**1. Introduction**

Peanut (*Arachishypogaea* L.) is one of the major food legumes as well as oilseed crops being grown in 118 countries (or regions) around the world on about 28 million ha of land [1], and offers multiple benefits to meet human nutritional needs as well as being an important resource in the context of food security and hunger issues [2]. Leaf chlorophyll content (LCC) is an important indicator of plant photosynthesis [3], nutritional state [4], and stress [5]. Determination of LCC is crucial for crop management and precision agriculture practices [6].

Spectral vegetation indices, which are defined with the objective of enhancing spectral sensitivity to vegetation properties, have long been popular for estimating vegetation's biophysical and biochemical variables [7,8]. Decades of research have gone into determining wavelength regions sensitive to LCC in order to develop indices to maximize the accuracy of retrieval for different types of plants [9–11]. Datt [12] developed a three-band index for retrieval of LCC in higher plants based on the different response of reflectance at 710 nm and 850 nm to LCC. Sims and Gamon [13] analyzed nearly 400 leaf samples from 53 species and found that the mSR705 (simple ratio) and mND705 (normalized difference) were relatively insensitive to species and leaf structural variation. Gitelson et al. [14] proposed an index (RnirRred-edge − 1), which is an effective LCC predictor for maple, chestnut, wild vine, and beech leaves.

The pinnate leaves of peanut are highly sensitive to excess solar radiation and drought stress [15]. Field observations show that under strong solar irradiance, peanut easily turns the abaxial surface of leaves upwards. As a result, the spectral reflectance recorded by satellites or spectroradiometers may represent a mixture of the adaxial and abaxial surfaces in different proportions. The differences in optical properties of dorsiventral leaves due to the structural difference among the two sides have been well documented [16,17]. Baránková et al. [18] found that light incident from the adaxial side is more effectively absorbed than light incident from the abaxial side of green tobacco leaves. Lu and Lu [19] reported the lower reflectance of the adaxial white poplar surfaces compared to the abaxial faces in the 400–700 nm spectrum but reported an inversion of this effect in the near infrared wavelengths (700–1000 nm).

Leaf optical properties are a vital factor in determining the sensitivity of vegetation indices to LCC [13]. However, to the best of our knowledge, few studies have considered the influence of abaxial leaf reflectance on the retrieval of biochemistry and structure parameters. In one of the few studies carried out, Lu et al. [20] extended the wavelengths in the Datt's index to incorporate spectral reflectance from 400 nm to 1000 nm. They found that the modified Datt's index (MDATT) efficiently reduced the effects of bifacial leaf structure and improved the retrieval of white poplar and Chinese elm LCC. However, several characteristics of peanut leaves, such as leaf hair, wax, palisade, and spongy tissues, substantially differ from woody plants. Thus, the applicability of the MDATT to peanut LCC retrieval requires further investigation. In addition, the structural effects were mostly removed by MDATT but partially remained [21].

Theoretically, multiple-band indices can incorporate a larger amount of information and have the potential to improve retrieval accuracy [22–26]. For example, the mSR705 and mND705, which were developed by adding a band (R445) to the exiting two-band indices SR705 and ND705, effectively improved sensitivity to LCC [13]. Similarly, three-band indices such as the MERIS terrestrial chlorophyll index (MTCI) [22] and OLCI terrestrial chlorophyll index (OTCI) has been successfully used to retrieve chlorophyll content at the canopy scale (i.e., chlorophyll content) [27–29]. To date, very few studies have been conducted to assess the potential of vegetation indices based on four or more bands for improving LCC retrieval accuracy.

To address these gaps, this paper focuses on the development and optimization of new and existing indices that are insensitive to spectral differences among two sides of peanut leaves. The objectives of this work were to (1) analyze spectral differences in the adaxial and abaxial surfaces of peanut leaves; (2) identify the optimal wavelengths of the MDATT for estimating peanut LCC; (3) develop a novel index based on a four-band combination to reduce spectral differences in dorsiventral leaves for improving LCC retrieval; (4) compare the performance of the indices developed in this study with those widely used in the literature.
