Assessment of red-edge vegetation indices for crop leaf area index estimation

By T. Dong, Jiangui Liu, J. Shang, B. Qian, Baoluo Ma, J. Kovacs, D. Walters, X. Jiao, X. Geng, Yichao Shi. Remote Sensing of Environment, 2019

269 citations recorded by the source corpus.

Abstract This study explores the potential of vegetation indices (VIs) for crop leaf area index (LAI) estimation, with a focus on comparing red-edge reflectance based (RE-based) and the visible reflectance based (VIS-based) VIs. Seven VIs were derived from multi-temporal RapidEye images to correlate with LAI of two crop species having contrasting leaf structures and canopy architectures: spring wheat (a monocot) and canola (a dicot) in northern Ontario, Canada. The relationship between LAI and the selected VIs (LAI-VI) was characterized using a semi-empirical model. The Markov Chain Monte Carlo (MCMC) sampling method was used to estimate the model parameters, including the extinction coefficient (KVI) and VI value for dense green canopy (VI∞). Results showed that crop-specific regression models were much closer to a generic regression model using the RE-based VIs than using the VIS-based VIs. Furthermore, the joint posterior probability distribution of the KVI and VI∞ of the RE-based VIs tended to converge for the two crops. This suggests that the RE-based VIs are not as sensitive to canopy structure, e.g., the average leaf angle (ALA), as the VIS-based VIs. This is also demonstrated by the sensitivity analyses using both PROSAIL simulations and field measurements. Hence, the RE-based VIs can be used to develop a more generic LAI estimation algorithm for different crops. Further studies are required to assess the impact of soil reflectance and other factors, such as illumination-target-viewing geometries and atmospheric conditions, on LAI retrieval.

Subfields: Ecology, Global and Planetary Change, Plant Science

View Paper: https://www.semanticscholar.org/paper/d7549cca72e82ac6ae845d98247a88ebc5fa4c96

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Citations in the corpus

Recent Applications of Landsat 8/OLI and Sentinel-2/MSI for Land Use and Land Cover Mapping: A Systematic Review (2020, methodology): “[172], was already formulated using NIR and Red-edge bands [37,86,168], two Red-edge bands [39,85,167,169], and Red-edge and Green bands [158].”
Applications of Remote Sensing in Precision Agriculture: A Review (2020, background): “Recently, red-edge based vegetation indices have been shown to be promising for estimating LAI in multiple crops [271].”