rv

^{2}= 0.55) and lowest predicted error (Bias = 0.0050, RMSEv = 0.014, and RRMSEv = 1.81%; Figure 3E and Table 5).

^{2}values from 0.58 to 0.62. The WF (465 nm, scale 4) located in the blue region produced the worst agreement in the calibration dataset and the independent validation dataset as compared with the other four wavelet features. And regardless of which dataset, the best predictive accuracy and the lowest predictive error were found when using the WF (704 nm, scale 4), followed by WF (702 nm, scale 3), and WF (707 nm, scale 7).

## 4. Discussion

#### 4.1. Signature Components of Leaf Chlf Parameters and Their Variation with N Regimes

#### 4.2. Comparing VIs and REPs for Quantitatively Retrieving Fv/Fm and Fv’/Fm’

#### 4.3. Comparison between CWA and Other Spectral Features for Estimating Fv/Fm and Fv’/Fm’

## 5. Conclusions

## Supplementary Materials

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

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**Figure 1.**Variations of Fv/Fm (

**A**–

**D**) and Fv’/Fm’ (

**E**,

**F**,

**H**) for leaves in all treatments under different N levels during the 2014 to 2015 growing season. V1 and V2 represent the wheat cultivar Shengxuan 6, and Yangmai 18. D1 and D2 mean the plant density of 25 and 40 cm. N0, N2, and N4 indicate the N treatments of 0, 150, and 300 kg/ha, respectively. (

**A**and

**E**, the leaf of VID1;

**B**and

**F**, the leaf of V1D2;

**C**and

**G**, the leaf of V2D1;

**D**and

**H**, the leaf of V2D2).

**Figure 2.**Leaf Fv/Fm and Fv’/Fm’ values plotted against five spectral features which performed best in each group: Curvature index (CUR) (

**A**,

**F**), D705/D722 (

**B**,

**G**), wavelet-based REP (WREP-S4) (

**C**,

**H**), CIred edge (

**D**,

**I**), and wavelet feature (WF) (704 nm, scale 4) (

**E**,

**J**). The black solid line is for all data in the growing season. All regressions are statistically significant (p < 0.01). Data points from the booting, heading, anthesis, and filling stages are shown in red (cycle), magenta (triangle), green (square), and blue (cross), respectively.

**Figure 3.**Plots of measured and predicted leaf Fv/Fm and Fv’/Fm’ developed from the regression models using CUR (

**A**,

**F**), D705/D722 (

**B**,

**G**), WREP-S4 (

**C**,

**H**), CIred edge (

**D**,

**I**), and wavelet feature (WF) (704 nm, scale 4) (

**E**,

**J**). Data points from the booting, heading, and filling stages are shown in red (cycle), green (triangle), and blue (cross), respectively.

**Figure 4.**The wavelet feature regions with the top 1% coefficients of determination between wavelet power and wheat leaf Fv/Fm and Fv’/Fm’ for the dataset acquired during the 2014 to 2015 growing season.

Experiment (Exp.) | Season | Rowledge (cm) | Cultivar | N rate (kg/ha) | Samples (Fv/Fm, Fv’/Fm’) | Sampling Date (Date/Phenological Stages/DAS) /Function |
---|---|---|---|---|---|---|

Exp. 1 | 2014 to 2015 | 25, 40 | Shengxuan 6 (V1) Yangmai 18 (V2) | 0 (N0), 150 (N2), 300 (N4) | 36, 35 36, 36 36, 36 33, 33 12, 12 | (9^{th} Apr/booting/164), (16^{th} Apr/heading/172), (26^{th} Apr/anthesis/179), (1^{st} May/filling/187),(7 ^{th} May/7days after filling/193)(calibration) |

Exp. 2 | 2015 to 2016 | 20, 30, 40 | Yangmai 18 (V2) | 0 (N0), 80 (N1), 150 (N2), 220 (N3) | 62, 35 62, 30 62, 30 | (9^{th} Apr/booting/160),(18 ^{th} Apr/heading/167), (5 ^{th} May/filling/187),(validation) |

Abbreviation | Description | Equation | |
---|---|---|---|

dark-adapted condition | Fo | Minimal fluorescence yield | |

Fm | Maximal fluorescence yield | ||

Fv/Fm | Maximal photochemical efficiency of PSII | = (Fm−Fo)/Fm | |

light-adapted condition | Fs | Steady-state fluorescence | |

Fo’ | Initial fluorescence in the presence of NPQ | ||

Fm’ | Maximal fluorescence in the presence of NPQ | ||

Fv’/Fm’ | Photochemical efficiency of PSII in the light | = (Fm’−Fs’)/Fm’ | |

Y(II) | Yield of quantum efficiency | ||

NPQ | Non-photochemical quenching calculated with Fm | = (Fm−Fm’)/Fm’ | |

ETR | Apparent photosynthetic electron transport rate | =Yield × PFD × 0.5 × 0.8 |

Spectral Feature Type | Index Name (Abbreviation) | Index Formulation And Reference | ||
---|---|---|---|---|

VI | ChlF VI | Reflectance ratio VI | Curvature index (CUR) | (R675 × R691)/R683^{2} [12] |

RVI (750,800) | R750/R800 [12] | |||

RVI (685,655) | R685/R655 [12] | |||

RVI (680,630) | R680/R630 [13] | |||

Derivative VI | Double-peak index (DPi) | (D688 × D710)/D697^{2} [14] | ||

D705/D722 | D705/D722 [14] | |||

D730/D706 | D730/D_{7}06 [14] | |||

DP22 | Dλρ/D720 [14] | |||

DPRI | Dλρ/D(λρ + 12 nm) [14] | |||

physiological VI | Photochemical reflectance index (PRI) | (R531 − R570)/(R531 + R570) [33] | ||

Chl VI | MERIS terrestrial chlorophyll index (MTCI) | (R754 − R709)/(R709 − R681) [34] | ||

Red edge chlorophyll index (CIred edge) | (R800/R720) − 1 [35,36] | |||

Structure VI | Normalized difference vegetation index (NDVI) | (R810 − R690)/(R810 + R690) [37] | ||

Enhanced vegetation index (EVI) | 2.5 × (R810 – R690)/(R810 + 2.4 × R690 + 1) [38] | |||

Water VI | Normalized difference water index (NDWI) | (R850 − R1240)/(R850 + R1240) [39] | ||

Water index (WI) | R850/R970 [40] | |||

REP | Linear interpolation (REP_{LI}) | Explained as below, [41] | ||

Polynomial fitting (REP_{PF}) | Explained as below, [42] | |||

Linear extrapolation (REP_{LE}) | Explained as below, [21] | |||

Wavelet-based REP extraction (WREP-S3) | Explained as below, [19] | |||

Wavelet-based REP extraction (WREP-S4) | Explained as below, [17] |

**Table 4.**Relationships among leaf pigments, physiological indices, and ChlF parameters acquired during the 2015 growing season considering all data and all N treatments. The red color indicates significant correlations among Fv/Fm, Fv’/Fm’, and other parameters listed in this table (p < 0.05).

Leaf Pigment | Physiological Indices | Dark-adapted ChlF Parameters | Steady-state ChlF Parameters | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Car | Chl_{ab} | An | gs | Fo | Fm | Fv/Fm | Fo’ | Fs | Fm’ | Fv’/Fm’ | NPQ | Y(Ⅱ) | ETR | |

Car | 1 | |||||||||||||

Chl_{ab} | 0.83^{***} | 1 | ||||||||||||

An | 0.41^{***} | 0.32^{***} | 1 | |||||||||||

gs | 0.32^{***} | 0.26^{**} | 0.75 | 1 | ||||||||||

Fo | 0.0008 | 0.011 | 0.083 | 0.016 | 1 | |||||||||

Fm | 0.20^{***} | 0.35^{***} | 0.12^{*} | 0.087^{*} | 0.54^{***} | 1 | ||||||||

Fv/Fm | 0.51^{***} | 0.60^{***} | 0.55^{***} | 0.32^{***} | 0.017 | 0.54^{***} | 1 | |||||||

Fo’ | 0.11^{**} | 0.11^{*} | 0.042 | 0.048 | 0.051 | 0.15^{*} | 0.13^{*} | 1 | ||||||

Fs | 0.13^{*} | 0.24^{***} | 0.0059 | 0.0097 | 0.34^{**} | 0.53^{***} | 0.20^{**} | 0.46^{***} | 1 | |||||

Fm’ | 0.32^{***} | 0.30^{***} | 0.30^{**} | 0.22^{**} | 0.0014 | 0.20^{**} | 0.52^{***} | 0.54^{***} | 0.31^{**} | 1 | ||||

Fv’/Fm’ | 0.34^{***} | 0.35^{***} | 0.47^{***} | 0.29^{***} | 0.017 | 0.17^{**} | 0.58^{***} | 0.0073 | 0.045 | 0.59^{***} | 1 | |||

NPQ | 0.035 | 0.0043 | 0.13^{*} | 0.078 | 0.22^{**} | 0.20^{**} | 0.085 | 0.12* | 0.014 | 0.31^{**} | 0.25^{**} | 1 | ||

Y(Ⅱ) | 0.063 | 0.016 | 0.39^{**} | 0.24^{**} | 0.0009 | 0.066 | 0.33^{***} | 0.0056 | 0.18^{**} | 0.25^{**} | 0.45^{***} | 0.53^{***} | 1 | |

ETR | 0.063 | 0.016 | 0.40^{**} | 0.24^{**} | 0.37^{***} | 0.065 | 0.15* | 0.0055 | 0.18^{**} | 0.25^{**} | 0.41^{***} | 0.53^{***} | 0.99^{***} | 1 |

**Table 5.**Performance of models for estimating the Fv/Fm based on vegetation indices (VIs), red edge positions (REPs), and wavelet features in calibration and validation.

FvFm | Indices | Calibration | Validation | |||||||
---|---|---|---|---|---|---|---|---|---|---|

Equation | Rc^{2} | RMSEc | Rv^{2} | Bias | RMSEv | RRMSEv | ||||

VI | ChlF VI | Reflectance ratio VI | CUR | y = −0.3963x + 1.196 | 0.66 | 0.017 | 0.50 | 0.0056 | 0.012 | 1.95 |

R680/R630 | y = 0.2209x + 0.5797 | 0.59 | 0.019 | 0.46 | 0.0047 | 0.016 | 1.96 | |||

R685/R655 | y = 0.3209ln (x) + 0.7388 | 0.50 | 0.021 | 0.25 | 0.021 | 0.027 | 3.43 | |||

R750/R800 | y = −2.008x + 2.761 | 0.21 | 0.026 | 0.10 | 0.011 | 0.024 | 2.96 | |||

Derivative VI | D705/D722 | y = −0.06905x + 0.8604 | 0.64 | 0.018 | 0.50 | 0.0037 | 0.015 | 1.87 | ||

D730/D706 | y = 0.07926ln(x) + 0.8109 | 0.62 | 0.018 | 0.42 | 0.0012 | 0.016 | 1.95 | |||

Dλρ/D720 | y = -0.08262ln(x) + 0.7919 | 0.64 | 0.018 | 0.28 | 0.0045 | 0.021 | 2.65 | |||

Dλρ/D(λρ+12 nm) | y = −0.1255x + 0.9238 | 0.43 | 0.022 | 0.03 | 0.012 | 0.028 | 3.49 | |||

DPi | y = −0.4849x + 0.9379 | 0.38 | 0.023 | 0.27 | 0.023 | 0.029 | 3.69 | |||

Physiological VI | PRI | y = 1.009 + 0.7439 | 0.50 | 0.021 | 0.23 | 0.015 | 0.022 | 2.80 | ||

Chl VI | MTCI | y = 0.04633 + 0.7071 | 0.61 | 0.018 | 0.33 | −0.014 | 0.019 | 2.41 | ||

CIred edge | y = 0.1664 + 0.7056 | 0.60 | 0.018 | 0.39 | −0.013 | 0.022 | 2.79 | |||

Structure VI | NDVI | y = 0.2546x + 0.5875 | 0.32 | 0.024 | 0.28 | 0.025 | 0.031 | 3.85 | ||

EVI | y = 0.001713x + 0.7663 | 0.021 | 0.029 | 0.10 | 0.025 | 0.031 | 3.92 | |||

Water VI | NDWI | y = −0.005260x + 0.7703 | 0.0012 | 0.029 | 0.15 | 0.025 | 0.032 | 4.01 | ||

WI | y = 0.7209x + 0.02677 | 0.032 | 0.029 | 0.16 | 0.027 | 0.033 | 4.12 | |||

REP | REP_{LI} | y = 0.005224x − 2.961 | 0.62 | 0.018 | 0.45 | 0.034 | 0.037 | 4.62 | ||

REP_{PF} | y = 0.004448x − 2.383 | 0.68 | 0.016 | 0.43 | −0.027 | 0.030 | 3.78 | |||

REP_{LE} | y = 0.003633x − 1.786 | 0.64 | 0.018 | 0.43 | 0.037 | 0.040 | 5.06 | |||

WREP-S3 | y = 3.155ln(x) − 19.94 | 0.66 | 0.017 | 0.48 | −0.013 | 0.019 | 2.33 | |||

WREP-S4 | y = 0.005376x − 3.052 | 0.67 | 0.017 | 0.51 | 0.0025 | 0.014 | 1.80 | |||

Wavelet feature | WF(702 nm, scale 3) | y = −0.5217x + 0.7406 | 0.70 | 0.016 | 0.48 | 0.0071 | 0.015 | 1.91 | ||

WF(637 nm, scale 3) | y = −3.514x + 0.8036 | 0.69 | 0.016 | 0.37 | 0.0089 | 0.016 | 1.99 | |||

WF(704 nm, scale 4) | y = −0.2112x + 0.7305 | 0.69 | 0.016 | 0.55 | 0.0050 | 0.014 | 1.81 | |||

WF(592 nm, scale 5) | y = −0.4664x + 0.7930 | 0.69 | 0.016 | 0.40 | 0.0089 | 0.016 | 1.98 | |||

WF(575 nm, scale 6) | y = −0.1260x + 0.7571 | 0.68 | 0.016 | 0.41 | 0.0047 | 0.016 | 1.99 |

**Table 6.**Performance of models for estimating the Fv’/Fm’ based on VIs, REPs, and wavelet features in calibration and validation.

Fv‘Fm’ | Indices | Calibration | Validation | |||||||
---|---|---|---|---|---|---|---|---|---|---|

Equation | Rc^{2} | RMSEc | Rv^{2} | Bias | RMSEv | RRMSEv | ||||

VI | ChlF VI | Reflectance ratio VI | CUR | y = -0.4089x + 1.119 | 0.55 | 0.022 | 0.38 | 0.0072 | 0.030 | 4.28 |

R680/R630 | y = 0.2097x + 0.4987 | 0.42 | 0.025 | 0.35 | 0.0074 | 0.030 | 4.28 | |||

R685/R655 | y = 0.2811x + 0.3689 | 0.37 | 0.026 | 0.18 | 0.022 | 0.039 | 5.51 | |||

R750/R800 | y = −2.928x + 3.583 | 0.36 | 0.026 | 0.24 | 0.0050 | 0.031 | 4.40 | |||

Derivative VI | D705/D722 | y = −0.07581x + 0.7785 | 0.60 | 0.021 | 0.40 | 0.0031 | 0.028 | 3.96 | ||

D730/D706 | y = 0.1405x + 0.5920 | 0.54 | 0.022 | 0.36 | 0.0034 | 0.030 | 3.96 | |||

Dλρ/D720 | y = −0.04609x + 0.7422 | 0.52 | 0.023 | 0.26 | 0.0087 | 0.033 | 4.61 | |||

Dλρ/D(λρ+12 nm) | y = −0.1332x + 0.8426 | 0.38 | 0.026 | 0.09 | 0.019 | 0.045 | 6.41 | |||

DPi | y = −0.4247x + 0.8264 | 0.22 | 0.029 | 0.02 | 0.026 | 0.044 | 6.22 | |||

Physiological VI | PRI | y = 1.084x + 0.6512 | 0.45 | 0.024 | 0.08 | 0.010 | 0.037 | 5.21 | ||

Chl VI | MTCI | y = 0.04936x + 0.6122 | 0.55 | 0.022 | 0.3 | −0.014 | 0.029 | 4.17 | ||

CIred edge | y = 0.1821x + 0.6088 | 0.57 | 0.022 | 0.32 | −0.011 | 0.030 | 4.07 | |||

Structure VI | NDVI | y = 0.2999x + 0.4644 | 0.35 | 0.026 | 0.11 | 0.0025 | 0.041 | 5.88 | ||

EVI | y = −0.000004619x + 0.6792 | 0.0000012 | 0.033 | 0.05 | 0.022 | 0.042 | 5.92 | |||

Water VI | NDWI | y = 0.008163x + 0.6797 | 0.0022 | 0.033 | 0.06 | 0.022 | 0.041 | 5.82 | ||

WI | y = 0.8144x − 0.1604 | 0.032 | 0.032 | 0.03 | 0.025 | 0.042 | 6.00 | |||

REP | REP_{LI} | y = 0.005767x − 3.439 | 0.60 | 0.021 | 0.38 | 0.0069 | 0.029 | 4.15 | ||

REP_{PF} | y = 0.004753x − 2.690 | 0.61 | 0.021 | 0.40 | 0.0050 | 0.028 | 3.94 | |||

REP_{LE} | y = 0.003943x − 2.095 | 0.59 | 0.021 | 0.41 | 0.0035 | 0.027 | 3.84 | |||

WREP-S3 | y = 0.004762x − 2.693 | 0.59 | 0.021 | 0.43 | 0.0016 | 0.027 | 3.76 | |||

WREP-S4 | y = 0.005792x – 3.438 | 0.61 | 0.021 | 0.43 | 0.0028 | 0.026 | 3.74 | |||

Wavelet feature | WF(630 nm, scale 3) | y = −5.381x + 0.7119 | 0.59 | 0.021 | 0.24 | −0.0034 | 0.031 | 4.41 | ||

WF(702 nm, scale 3) | y = −0.5553x + 0.6480 | 0.62 | 0.020 | 0.37 | −0.0009 | 0.028 | 4.03 | |||

WF(465 nm, scale 4) | y = 4.335x + 0.7136 | 0.58 | 0.021 | 0.31 | −0.0112 | 0.031 | 4.46 | |||

WF(704 nm, scale 4) | y = −0.2197x + 0.6440 | 0.62 | 0.020 | 0.43 | −0.0028 | 0.027 | 3.79 | |||

WF(707 nm, scale 7) | y = −0.1804x + 0.7008 | 0.61 | 0.020 | 0.37 | −0.0012 | 0.028 | 3.94 |

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Jia, M.; Li, D.; Colombo, R.; Wang, Y.; Wang, X.; Cheng, T.; Zhu, Y.; Yao, X.; Xu, C.; Ouer, G.; Li, H.; Zhang, C. Quantifying Chlorophyll Fluorescence Parameters from Hyperspectral Reflectance at the Leaf Scale under Various Nitrogen Treatment Regimes in Winter Wheat. *Remote Sens.* **2019**, *11*, 2838.
https://doi.org/10.3390/rs11232838

**AMA Style**

Jia M, Li D, Colombo R, Wang Y, Wang X, Cheng T, Zhu Y, Yao X, Xu C, Ouer G, Li H, Zhang C. Quantifying Chlorophyll Fluorescence Parameters from Hyperspectral Reflectance at the Leaf Scale under Various Nitrogen Treatment Regimes in Winter Wheat. *Remote Sensing*. 2019; 11(23):2838.
https://doi.org/10.3390/rs11232838

**Chicago/Turabian Style**

Jia, Min, Dong Li, Roberto Colombo, Ying Wang, Xue Wang, Tao Cheng, Yan Zhu, Xia Yao, Changjun Xu, Geli Ouer, Hongying Li, and Chaokun Zhang. 2019. "Quantifying Chlorophyll Fluorescence Parameters from Hyperspectral Reflectance at the Leaf Scale under Various Nitrogen Treatment Regimes in Winter Wheat" *Remote Sensing* 11, no. 23: 2838.
https://doi.org/10.3390/rs11232838