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

The performance of the published vegetation indices for LCC retrieval using adaxial and bifacial reflectance measurements is shown in Figure 8, as is the performance of the indices developed in this study. The published indices, which ranged from single- to four-band formulae, produced reliable retrievals of LCC when applied to adaxial reflectance measurements. In general, the three-band indices performed better than the two-band indices and the two-band indices performed better than Gitelson's index. The best performing indices were MTCI, DATT, and Maccioni followed by the four-band index VOG2. The red edge position index performed worse than all the three-band indices. However, when applied to the bifacial dataset, much lower *R*<sup>2</sup> cv and higher RMSEcv values were obtained. The VOG1, MTCI, mSR705, mND705, and VOG2 indices yielded RMSEcv values of approximately 3.5 from the adaxial dataset, while the RMSEcv 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 (Σ*k*i*C*<sup>i</sup> = 0) [12]. This partly reduces spectral differences caused by the different absorption properties of pigments at the adaxial and abaxial surfaces.

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

Among the published indices, Lu's MDATTs, which uses different wavelengths for the adaxial and bifacial surfaces, provided the most accurate LCC retrievals (RMSEcv = 2.72; RMSEcv = 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.
