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 ORCID: https://orcid.org/0000-0003-4807-9056, Wang, H, Xie, Q, Xu, X and 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) .

[img]
Preview
PDF - Published Version
Available under License Creative Commons Attribution 4.0.

Download (4MB) | Preview

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.

Item Type: Article
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: Remote Sensing
Publisher: MDPI
ISSN: 2072-4292
Depositing User: LA Brown
Date Deposited: 28 Oct 2022 08:59
Last Modified: 28 Oct 2022 09:00
URI: https://usir.salford.ac.uk/id/eprint/65385

Actions (login required)

Edit record (repository staff only) Edit record (repository staff only)