Retrieval of leaf chlorophyll content in field crops using narrow-band indices: Effects of leaf area index and leaf mean tilt angle

Xiaochen C. Zou (Corresponding Author), Rocío Hernández-Clemente, Priit Tammeorg, Clara Lizarazo Torres, Frederick L. Stoddard, Pirjo Mäkelä, Petri Pellikka, Matti Mõttus

Research output: Contribution to journalArticleScientificpeer-review

26 Citations (Scopus)


Reliable estimation of leaf chlorophyll-a and -b content (chl-a + b) at canopy scales is essential for monitoring vegetation productivity, physiological stress, and nutrient availability. To achieve this, narrow-band vegetation indices (VIs) derived from imaging spectroscopy data are commonly used. However, VIs are affected by canopy structures other than chl-a + b, such as leaf area index (LAI) and leaf mean tilt angle (MTA). In this study, we evaluated the performance of 58 VIs reported in the literature to be chl-a + b-sensitive against a unique measured set of species-specific leaf angles for six crop species in southern Finland. We created a large simulated canopy reflectance database (100,000 canopy configurations) using the physically based PROSAIL (coupling of PROSPECT and SAIL (Scattering by Arbitrarily Inclined Leaves) radiative transfer models) model. The performance of model-simulated indices was compared against airborne AISA Eagle II imaging spectroradiometer data and field-measured chl-a + b, LAI, and MTA values. In general, LAI had a positive effect on the strength of the correlation between chl-a + b and VIs while MTA had a negative effect in both measured and simulated data. Three indices (REIP (red edge inflection point), TCARI (transformed chlorophyll absorption ratio index)/OSAVI (optimized soil-adjusted vegetation index), and CTR6 (Carter indices)) showed strong correlations with chl-a + b and similar performance in model-simulated and measured data set. However, only two (TCARI/OSAVI and CTR6) were independent from LAI and MTA. We consider these two indices robust proxies of crop leaf chl-a + b.
Original languageEnglish
Pages (from-to)6031-6055
JournalInternational Journal of Remote Sensing
Issue number24
Publication statusPublished - 2015
MoE publication typeA1 Journal article-refereed


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