Estimating peanut leaf chlorophyll content with dorsiventral leaf adjusted indices: minimizing the impact of spectral differences between adaxial and abaxial leaf surfaces

Xie, M; Wang, Z; Huete, A; Brown, LA; Wang, H; Xie, Q; Xu, X; Ding, Y

Abstract

Relatively little research has assessed the impact of spectral differences among dorsiventral leaves caused by leaf structure on leaf chlorophyll content (LCC) retrieval. Based on reflectance measured from peanut adaxial and abaxial leaves and LCC measurements, this study proposed a dorsiventral leaf adjusted ratio index (DLARI) to adjust dorsiventral leaf structure and improve LCC retrieval accuracy. Moreover, the modified Datt (MDATT) index, which was insensitive to leaves structure, was optimized for peanut plants. All possible wavelength combinations for the DLARI and MDATT formulae were evaluated. When reflectance from both sides were considered, the optimal combination for the MDATT formula was (R723−R738)/(R723−R722) with a cross-validation R2cv of 0.91 and RMSEcv of 3.53 μg/cm2. The DLARI formula provided the best performing indices, which were (R735−R753)/(R715−R819) for estimating LCC from the adaxial surface (R2cv = 0.96, RMSEcv = 2.37 μg/cm2) and (R732−R754)/(R724−R773) for estimating LCC from reflectance of both sides (R2cv = 0.94, RMSEcv = 2.81 μg/cm2). 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.

Citation

Xie, M., Wang, Z., Huete, A., Brown, L., Wang, H., Xie, Q., …Ding, Y. (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(18), https://doi.org/10.3390/rs11182148

• Journal Article Type: Article
• Acceptance Date: Sep 11, 2019
• Online Publication Date: Sep 15, 2019
• Publication Date: Sep 15, 2019
• Deposit Date: Oct 28, 2022
• Publicly Available Date: Oct 28, 2022
• Journal: Remote Sensing
• Publisher: MDPI
• Volume: 11
• Issue: 18
• DOI: https://doi.org/10.3390/rs11182148
• Publisher URL: https://doi.org/10.3390/rs11182148