Comparison of Different Vegetative Indices for Calibrating Proximal Canopy Sensors to Grapevine Pruning Weight

Canopy sensing in viticulture is widely associated with the term NDVI (normalized difference vegetation index). However, there are many other vegetative indices (VIs) that can be calculated from information captured with visible/near-infrared (NIR) sensors. A proximal canopy sensor was used to survey 27 vineyards in the Lake Erie Concord belt and stratified to collect pruning weights (PW) at a density of similar to 25 samples per vineyard. Seven VIs were derived from the sensor data and the first principal component (PC1) extracted from a principal components analysis of the seven VIs. The VIs and PC1 were regressed against the local PW measurements and ranked in terms of their goodness-of-fit. Over the 27 vineyards, there was no single VI that outperformed the others, although VIs that used the red-edge band had a slight advantage over VIs using the red band. It is therefore recommended to use the normalized difference red edge index (NDRE) in place of the NDVI when predicting PW from terrestrial-based proximal canopy surveys. The PC1 derived from the decomposition of all seven VIs did appear to convey some benefit to PW prediction compared with a single VI approach, particularly with just NDVI. More research into the potential for multivariate approaches is recommended.

Automated summary

Research on canopy sensing in viticulture explores the use of various vegetative indices and principal component analysis to predict pruning weights. The study suggests that the normalized difference red edge index (NDRE) outperforms the normalized difference vegetation index (NDVI) in predicting pruning weights, and that multivariate approaches may offer advantages over single index methods.