# Estimating Peanut Leaf Chlorophyll Content with Dorsiventral Leaf Adjusted Indices: Minimizing the Impact of Spectral Differences between Adaxial and Abaxial Leaf Surfaces

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## Abstract

**:**

^{2}

_{cv}of 0.91 and RMSE

_{cv}of 3.53 μg/cm

^{2}. The DLARI formula provided the best performing indices, which were $\left({\mathrm{R}}_{735}-{\mathrm{R}}_{753}\right)/\left({\mathrm{R}}_{715}-{\mathrm{R}}_{819}\right)$ for estimating LCC from the adaxial surface (R

^{2}

_{cv}= 0.96, RMSE

_{cv}= 2.37 μg/cm

^{2}) and $\left({\mathrm{R}}_{732}-{\mathrm{R}}_{754}\right)/\left({\mathrm{R}}_{724}-{\mathrm{R}}_{773}\right)$ for estimating LCC from reflectance of both sides (R

^{2}

_{cv}= 0.94, RMSE

_{cv}= 2.81 μg/cm

^{2}). A comparison with published vegetation indices demonstrated that the published indices yielded reliable estimates of LCC from the adaxial surface but performed worse than DLARIs when both leaf sides were considered. This paper concludes that the DLARI is the most promising approach to estimate peanut LCC.

## 1. Introduction

_{nir}R

_{red-edge}− 1), which is an effective LCC predictor for maple, chestnut, wild vine, and beech leaves.

## 2. Materials and Methods

#### 2.1. Data Collection

^{®}3 portable spectroradiometer (Analytical Spectral Devices, Boulder, CO, USA) and a contact probe, equipped with an internal halogen source and directly attached to the leaf surface using a leaf clip accessory. The spectrometer can collect data in the 350–2500 nm spectral region, with a sampling interval of 1.4 nm in the 350–1000 nm wavelength range and 2 nm in the 1000–2500 nm wavelength range. The average of 10 separate measurements from each sample was recorded. To reduce errors associated with variations in illumination geometry, the contact probe was pressed to the leaf surface, which was illuminated by the internal light source, ensuring a consistent illumination geometry.

^{2}) was then calculated according to the equations provided by Arnon [32]. In total, measurements of LCC and reflectance were collected for 84 leaves. The LCC values ranged from 21.50 to 70.55 μg/cm

^{2}with a mean value of 40.78 μg/cm

^{2}and a standard deviation of 11.68 μg/cm

^{2}.

#### 2.2. Data Analysis

#### 2.2.1. Construction of a Dorsiventral Leaf Adjusted Ratio Index

_{s}is the reflectance of the leaf surface, S represents scattering effects of the leaf mesophyll structure, and k

_{i}and C

_{i}are the extinction coefficient and chlorophyll concentration, respectively. R

_{s}and S are thought to be the main factors introducing variability between adaxial and abaxial leaf reflectance, as they depend on the differences in the leaf surface and internal mesophyll structure Datt [12]. Based on this model and the principle that there is no absorption at 850 nm by any leaf pigment ($\mathrm{i}.\mathrm{e}.,\text{}{\mathsf{\Sigma}k}_{\mathrm{i}}{C}_{\mathrm{i}}=0$), Datt [12] proposed an index of $\left({\mathrm{R}}_{850}-{\mathrm{R}}_{710}\right)/\left({\mathrm{R}}_{850}-{\mathrm{R}}_{680}\right)$. The index removed R

_{s}and S. Lu et al. [21] extended the wavelengths in the Datt’s index to 400–1000 nm, which can also remove R

_{s}and S. The formula of MDATT is:

_{chl}is the chlorophyll content and k

_{chl(λ1)}, k

_{chl(λ2)}, and k

_{chl(λ3)}are the specific absorption coefficients for Chl at wavelengths λ

_{1}, λ

_{2}, and λ

_{3}, respectively.

_{s}and S. The efficiency of incorporating an additional band is evaluated in the following sections.

#### 2.2.2. Published Vegetation Indices

#### 2.2.3. Model Calibration and Validation

^{2}) for each dataset were selected as the optimal indices and used to model LCC. Relationships between the measured LCC and indices were established using empirical regression analysis. The form of fitting functions (e.g., linear, exponential, logarithmic) relating the indices to LCC appeared to have a marginal impact when compared to the impact of band selection [24]. Therefore, we restricted the fitting method to ordinary least-squares linear regression.

^{2}and root mean square error (RMSE) with respect to the biochemically measured LCC. In order to avoid dependence on a single random partitioning of the datasets and guarantee that all samples were used for both training and validation, a repeated 10 fold cross-validation was used to evaluate the performance of each index [40]. The dataset was split into 10 consecutive folds, and each fold was then used once for validation while the remaining 9 folds formed the training dataset. This process was repeated 50 times, and combined R

^{2}

_{cv}and RMSE

_{cv}values were calculated as the mean of those from each repetition.

## 3. Results

#### 3.1. Spectral Differences Between Adaxial and Abaxial Surfaces

#### 3.2. Relationships Between Optimal MDATT Indices and Peanut LCC

^{2}was determined by fixing λ

_{2}and λ

_{3}as single values and changing λ

_{1}from 400 nm to 1000 nm. For the sake of concise display, only R

^{2}values greater than 0.8 were considered and they are shown in Figure 5, where the x-axis represents λ

_{3}and the y-axis λ

_{2}. From this figure, robust wavelength regions for each band of the MDATT can be identified. For the adaxial dataset, the most sensitive region (red color in Figure 5a) ranged from 700 nm to 800 nm for λ

_{2}, 400 nm to 800 nm for λ

_{3}, and 650 nm to 750 nm for λ

_{1}(Figure 5d). The robust regions for the abaxial dataset were similar to those for the adaxial dataset (Figure 5b,e), but the most sensitive area (red color in Figure 5b) was reduced, and covered approximately 750 nm for λ

_{2}, 730 nm for λ

_{3}, and 690–750 nm for λ

_{1}. For the bifacial dataset (Figure 5c,e), the sensitive wavelength regions were further reduced. The R

^{2}values greater than 0.88 were demonstrated when λ

_{2}and λ

_{3}were between 700 nm to 750 nm and λ

_{1}was between 660 nm and 710 nm.

^{2}values, three optimal MDATT indices for each dataset were determined and are presented in Table 2. The wavelengths λ

_{1}, λ

_{2}, and λ

_{3}of the best performing MDATT index for all three datasets were concentrated in the region of 701 to 747 nm. For the adaxial surface, the index incorporating reflectance values at 701 nm, 742 nm, and 740 nm demonstrated an R

^{2}of 0.95, whilst for the abaxial dataset, the best index incorporated reflectance values at 718 nm, 747 nm, and 720 nm (R

^{2}= 0.94). In the case of the bifacial dataset, the best index incorporated reflectance values at 723 nm, 738 nm, and 722 nm, reaching an R

^{2}of 0.91. The optimal indices for the adaxial and abaxial surfaces employed at least one wavelength that was highly correlated to LCC (r < −0.60), i.e., 701 nm, 718 nm, and 720 nm (Figure 4). The reflectance values at 722 nm and 723 nm, which were used by the optimal index for the bifacial dataset, demonstrated the minimum differences between adaxial and abaxial surfaces (Figure 2b).

^{2}

_{cv}= 0.95, RMSE

_{cv}= 2.52) (Figure 6a,d), followed by the index $\left({\mathrm{R}}_{718}-{\mathrm{R}}_{747}\right)/\left({\mathrm{R}}_{718}-{\mathrm{R}}_{720}\right)$ for the abaxial surface (R

^{2}

_{cv}= 0.94, RMSE

_{cv}= 2.69) (Figure 6b,e). Both performed better than the index $\left({\mathrm{R}}_{723}-{\mathrm{R}}_{738}\right)/\left({\mathrm{R}}_{723}-{\mathrm{R}}_{722}\right)$ for the bifacial dataset (R

^{2}

_{cv}= 0.91, RMSE

_{cv}= 3.53) (Figure 5c,f). The observed and predicted values fell close to the 1:1 line, indicating that the optimized MDATT indices were stable predictors of LCC. The optimal index for the bifacial dataset demonstrated a lower correlation with LCC and was not as accurate an LCC predicator as the other two indices.

^{2}

_{cv}= 0.87, RMSE

_{cv}= 4.14). In light of the errors, it was necessary to consider the influence of spectral differences between adaxial and abaxial sides when estimating LCC from reflectance mixed by the two sides.

#### 3.3. Relationships Between DLARI and Peanut LCC

#### 3.3.1. Performance of DLARIs Incorporating Wavelengths Between 660 and 750 nm

^{2}, optimal DLARIs were established for each dataset (Table 4). The results demonstrated that the wavelengths used in the DLARIs were similar to those used in the MDATTs (Table 2). When compared with the MDATTs for the adaxial surface, the DLARI did not improve retrieval accuracy (RMSE

_{cv}= 2.53), the DLARI for the abaxial surface demonstrated a marginal advantage over the abaxial MDATT (RMSE

_{cv}= 2.62). In the case of the bifacial dataset, the DLARI achieved some improvement over the bifacial MDATT (RMSE

_{cv}= 3.34).

#### 3.3.2. Performance of DLARIs Incorporating Wavelengths Over 750 nm

_{4}was at the limit of the considered region (i.e., 750 nm), indicating that relevant information might be contained at longer wavelengths. When evaluated, DLARIs incorporating longer wavelengths (around 820 nm) achieved higher retrieval accuracies than those described in Section 3.3.1. (Table 5). It showed that for the three datasets, the optimal wavelengths of λ

_{1}and λ

_{3}moved to approximately 730 nm and 720 nm, where the differences in adaxial and abaxial reflectance were less than 5% (Figure 2). The optimal location of λ

_{2}moved to the red-edge shoulder, which means less sensitivity to leaf structure [42], while the optimal location of λ

_{4}moved to the NIR, where there is less absorption by leaf pigments [12]. The new adaxial DLARI and abaxial DLARI demonstrated advantages over the DLARIs derived from reflectance over 660 and 750 nm (RMSE

_{cv}= 2.37; RMSE

_{cv}= 2.58). The new bifacial DLARI not only substantially improved the retrieval accuracy (RMSE

_{cv}= 2.81), but also enhanced its sensitivity to LCC (R

^{2}

_{cv}= 0.94).

^{2}

_{cv}= 0.96, RMSE

_{cv}= 2.37; R

^{2}

_{cv}= 0.95, RMSE

_{cv}= 2.58) than the MDATT indices (R

^{2}

_{cv}= 0.95, RMSE

_{cv}= 2.52; R

^{2}

_{cv}= 0.94, RMSE

_{cv}= 2.69). For the bifacial dataset (Figure 7e–f), the index $\left({\mathrm{R}}_{732}-{\mathrm{R}}_{754}\right)/\left({\mathrm{R}}_{724}-{\mathrm{R}}_{773}\right)$ achieved an R

^{2}

_{cv}of 0.94 and RMSE

_{cv}of 2.81, demonstrating a substantial advantage over the bifacial MDATT index (R

^{2}

_{cv}= 0.91, RMSE

_{cv}= 3.53) and the DLARI derived using wavelengths shorter than 750 nm. The results revealed that the DLARIs incorporating longer wavelengths efficiently improved LCC estimation accuracy, whether for the adaxial, abaxial or bifacial datasets.

#### 3.4. Comparing Developed Indices with Those of Previous Studies

^{2}

_{cv}and higher RMSE

_{cv}values were obtained. The VOG1, MTCI, mSR705, mND705, and VOG2 indices yielded RMSE

_{cv}values of approximately 3.5 from the adaxial dataset, while the RMSE

_{cv}values increased to 7.5 when applied to the bifacial dataset. Although the MTCI, mSR705, Maccioni, and DATT share the same format, the Maccioni and DATT indices (which employ two of the same wavelengths) performed better on the bifacial dataset. This was possibly because one of the bands used by Maccioni and Datt is located within the NIR region (780 nm and 850 nm), where there is little absorption by any leaf pigments (${\mathsf{\Sigma}k}_{\mathrm{i}}{C}_{\mathrm{i}}$ = 0) [12]. This partly reduces spectral differences caused by the different absorption properties of pigments at the adaxial and abaxial surfaces.

_{cv}= 2.72; RMSE

_{cv}= 3.73), although they did not perform as well as the indices developed in this study. The two MDATTs were proposed for estimating the LCC of woody plants, such as white poplar (Populus alba) and grapevine (Vitis L.) [21]. The difference in wavelength combinations and retrieval accuracy between Lu’s MDATTs and the MDATT optimized in this study can be attributed to the differences in phenotypic expressions (such as leaf hair, wax, palisade tissues, spongy tissues, etc.) between woody plant leaves and peanut leaves. By adding an additional band to the MDATT, the DLARI substantially improved retrieval accuracy, especially for bifacial reflectance measurements. When compared with the published vegetation indices, the indices developed in this study achieved the highest retrieval accuracies for estimating peanut LCC, whether for the adaxial or mixed surfaces.

#### 3.5. Comparison of the DLARI and MDATT

_{1}with an additional wavelength (λ

_{4}). We evaluated the improvement of incorporating this additional wavelength by calculating the maximum R

^{2}of all band combinations for the DLARI formula based on the three datasets.

^{2}was derived from combinations by fixing λ

_{4}while changing λ

_{1}, λ

_{2}, and λ

_{3}from 700 nm to 760 nm. The wavelength regions for λ

_{4}were from 700 nm to 900 nm. The results are plotted in Figure 9. It shows that when λ

_{4}was at 700 nm to 760 nm, the maximum R

^{2}derived from adaxial DLARIs and abaxial DLARIs were similar to those of the adaxial MDATT (0.95) and abaxial MDATT (0.94), while bifacial DLARIs achieved higher correlations with LCC than the bifacial MDATT (0.91) since λ

_{4}was higher than 709 nm. When λ

_{4}was higher than 760 nm, all three DLARIs were much more correlated to LCC than the three MDATTs. The highest R

^{2}of the adaxial DLARI and bifacial DLARI were obtained at λ

_{4}equal to 819 nm and 773 nm, respectively, and then rapidly became lower than those of the MDATT when λ

_{4}was located above 890 nm. For the abaxial dataset, the highest R

^{2}appeared at λ

_{4}equal to 774 nm and then became lower than the abaxial MDATT when λ

_{4}was above 840 nm. Compared to the MDATT, the effective regions of λ

_{4}for the adaxial DLARI were from 760 nm to 890 nm and for the abaxial DLARI when they were from 760 nm to 840 nm. For the bifacial DLARI, the robust wavelength regions of λ

_{4}were from 709 nm to 890 nm.

_{cv}of MDATT dramatically increased from 2.52 to 3.35 when adding one-sixth of the abaxial samples into the adaxial dataset and then linearly increased to 3.55. With the increase of abaxial reflectance, the RMSE

_{cv}of DLARI stably increased from 2.37 to 2.82. Compared to the MDATT, the DLARI possessed a linear response to the impact of abaxial reflectance. The optimal wavelengths for the DLARI and MDATT derived from each sub-dataset showed unobvious changes with the addition of the abaxial reflectance. It can be concluded that the presence of abaxial leaves decreased the accuracy of DLARI and MDATT for LCC retrieval, but had no obvious influence on the optimal wavelengths for DLARI and MDATT.

## 4. Discussion

_{1}is from 723 to 885 nm and for λ

_{2}and λ

_{3}from 697 to 771 nm for woody plants. We optimized the wavelengths used in the MDATT to increase its suitability for peanut LCC estimation, but the effects from the dorsiventral leaf structure remained. The optimal wavelengths for the bifacial MDATT were 723 nm, 738 nm, and 722 nm. Using the MDATT as a basis, we constructed a DLARI to further decrease the impact of abaxial leaves. Compared with the MDATT and the published indices without considering the dorsiventral leaf structure, the three DLARIs performed best. The adaxial DLARI improved the estimation accuracy to 2.37 with a high R

^{2}of 0.96. The abaxial DLARI achieved a performance with R

^{2}= 0.95 and RMSE = 2.58. The bifacial DLARI showed an R

^{2}value of 0.94 and RMSE of 2.81. The results showed that the DLARIs not only improved the retrieval of LCC from the adaxial side of the leaf, but also further reduced the impact of differences in the adaxial and abaxial leaf reflectance thus increasing LCC estimation from the bifacial reflectance.

_{s}) of the leaf and internal reflectance (R

_{i}) of the leaf [42]. The reflectance of the adaxial and abaxial leaf side differ both in the R

_{s}and R

_{i}. The MDATT and DLARI formulae both successfully removed R

_{s}according to Equations (2) and (3), respectively. The left R

_{i}is influenced by pigments concentrations and absorption properties which are different at the two sides of the leaf [16]. For the MDATT, the optimal wavelengths for λ

_{1}and λ

_{3}changed from 701 nm and 740 nm to 723 nm and 722 nm (Figure 10). The reflectance of the adaxial surface and abaxial surface at 723 nm and 722 nm showed minimum difference (Figure 2). The optimal wavelengths λ

_{2}for bifacial MDATT was located at 738 nm where the reflectance of both sides showed similar sensitivity to LCC (Figure 3). The ability of MDATT to decrease the R

_{i}effect contributed to the combination of these three wavelengths. For the DLARI, with the addition of abaxial samples into the adaxial dataset, the four wavelengths gradually changed to approximately 732 nm, 754 nm, 724 nm, and 773 nm (Figure 10). At 732 nm and 724 nm, the adaxial reflectance showed higher sensitivity to LCC than the abaxial reflectance. In contrast, at 754 nm and 773 nm, the abaxial reflectance showed stronger correlation to LCC than the adaxial reflectance (Figure 3). In addition, the optimal λ

_{3}was located at the region where spectral differences among the two sides of the leaf were negligible (Figure 2). The wavelength near 754 nm is known as the red-edge shoulder and has shown considerable potential in suppressing the influence of leaf structure [43]. These factors contributed to DLARI being optimal for LCC estimation when bifacial reflectance measurements were used.

_{cv}) was improved from 2.52 to 2.37 by adding the fourth wavelength. Our results proved that indices based on four bands led to further improvements compared to two-band and three-band indices.

## 5. Conclusions

^{2}

_{cv}of 0.91 (RMSE

_{cv}= 3.53). The DLARI incorporated an additional wavelength in the NIR and exhibited the best retrieval accuracy when compared to the MDATT and other previously published indices. The DLARIs of $\left({\mathrm{R}}_{735}-{\mathrm{R}}_{753}\right)/\left({\mathrm{R}}_{715}-{\mathrm{R}}_{819}\right)$ and $\left({\mathrm{R}}_{732}-{\mathrm{R}}_{754}\right)/\left({\mathrm{R}}_{724}-{\mathrm{R}}_{773}\right)$ are recommended for retrieval of LCC using adaxial and bifacial reflectance, respectively. These two DLARIs delivered excellent cross-validation accuracies (R

^{2}

_{cv}= 0.96, RMSE

_{cv}= 2.37; R

^{2}

_{cv}= 0.94, RMSE

_{cv}= 2.81). The effective wavelength regions for DLARI were from the red edge to the NIR. Compared to the MDATT, the DLARI showed stronger correlation to LCC and less sensitivity to abaxial surface structure. This research provided new insights into the impact of spectral differences between adaxial and abaxial leaf surfaces on LCC estimation and proposed DLARI to improve LCC retrieval accuracy. The spectral differences between adaxial and abaxial leaf surfaces should be considered when estimating peanut canopy parameters. Further studies should be carried out to verify the applicability of DLARI to other plant species which have similar physiological response to solar radiation and drought stress as peanut.

## Supplementary Materials

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

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**Figure 1.**Photographs of the peanut canopy in the field. Only the adaxial surface is visible under low solar irradiance (

**a**), while both the adaxial and abaxial surfaces are visible under high solar irradiance (

**b**).

**Figure 2.**The spectral reflectance of the adaxial and abaxial surfaces (

**a**) and the associated difference in reflectance among the two sides (

**b**). (The solid line represents the mean of sampled reflectance and the shaded zone represents standard deviation).

**Figure 4.**The correlation between LCC and the reflectance of the adaxial and abaxial surfaces from 350 to 2500 nm.

**Figure 5.**The maximum R

^{2}values associated with the MDATT band combinations ranging from 400 nm to 1000 nm (

**a**–

**c**) and its corresponding λ

_{1}(

**d**–

**f**). The left column is for the adaxial dataset, the middle column is for the abaxial dataset, and the right column is for the bifacial dataset. For the sake of concise display, only R

^{2}values greater than 0.8 were considered.

**Figure 6.**Relationships between MDATTs and LCC (

**a**–

**c**) and scatter plots between observed LCC and LCC predicted by the associated linear models (

**d**–

**f**). The different colors indicate the 10 fold cross-validation subsets. The left column is for the adaxial dataset, the middle column is for the abaxial dataset, and the right column is for the bifacial dataset.

**Figure 7.**Relationships between optimal DLARI indices and LCC (

**a**–

**c**) and scatter plots between observed LCC and LCC predicted by the associated linear models (

**d**–

**f**). The different colors indicate the 10 fold cross-validation subsets. The left column is for the adaxial dataset, the middle column is for the abaxial dataset, and the right column is for the bifacial dataset.

**Figure 8.**Comparison of published vegetation indices and the indices developed in this study for LCC estimation using adaxial and bifacial reflectance measurements.

**Figure 9.**Maximum R

^{2}between LCC and MDATTs, DLARIs with λ

_{1}, λ

_{2}, and λ

_{3}from 700 nm to 760 nm and λ

_{4}from 700 nm to 900 nm.

**Figure 10.**The impact of the dorsiventral leaf structure on DLARI and MDATT in terms of the estimation accuracy and optimal wavelengths. The numbers in the brackets are the optimal wavelengths for DLARI and MDATT with a unit of nm.

Index | Abbreviation | Formula | Scale | Reference |
---|---|---|---|---|

Gitelson’s index | Gitelson | 1/R700 | Leaf | [35] |

Vogelmann’s first Index | VOG1 | R740/R720 | Leaf | [36] |

Carter’ Index | Carter | R710/R760 | Leaf | [37] |

MERIS Terrestrial Chlorophyll Index | MTCI | (R740 − R705)/(R705 − R665) | Canopy | [22] |

Modified Simple Ratio | mSR705 | (R750 − R445)/(R705 − R445) | Leaf | [13] |

Modified Normalized Difference Vegetation Index | mND705 | (R750 − R705)/(R750 + R705 − 2 × R445) | Leaf | [13] |

Datt’ Index | DATT | (R850 − R710)/(R850 − R680) | Leaf | [12] |

Maccioni’ Index | Maccioni | (R780 − R710)/(R780 − R680) | Leaf | [38] |

Vogelmann’s second Index | VOG2 | (R734 − R747)/(R715 + R720) | Leaf | [36] |

Red-Edge Position Index | REP | 700 + 40 × (((R670 + R780)/2 − R700)/(R740 − R700)) | Leaf | [39] |

Modified Datt Index | Lu′s MDATT | (R691 − R7745)/(R7691 − R745) | Leaf | [21] |

Modified Datt Index | Lu′s MDATT | (R721 − R744)/(R721 − R714) | Leaf | [21] |

**Table 2.**Cross-validation results for the MDATT indices in the case of the adaxial, abaxial, and bifacial datasets.

Index | Dataset | Wavelength Region Considered (nm) | Optimal Wavelengths (nm) | R^{2} | R^{2}_{cv} | RMSE_{cv}(μg/cm ^{2}) |
---|---|---|---|---|---|---|

MDATT | Adaxial reflectance | 400–1000 | λ_{1}: 701; λ_{2}: 742; λ_{3}: 740 | 0.95 | 0.95 | 2.52 |

Abaxial reflectance | 400–1000 | λ_{1}: 718; λ_{2}: 747; λ_{3}: 720 | 0.94 | 0.94 | 2.69 | |

Bifacial reflectance | 400–1000 | λ_{1}: 723; λ_{2}: 738; λ_{3}: 722 | 0.91 | 0.91 | 3.53 |

**Table 3.**LCC retrieval accuracy of the adaxial and bifacial MDATT indices when applied to bifacial dataset.

Dataset | Adaxial MDATT | Bifacial MDATT | ||
---|---|---|---|---|

R^{2}_{cv} | RMSE_{cv} (μg/cm^{2}) | R^{2}_{cv} | RMSE_{cv} (μg/cm^{2}) | |

Bifacial reflectance | 0.87 | 4.14 | 0.91 | 3.53 |

**Table 4.**Cross-validation results for the optimal dorsiventral leaf adjusted ratio indices (DLARIs) derived using wavelengths between 660 and 750 nm in the case of the adaxial, abaxial, and bifacial datasets.

Index | Dataset | Wavelength Region Considered (nm) | Optimal Wavelengths (nm) | R^{2} | R^{2}_{cv} | RMSE_{cv}(μg/cm ^{2}) |
---|---|---|---|---|---|---|

DLARI | Adaxial reflectance | 660–750 | λ_{1}: 740; λ_{2}: 742; λ_{3}: 703; λ_{4}: 731 | 0.95 | 0.95 | 2.53 |

Abaxial reflectance | 660–750 | λ_{1}: 714; λ_{2}: 746; λ_{3}: 718; λ_{4}: 720 | 0.94 | 0.94 | 2.62 | |

Bifacial reflectance | 660–750 | λ_{1}: 731; λ_{2}: 741; λ_{3}: 722; λ_{4}: 750 | 0.91 | 0.92 | 3.34 |

**Table 5.**Cross-validation results for the optimal DLARIs derived using wavelengths between 660 and 820 nm in the case of the adaxial, abaxial, and bifacial datasets.

Index | Dataset | Wavelength Region Considered (nm) | Optimal Wavelengths (nm) | R^{2} | R^{2}_{cv} | RMSE_{cv}(μg/cm ^{2}) |
---|---|---|---|---|---|---|

DLARI | Adaxial reflectance | 660–820 | λ_{1}: 735; λ_{2}: 753; λ_{3}: 715; λ_{4}: 819 | 0.96 | 0.96 | 2.37 |

Abaxial reflectance | 660–820 | λ_{1}: 731; λ_{2}: 755; λ_{3}: 722; λ_{4}: 774 | 0.95 | 0.95 | 2.58 | |

Bifacial reflectance | 660–820 | λ_{1}: 732; λ_{2}: 754; λ_{3}: 724; λ_{4}: 773 | 0.94 | 0.94 | 2.81 |

© 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**

Xie, M.; Wang, Z.; Huete, A.; Brown, L.A.; Wang, H.; Xie, Q.; Xu, X.; Ding, Y.
Estimating Peanut Leaf Chlorophyll Content with Dorsiventral Leaf Adjusted Indices: Minimizing the Impact of Spectral Differences between Adaxial and Abaxial Leaf Surfaces. *Remote Sens.* **2019**, *11*, 2148.
https://doi.org/10.3390/rs11182148

**AMA Style**

Xie M, Wang Z, Huete A, Brown LA, Wang H, Xie Q, Xu X, Ding Y.
Estimating Peanut Leaf Chlorophyll Content with Dorsiventral Leaf Adjusted Indices: Minimizing the Impact of Spectral Differences between Adaxial and Abaxial Leaf Surfaces. *Remote Sensing*. 2019; 11(18):2148.
https://doi.org/10.3390/rs11182148

**Chicago/Turabian Style**

Xie, Mengmeng, Zhongqiang Wang, Alfredo Huete, Luke A. Brown, Heyu Wang, Qiaoyun Xie, Xinpeng Xu, and Yanling Ding.
2019. "Estimating Peanut Leaf Chlorophyll Content with Dorsiventral Leaf Adjusted Indices: Minimizing the Impact of Spectral Differences between Adaxial and Abaxial Leaf Surfaces" *Remote Sensing* 11, no. 18: 2148.
https://doi.org/10.3390/rs11182148