Comparative analysis of index and chemometric techniques-based assessment of leaf area index (LAI) in wheat through field spectroradiometer, Landsat-8, Sentinel-2 and Hyperion bands

Abstract

Successful retrieval of leaf area index (LAI) from hyperspectral remote sensing relies on the proper selection of indices or multivariate models. The objectives of the research work were to identify best vegetation index and multivariate model based on canopy reflectance and LAI measured at different growth stages of wheat. Comparison of existing indices revealed optimized soil-adjusted vegetation index (OSAVI) as the best index based on R² of calibration, validation and root mean square error of validation. Proposed ratio index (RI; R₆₇₀, R₈₄₅) and normalized difference index (NDI; R₆₇₀, R₈₄₅) provided comparable performance with the existing vegetation indices (R² = 0.65 and 0.62 for RI and NDI, respectively, during validation). Among the multivariate models, partial least squares regression (PLSR) model with Hyperion band configuration performed the best during validation (R² = 0.80 and RMSE = 0.58 m² m⁻²). Our results manifested the opportunities for developing biophysical products based on satellite sensors.

Keywords:

• Leaf area index
• Landsat-8
• Sentinel-2
• Hyperion
• multivariate models

Disclosure statement

No potential conflict of interests was reported by the author(s).

Additional information

Funding

Bappa das acknowledges the fellowship received from CSIR, New Delhi, India [18-12/2011 (ii) EU-V] toundertake this research work as part of Ph.D. This work was supported by National Agriculture ScienceFund (NASF), ICAR [NASF(ICAR)-Phen 2015/2011-12] under its project on ‘Phenomics of moisture deficit and low temperature stress tolerance in rice’ at IARI.