**4. Discussion**

In the case of narrow band indices such as the MDATT, different wavelength combinations can provide the best performance for different vegetation types. Lu et al. [21] suggested the robust wavelength region for λ<sup>1</sup> is from 723 to 885 nm and for λ<sup>2</sup> and λ<sup>3</sup> 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*<sup>2</sup> of 0.96. The abaxial DLARI achieved a performance with *R*<sup>2</sup> = 0.95 and RMSE = 2.58. The bifacial DLARI showed an *R*<sup>2</sup> 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.

The measured leaf reflectance can be composed of the external (surface) reflectance (*R*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*<sup>s</sup> and *R*i. The MDATT and DLARI formulae both successfully removed *R*<sup>s</sup> according to Equations (2) and (3), respectively. The left *R*<sup>i</sup> 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<sup>1</sup> and λ<sup>3</sup> 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<sup>2</sup> 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*<sup>i</sup> 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<sup>3</sup> 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.

Compared to the published vegetation indices, the DLARI not only decreased the effect of dorsiventral leaf structure but also significantly improved LCC estimation from the adaxial reflectance measurements. In fact, when multiple bands are available, there is no reason to limit to two-band or three-band indices. For instance, the first three wavelengths used in the DLARI were similar to that used in the MDATT for the adaxial dataset, but the accuracy (RMSEcv) 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.

As previously mentioned, the reliability of narrow-band indices can be influenced by a range of phenotypic characteristics. Further work is required to assess the application of DLARI to estimate LCC for other crop species. The robust wavelength regions proposed should provide a good starting point for optimizing the index for other crop species.

The potential of satellites, such as Sentinel-2, to map crop biophysical variables has been shown by many studies [44,45]. The Sentinel-2 multispectral instrument includes three bands in the red-edge region centered at 705, 740, and 775 nm, which were found to be of great interest for crop monitoring [46]. Unmanned aerial vehicle (UAV) platforms coupled with imaging sensors are able to collect multispectral or hyperspectral imagery and offer great possibilities in the precision farming [47,48]. When using these remote sensing techniques to investigate peanut canopy information, the spectral information collected by the sensors may not only come from the adaxial leaf surfaces but also the abaxial leaf surfaces. Our results provide evidence that ignoring the spectral difference among the two faces introduces significant errors in LCC estimation. Further work should consider this effect when estimating peanut chlorophyll content or other biochemistry parameters at the canopy scale. The application of DLARI on remote sensing sensors to estimate canopy chlorophyll content is yet to be tested.
