Detection of symptoms induced by vascular plant pathogens in tree crops using high-resolution satellite data: Modelling and assessment with airborne hyperspectral imagery

Abstract

Infection by the fungus Verticillium dahliae (Vd) and the bacterium Xylella fastidiosa (Xf) threatens the production of olives (Olea europaea L.) and almonds (Prunus dulcis Mill.) worldwide. Producing symptoms that resemble water stress or nutrient deficiency, infection by these vascular pathogens restricts water and nutrient flow through the xylem. Hyperspectral, narrow-band multispectral, and thermal imagery acquired at a high spatial resolution can detect disease symptoms, even before they are visible, potentially allowing growers to distinguish infected plants from those affected by confounding environmental stresses. Nevertheless, operational detection of vascular disease using high-resolution commercial satellite multispectral images remains to be evaluated. Here, we assessed the capacity of high-resolution Worldview-2 and -3 multispectral imagery to detect Xf and Vd infections in olive and almond orchards in Spain, Italy, and Australia between 2011 and 2021. We compared the accuracy of detecting both pathogens using the satellite imagery with results obtained using aerial high-resolution hyperspectral and thermal imaging, with model-inverted plant traits, solar-induced chlorophyll fluorescence (SIF), and thermal data as a reference. Our results using spectral plant traits to examine disease progression at all stages showed that traits and their importance varied as a function of disease severity. Worldview-2 and -3 detected the disease incidence with overall accuracies ranging from 0.63 to 0.83 and kappa coefficients (κ) ranging from 0.29 to 0.68. Nevertheless, detecting the early stages of disease with multispectral satellite data yielded poorer results, with κ values of 0.22–0.45, compared with κ values of 0.3–0.69 obtained from hyperspectral data. Typical multispectral bandsets available from satellite sensors cannot measure important plant traits such as the blue index NPQI, xanthophyll proxy PRIn, SIF, and anthocyanin levels, thus explaining the poorer results obtained from multispectral satellite data for the early detection of vascular diseases. Adding a thermal-based crop water stress indicator to the satellite data improved the overall accuracies by 10–15% and increased κ by >0.2 units. This work shows that commercial multispectral high-spatial resolution imagery can be used to detect intermediate and advanced Xf and Vd infection, but that the early detection of disease symptoms requires hyperspectral and thermal data.