Combination of multispectral imagery, environmental data and thermography for on-the-go monitoring of the grapevine water status in commercial vineyards

Abstract

Efficient irrigation in viticulture requires objective and representative monitoring of the vineyard water status variability. In this work the combination of multispectral, environmental and thermal data (using an infrared radiometer) acquired simultaneously on-the-go (at midday), from a ground moving vehicle (moving at 3 km/h) was tested to assess the vineyard stem water potential (Ψs) and its spatial variability (three different irrigation treatments were imposed) over two seasons in north Spain. Partial least squares (PLS) cross-validation regression models involving the canopy temperature (Tc), environmental and spectral variables yielded determination coefficients (R2cv) of ~ 0.63 and root mean square error of cross-validation (RMSECV) between 0.124 MPa and 0.206 MPa in the two seasons. Linear discriminant analysis (LDA) involving only the variables used to build the regression models was run to distinguish among low, medium and high water stressed vines, yielding an average percentage of correct classification samples of 74%. The satisfactory performance of the multivariate models involving thermal, environmental and spectral data to either estimate or classify the plant water status within a vineyard supports the approach towards the combination of different data source to improve the capabilities of thermography itself. The inclusion of vegetative spectral indices in the regression and classification models of grapevine water status may provide real-time feedback on grapevine water use as influenced by actual vegetative growth, abiotic and/or biotic stress patterns. This combined approach can be seen as an advancement from existing solutions to assess plant water status variability given the simplicity and potential to automation of the thermal sensor employed and the integration of environmental and canopy vigour data into the model.