Estimating LAI in vineyard using the PocketLAI smart-app.

Estimating leaf area index (LAI) of Vitis vinifera using indirect methods involves some
critical issues, related to its discontinuous and non-homogeneous canopy. This study evaluates
the smart app PocketLAI and hemispherical photography in vineyards against destructive LAI
measurements. Data were collected during six surveys in an experimental site characterized by
a high level of heterogeneity among plants, allowing us to explore a wide range of LAI values.
During the last survey, the possibility to combine remote sensing data and in-situ PocketLAI estimates
(smart scouting) was evaluated. Results showed a good agreement between PocketLAI data and direct
measurements, especially for LAI ranging from 0.13 to 1.41 (R2 = 0.94, RRMSE = 17.27%), whereas
the accuracy decreased when an outlying value (vineyard LAI = 2.84) was included (R2 = 0.77,
RRMSE = 43.00%), due to the saturation effect in case of very dense canopies arising from lack of
green pruning. The hemispherical photography showed very high values of R2, even in presence of
the outlying value (R2 = 0.94), although it showed a marked and quite constant overestimation error
(RRMSE = 99.46%), suggesting the need to introduce a correction factor specific for vineyards. During
the smart scouting, PocketLAI showed its reliability to monitor the spatial-temporal variability of
vine vigor in cordon-trained systems, and showed a potential for a wide range of applications, also in
combination with remote sensing.