# Validating and Developing Hyperspectral Indices for Tracing Leaf Chlorophyll Fluorescence Parameters under Varying Light Conditions

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## Abstract

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_{max}), the cumulative quantum yield of photochemistry (ΦP), and the fraction of open reaction centers in photosystem II (qL) of sunlit leaves were significantly higher than those of shaded leaves, while the cumulative quantum yield of regulated thermal dissipation (ΦN) and fluorescence (ΦF) of shaded leaves was higher than that of sunlit leaves. Efficient tracing of ChlFa parameters could not be achieved from previously published spectral indices. In comparison, all ChlFa parameters were well quantified in shaded leaves when using novel hyperspectral indices, although the hyperspectral indices for tracing the non-photochemical quenching (NPQ) and ΦF were not stable, especially for sunlit leaves. Our findings justify the use of hyperspectral indices as a practical approach to estimating ChlFa parameters. However, caution should be used when using spectral indices to track ChlFa parameters based on the differences in sunlit and shaded leaves.

## 1. Introduction

_{max}), non-photochemical quenching (NPQ), fraction of open reaction centers in photosystem II (qL), cumulative quantum yield of photochemistry (ΦP), regulated thermal dissipation (ΦN), and fluorescence (ΦF) can be calculated. All of these values can provide information about the efficiency of photochemistry and thermal dissipation in PSII [9]. Unfortunately, because of series inherent limitations, PAM technology cannot be applied to large-area satellite remote sensing monitoring [10,11]. On the other hand, the recent boom in passive solar-induced chlorophyll fluorescence (SIF) exploits the Fraunhofer line to decouple fluorescence from the reflected signals, using the infilling of the O

_{2}A and O

_{2}B bands at the 600–800 nm window [12] and providing an alternative indicator of large-scale photosynthetic activity to study global patterns and dynamics of terrestrial vegetation productivity [5,13,14]. However, this radiance-based approach relies heavily on high-resolution spectral sensors, and low resolution can result in the loss of absorption features [15]. Furthermore, the complete process of energy capture, conversion, and dissipation in photosynthetic systems (i.e., NPQ and qL) could not be accurately estimated by this radiance-based approach [16].

_{max}and ΦP of Suaeda salsa under salinity stress. Several studies have further attempted different spectral transformation methods for the quantification of ChlFa parameters, e.g., Zarco-Tejada et al. [20] proposed that derivative indices can track fluorescence more efficiently and minimize other confusing effects, while the first-derivative (first) method for spectral reflectance has been applied to estimate PSII

_{max}and ΦP of Suaeda salsa under water and salt conditions [21]. Furthermore, the standard normal variate transformation (SNV) and multiplicative scatter correction (MSC) methods have been successfully applied to highlight the correlation between spectral data and PSII

_{max}in rice [22]. The results of these studies demonstrate the potential of hyperspectral indices coupled with different spectral transformation methods for monitoring and detecting changes in ChlFa parameters.

_{max}, NPQ, qL, ΦP, ΦN, and ΦF were investigated. The leaf reflectance spectra were collected simultaneously. The objectives of this study were (1) to differentiate the variation in ChlFa parameters between sunlit and shaded leaves, (2) to verify the performance of published indices for tracking ChlFa parameters, and (3) to develop new spectral indices for detecting ChlFa parameters while assessing their feasibility.

## 2. Materials and Methods

#### 2.1. Data Acquisition

_{o}, F

_{m}, F

_{s}, F′

_{o}, and F′

_{m}). Among these, F

_{o}and F

_{m}represent the minimum initial and maximum fluorescence under dark adaptation, respectively. F

_{s}and F′

_{m}denote the minimum and maximum fluorescence under light adaptation, respectively, and F

_{s}denotes the steady-state fluorescence. In light adaptation fluorescence measurements, we controlled the photosynthetic photon flux density (PPFD) at 200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800, and 2000 μmol m

^{−2}s

^{−1}. The initial light compositions of red and blue were 90% and 10%, respectively. We gradually increased the proportion of blue light and reduced the proportion of red light at intervals of 10%. Leaves had to adapt to specific light intensity and light quality (about 20–30 min), and the fluorescence parameters were recorded based on the stability of the fluorescence curve. The blue actinic light output of the LI-6800-01A is only rated to go up to 1000 μmol m

^{−2}s

^{−1}; thus, we screened for PPFD equal to or less than 1200 μmol m

^{−2}s

^{−1}and considered blue actinic light equal to or less than 1000 μmol m

^{−2}s

^{−1}. Based on the measured chlorophyll fluorescence parameters, PSII

_{max}, qL, and NPQ can be derived by Equations (1)–(3):

_{P}, K

_{F}, K

_{D}, and K

_{N}were calculated using Equations (9)–(12):

_{P}and K

_{N}are the rate of photochemistry and the rate of energy-dependent heat dissipation, respectively. K

_{D}denotes the rate of constitutive thermal dissipation, varying with temperature and attaining a maximum value of 0.87. K

_{F}is a constant and represents the rate of fluorescence.

#### 2.2. Data Preprocessing

#### 2.3. Data Analyses

^{2}), root mean square error (RMSE), and the ratio of performance to deviation (RPD) were used to evaluate the performance of prediction. The predicted models can be classified as good (RPD > 2.0), fair (1.4 ≤ RPD ≤ 2.0), or unreliable (RPD < 1.4) [48].

## 3. Results

#### 3.1. Variation of ChlFa Parameters in Sunlit and Shaded Leaves

_{max}, qL, ΦP, ΦN and ΦF were revealed by one-way ANOVA (Table 1, p < 0.01). The differences in mean ± SE of ChlFa parameters between sunlit and shaded leaves also were compared by t-test (Figure 3). The PSII

_{max}, qL, and ΦP of sunlit leaves (0.810 ± 0.001, 0.470 ± 0.015, and 0.386 ± 0.011) were significantly higher (p ≤ 0.0001) than those of shaded leaves (0.804 ± 0.001, 0.330 ± 0.014, and 0.298 ± 0.009). Significantly lower (p ≤ 0.05) values of ΦN and ΦF were found in sunlit leaves (0.413 ± 0.011 and 0.011 ± 0.0001) compared with shaded leaves (0.462 ± 0.008 and 0.013 ± 0.002). However, there were no significant differences between groups for NPQ (p > 0.05).

#### 3.2. Performance of Reported Spectral Indices

^{2}= 0.05, RMSE = 0.01), mSR705 (R

^{2}= 0.11, RMSE = 0.01), and EVI (R

^{2}= 0.07, RMSE = 0.01) for tracing the PSII

_{max}of all leaves, sunlit leaves, and shaded leaves, respectively. For estimating NPQ, PSRI had the best performance in all leaves (R

^{2}= 0.03, RMSE = 0.76) and sunlit leaves (R

^{2}= 0.12, RMSE = 0.81), and ARI2 had the highest RPD in shaded leaves (R

^{2}= 0.13, RMSE = 0.59). The best indices for tracing qL were RSI (R

^{2}= 0.21, RMSE = 0.20), RGI (R

^{2}= 0.25, RMSE = 0.20), and CRI1 (R

^{2}= 0.73, RMSE = 0.10) in all leaves, sunlit leaves, and shaded leaves, respectively. The RSI (R

^{2}= 0.18, RMSE = 0.14), EVI (R

^{2}= 0.23, RMSE = 0.14), and CRI2 (R

^{2}= 0.70, RMSE = 0.07) best estimated ΦP in all leaves, sunlit leaves, and shaded leaves, respectively. The RSI (R

^{2}= 0.11, RMSE = 0.14), ARI2 (R

^{2}= 0.19, RMSE = 0.14), and CRI2 (R

^{2}= 0.55, RMSE = 0.08) were the best indices to evaluate the ΦN of all leaves, sunlit leaves, and shaded leaves, respectively. In terms of the ΦF, the RGI (R

^{2}= 0.19, RMSE = 0.002), PRI (R

^{2}= 0.24, RMSE = 0.002), and CRI1 (R

^{2}= 0.16, RMSE = 0.003) had the best performance in all leaves, sunlit leaves, and shaded leaves, respectively.

#### 3.3. Developing New Indices to Evaluate ChlFa Parameters

_{max}, the ND (R

^{2}= 0.29, RMSE = 0.01), DDn (R

^{2}= 0.45, RMSE = 0.01), and mND (R

^{2}= 0.60, RMSE = 0.01) were calculated as the log, first-order derivative, and EMSC reflectance, which were the best indices in all leaves, sunlit leaves, and shaded leaves, respectively. In terms of first-order derivative spectra, the D (R

^{2}= 0.33, RMSE = 0.63), DDn (R

^{2}= 0.43, RMSE = 0.65), and ND (R

^{2}= 0.52, RMSE = 0.44) were the best compared to other transformed indices to quantify NPQ in all leaves, sunlit leaves, and shaded leaves, respectively. The ND (R

^{2}= 0.60, RMSE = 0.14) and mSR1 (R

^{2}= 0.80, RMSE = 0.08) had the highest accuracy to track qL after SNV transformation in sunlit and shaded leaves. The performance of mSR1 (R

^{2}= 0.49, RMSE = 0.16) was the highest when applying original reflectance to estimate qL in all leaves. The DDn had the highest RPD to estimate the ΦP in all leaves (R

^{2}= 0.48, RMSE = 0.11) and sunlit leaves (R

^{2}= 0.53, RMSE = 0.11) under the first-order derivative transformation, and ND (R

^{2}= 0.80, RMSE = 0.08) was the best index in shaded leaves under original reflectance. For estimating the ΦN, ID (R

^{2}= 0.44, RMSE = 0.11) and D (R

^{2}= 0.52, RMSE = 0.11) had the best performance in all leaves and sunlit leaves under the first-order derivative transformation. Moreover, D (R

^{2}= 0.66, RMSE = 0.07) can be used in shaded leaves under original reflectance. The transformed ND (R

^{2}= 0.37, RMSE = 0.002) and D (R

^{2}= 0.59, RMSE = 0.002) of the first-order derivative can be used to track ΦN for all leaves and shaded leaves, while the best index for tracing sunlit leaves was mND (R

^{2}= 0.44, RMSE = 0.001) under log transformation.

^{2}, RMSE, and RPD between measured ChlFa and predicted ChlFa parameters from new indices across different light intensities and light qualities in all leaves, sunlit leaves, and shaded leaves. Overall, all ChlFa parameters retrieved from our newly developed indices captured more than 50% of the total variance in measured ChlFa parameters for shaded leaves. Moreover, satisfactory performance was observed in qL (R

^{2}= 0.80, RMSE = 0.08) and ΦP (R

^{2}= 0.76, RMSE = 0.06), in which the RPD was greater than 2. In addition, the new indices had fair performance for the prediction of qL (R

^{2}= 0.60, RMSE = 0.14), ΦP (R

^{2}= 0.53, RMSE = 0.11), and ΦN (R

^{2}= 0.51, RMSE = 0.11) in sunlit leaves (RPD > 1.4). However, the new indices were unreliable for ChlFa parameters in all leaves (RPD < 1.4).

## 4. Discussion

#### 4.1. Difference of Acclimation in ChlFa Parameters between Sunlit and Shaded Leaves

_{max}, ΦP, and qL of sunlit leaves were significantly higher than those in shaded leaves, while the ΦN was higher in shaded leaves compared to sunlit leaves. Previous studies have demonstrated the difference in ChlFa parameters between sunlit leaves and shaded leaves for varying species [49,50,51]. Generally, plants have evolved adaptive mechanisms to cope with various light conditions and achieve optimum photosynthetic efficiency [52,53,54]. Differences in ChlFa parameters between sunlit and shaded leaves result from taxon-specific adaptation mechanisms and photosynthetic apparatus. The value of initial fluorescence is related to the oxidized QA (primary quinone) centers. Dietz et al. [55] pointed out that the QA was higher in sunlit leaves than in shade leaves of beech, causing a higher efficiency in chemical energy conversion for the sunlit leaves. In shaded leaves, photons are channeled into the dissipation pathways available for driving photochemistry, increasing heat dissipation [56]. In our study, the up-regulation of ΦN and down-regulation of ΦP in shaded leaves demonstrated adaptive photoprotection of the photosynthetic apparatus and lower PSII connectivity.

#### 4.2. New Spectral Indices Reinforce the Potential for Tracing the ChlFa Parameters Compared with Reported Spectral Indices

^{2}values ranging from 0.52 to 0.80 and RPD values ranging from 1.45 to 2.24 (Figure 6). Previous studies have substantiated the suitability of spectral indices derived from the visible light region (400 to 700 nm) and red edge region (680 to 780 nm) of the first-derivative spectrum for the monitoring of chlorophyll fluorescence parameters [69,70,71]. This is attributed to the fact that the visible light and red edge region are characterized by a significant absorption of red light by chlorophyll, coupled with multiple scattering events within mesophyll cells. Nevertheless, Dobrowski et al. [72] explored wavelengths beyond the absorption range of chlorophyll and carotenoid pigments to achieve more efficient tracking of changes in ChlFa parameters than what can be achieved with traditional chlorophyll-related wavelengths. In our case, the selected bands of indices for ΦP detection were at 1610 and 1660 nm, and for qL detection they were at 1670, 1620, and 1720 nm in shaded leaves. This aligns with the findings of El-Hendawy et al. [73], who suggested that alterations in moisture levels directly and indirectly impact the functional state of photosynthetic machinery. Consequently, the pronounced water absorption band situated in the shortwave infrared (SWIR) region is deemed valuable for the estimation of ChlFa parameters. In addition, previous studies have demonstrated the veracity of strong light absorbance by chlorophyll content at SWIR bands such as 1694, 1768, and 1773 nm [74,75,76]. It is well known that the chlorophyll content is closely related to the intensity of chlorophyll fluorescence and affects the light-harvesting complex and chloroplast structure [57,77]. Zhuang et al. [78] showed that chlorophyll content governed the intensity of fluorescence emission by affecting the photochemical process in cucumbers. Thus, the major influence of chlorophyll content on the photochemical process also confirms the accuracy of our novel spectral indices.

^{2}= 0.60, RPD = 1.59), ΦP (R

^{2}= 0.53, RPD = 1.47), and ΦN (R

^{2}= 0.51, RPD = 1.43) in sunlit leaves (Figure 6). The probable reason for this is that we measured the NPQ and ΦF under varying light conditions with the fast-regulated processes of the photosynthetic apparatus. In comparison, the spectral reflectance was taken under the halogen lamp with the leaf clip. For sunlit leaves in our study, NPQ and ΦF depend not only on structure and pigment content, but also on light conditions. Hallik, Niinemets, and Kull [26] illustrated that sunlit leaves had a higher capacity to tolerate light fluctuations and fast-regulated flexible heat dissipation compared with shaded leaves. The observed poor prediction of NPQ and ΦF may be because sunlit leaves have a higher sensitivity to short-term changes in the state of the xanthophyll cycle under varying light conditions. Thus, new spectral indices may not accurately capture the dynamics of NPQ and ΦF in sunlit leaves.

#### 4.3. Uncertainty and Perspective

## 5. Conclusions

## Supplementary Materials

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

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**Figure 2.**Different spectral reflectance transformations from original (OR) (

**a**), first-order (first) (

**b**), logarithm (Log) (

**c**), standard normal variate transformation (SNV) (

**d**), multiplicative scatter correction (MSC) (

**e**), and extended multiplicative scatter correction (EMSC) (

**f**) for all leaves and sunlit and shaded leaves; color coding is used for different leaf groups.

**Figure 3.**Comparing the maximum quantum efficiency of photosystem II (PSII

_{max}) (

**a**), non-photochemical quenching (NPQ) (

**b**), the fraction of open reaction centers in photosystem II (qL) (

**c**), the cumulative quantum yield of photochemistry (ΦP) (

**d**), regulated thermal dissipation (ΦN) (

**e**), and fluorescence (ΦF) (

**f**) for all leaves and sunlit and shaded leaves; in the boxplot, the black lines and white diamonds are the median lines and mean points, respectively; number and n represent the mean value and the sample size in each group; color coding is used for different leaf groups; asterisks represent significant differences of t-test (NS. p > 0.05, * p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001). Descriptive statistics of t-test can be viewed in Table S3.

**Figure 4.**Performance of published indices for estimating ChlFa parameters among all leaves (green) and sunlit (red) and shaded leaves (blue); RPD is the ratio of performance to deviation; the red box is the best index based on RPD evaluation; all RPD values are presented in Tables S4–S6.

**Figure 5.**Performance of different indices for the estimation of the maximum quantum efficiency of photosystem II (PSII

_{max}) (

**a**), non-photochemical quenching (NPQ) (

**b**), the fraction of open reaction centers in photosystem II (qL) (

**c**), the cumulative quantum yield of photochemistry (ΦP) (

**d**), regulated thermal dissipation (ΦN) (

**e**), and fluorescence (ΦF) (

**f**) applying various spectral transformations among all leaves, sunlit leaves, and shaded leaves; color coding is used for different leaf groups, and shape coding is used for different index types; RPD is the ratio of performance to deviation; the label is the best index based on RPD evaluation for different leaf groups. The wavelength information of the determined new spectral indices can be viewed in Table S7.

**Figure 6.**Measurements and predictions of the maximum quantum efficiency of photosystem II (PSIImax) (

**a**), non-photochemical quenching (NPQ) (

**b**), the fraction of open reaction centers in photosystem II (qL) (

**c**), the cumulative quantum yield of photochemistry (ΦP) (

**d**), regulated thermal dissipation (ΦN) (

**e**), and fluorescence (ΦF) (

**f**) using newly developed indices in all leaves, sunlit leaves, and shaded leaves; color coding is used for different leaf groups; the black dashed line represents the 1:1 line; R

^{2}is the coefficient of determination, RMSE is the root mean square error, and RPD is the ratio of performance to deviation.

Dependent Variable | Df | F Value | p Value |
---|---|---|---|

PSII_{max} | 2 | 14.46 | <0.001 |

NPQ | 2 | 0.72 | 0.49 |

qL | 2 | 20.73 | <0.001 |

ΦP | 2 | 17.11 | <0.001 |

ΦN | 2 | 6.07 | <0.01 |

ΦF | 2 | 33.88 | <0.001 |

**Table 2.**Performance of the best-performing published index for estimating the ChlFa parameters among all leaves and sunlit and shaded leaves. Asterisks indicate significance levels (***, p < 0.001).

Variable | Leaf Group | Index Name | R^{2} | RMSE | AIC | RPD |
---|---|---|---|---|---|---|

All leaves | PRI | 0.05 *** | 0.01 | 6.07 | 1.03 | |

PSII_{max} | Sunlit | mSR705 | 0.11 *** | 0.01 | 6.21 | 1.06 |

Shaded | EVI | 0.07 *** | 0.01 | 6.16 | 1.04 | |

All leaves | PSRI | 0.03 *** | 0.76 | 2.30 | 1.02 | |

NPQ | Sunlit | PSRI | 0.12 *** | 0.81 | 2.42 | 1.07 |

Shaded | ARI2 | 0.13 *** | 0.59 | 1.81 | 1.07 | |

All leaves | RSI | 0.21 *** | 0.20 | 0.41 | 1.13 | |

qL | Sunlit | RGI | 0.25 *** | 0.20 | 0.41 | 1.16 |

Shaded | CRI1 | 0.73 *** | 0.10 | 1.85 | 1.94 | |

All leaves | RSI | 0.18 *** | 0.14 | 1.12 | 1.11 | |

ΦP | Sunlit | EVI | 0.23 *** | 0.14 | 1.06 | 1.14 |

Shaded | CRI2 | 0.70 *** | 0.07 | 2.55 | 1.82 | |

All leaves | RSI | 0.11 *** | 0.14 | 1.16 | 1.06 | |

ΦN | Sunlit | ARI2 | 0.19 *** | 0.14 | 1.04 | 1.12 |

Shaded | CRI2 | 0.55 *** | 0.08 | 2.34 | 1.50 | |

All leaves | RGI | 0.19 *** | 0.002 | 9.31 | 1.11 | |

ΦF | Sunlit | PRI | 0.24 *** | 0.002 | 9.93 | 1.15 |

Shaded | CRI1 | 0.16 *** | 0.003 | 9.09 | 1.10 |

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## Share and Cite

**MDPI and ACS Style**

Zhuang, J.; Wang, Q.; Song, G.; Jin, J.
Validating and Developing Hyperspectral Indices for Tracing Leaf Chlorophyll Fluorescence Parameters under Varying Light Conditions. *Remote Sens.* **2023**, *15*, 4890.
https://doi.org/10.3390/rs15194890

**AMA Style**

Zhuang J, Wang Q, Song G, Jin J.
Validating and Developing Hyperspectral Indices for Tracing Leaf Chlorophyll Fluorescence Parameters under Varying Light Conditions. *Remote Sensing*. 2023; 15(19):4890.
https://doi.org/10.3390/rs15194890

**Chicago/Turabian Style**

Zhuang, Jie, Quan Wang, Guangman Song, and Jia Jin.
2023. "Validating and Developing Hyperspectral Indices for Tracing Leaf Chlorophyll Fluorescence Parameters under Varying Light Conditions" *Remote Sensing* 15, no. 19: 4890.
https://doi.org/10.3390/rs15194890