Comparison of Vegetation Indices for Leaf Area Index Estimation in Vertical Shoot Positioned Vine Canopies with and without Grenbiule Hail-Protection Netting

Pedro C Towers,Carlos Poblete-Echeverría,Albert Strever

doi:10.3390/rs11091073

Pedro C Towers, Carlos Poblete-Echeverría + Show 1 more

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https://doi.org/10.3390/rs11091073

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Journal: Remote Sensing	Publication Date: May 7, 2019
Citations: 54	License type: CC BY 4.0

Affiliation: Stellenbosch University

Abstract

Leaf area per unit surface (LAI—leaf area index) is a valuable parameter to assess vine vigour in several applications, including direct mapping of vegetative–reproductive balance (VRB). Normalized difference vegetation index (NDVI) has been successfully used to assess the spatial variability of estimated LAI. However, sometimes NDVI is unsuitable due to its lack of sensitivity at high LAI values. Moreover, the presence of hail protection with Grenbiule netting also affects incident light and reflection, and consequently spectral response. This study analyses the effect of protective netting in the LAI–NDVI relationship and, using NDVI as a reference index, compares several indices in terms of accuracy and sensitivity using linear and logarithmic models. Among the indices compared, results show NDVI to be the most accurate, and ratio vegetation index (RVI) to be the most sensitive. The wide dynamic range vegetation index (WDRVI) presented a good balance between accuracy and sensitivity. Soil-adjusted vegetation index 2 (SAVI2) appears to be the best estimator of LAI with linear models. Logarithmic models provided higher determination coefficients, but this has little influence over the normal range of LAI values. A similar NDVI–LAI relationship holds for protected and unprotected canopies in initial vegetation stages, but different functions are preferable once the canopy is fully developed, in particular, if tipping is performed.

Highlights

Leaf area per unit surface (LAI—leaf area index) has long been recognised as an essential parameter to assess net assimilation rate, growth, and primary productivity in crops, including vines [1,2,3]
remote sensing (RS) could be applied to asses one of the two components of the vegetative–reproductive balance (VRB), defined as the leaf area required to carry a unit of fruit to maturity [8], which has been shown to be a critical factor in the determination of grape quality [9]
If RS data available have sufficient resolution to allow canopy values to be separated from soil values, the high LAI values typical of VSPs should occlude soil response and VIs adjusted for soil response would be expected to show little improvement in the accuracy of LAI estimation

Summary

Introduction

Leaf area per unit surface (LAI—leaf area index) has long been recognised as an essential parameter to assess net assimilation rate, growth, and primary productivity in crops, including vines [1,2,3]. Its spatial variability may be mapped for management or harvest decisions and is closely related to vegetation indices obtained from remote sensing (RS) [4,5]. Such techniques have been employed to map viticulture crop status parameters such as pruning weight [4,5,6,7]. Significant variability in plant vigour and yield have been observed in many crops, including vines [10] This effect is more evident with the increasing use of yield monitors [11,12,13].

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