The early detection of Xylella fastidiosa (Xf) infections is critical to the management of this dangerous plan pathogen across the world. Recent studies with remote sensing (RS) sensors at different scales have shown that Xf-infected olive trees have distinct spectral features in the visible and infrared regions (VNIR). However, further work is needed to integrate remote sensing in the management of plant disease epidemics. Here, we research how the spectral changes picked up by different sets of RS plant traits (i.e., pigments, structural or leaf protein content), can help capture the spatial dynamics of Xf spread. We coupled a spatial spread model with the probability of Xf-infection predicted by a RS-driven support vector machine (RS-SVM) model. Furthermore, we analyzed which RS plant traits contribute most to the output of the prediction models. For that, in almond orchards affected by Xf (n = 1426 trees), we conducted a field campaign simultaneously with an airborne campaign to collect high-resolution thermal images and hyperspectral images in the visible-near-infrared (VNIR, 400–850 nm) and short-wave infrared regions (SWIR, 950–1700 nm). The best performing RS-SVM model (OA = 75%; kappa = 0.50) included as predictors leaf protein content, nitrogen indices (NIs), fluorescence and a thermal indicator (T_c), alongside pigments and structural parameters. Leaf protein content together with NIs contributed 28% to the explanatory power of the model, followed by chlorophyll (22%), structural parameters (LAI and LIDF_a), and chlorophyll indicators of photosynthetic efficiency. Coupling the RS model with an epidemic spread model increased the accuracy (OA = 80%; kappa = 0.48). In the almond trees where the presence of Xf was assayed by qPCR (n = 318 trees), the combined RS-spread model yielded an OA of 71% and kappa = 0.33, which is higher than the RS-only model and visual inspections (both OA = 64–65% and kappa = 0.26–31). Our work demonstrates how combining spatial epidemiological models and remote sensing can lead to highly accurate predictions of plant disease spatial distribution.

早期检测叶缘焦枯病菌( Xf ) 感染对于全世界管理这种危险的计划病原体至关重要。最近对不同尺度遥感（RS）传感器的研究表明，受 Xf感染的橄榄树在可见光和红外区域（VNIR）具有明显的光谱特征。然而，将遥感技术整合到植物病害流行管理中还需要进一步的工作。在这里，我们研究不同组 RS 植物性状（即色素、结构或叶子蛋白质含量）所检测到的光谱变化如何帮助捕获Xf传播的空间动态。我们将空间传播模型与 RS 驱动支持向量机 (RS-SVM) 模型预测的Xf感染概率相结合。此外，我们分析了哪些 RS 植物性状对预测模型的输出贡献最大。为此，在受Xf影响的杏仁园（ n = 1426 棵树）中，我们与机载活动同时进行了实地活动，以收集可见光-近红外（VNIR，400-850 nm）的高分辨率热图像和高光谱图像。 ）和短波红外区域（SWIR，950–1700 nm）。性能最佳的 RS-SVM 模型（OA = 75%；kappa = 0.50）包括作为预测因子的叶蛋白含量、氮指数 (NI)、荧光和热指示剂 (T _c )，以及色素和结构参数。叶子蛋白质含量和 NI 对模型的解释力贡献了 28%，其次是叶绿素 (22%)、结构参数（LAI 和 LIDF _a ）以及光合效率的叶绿素指标。 将 RS 模型与流行病传播模型耦合提高了准确性（OA = 80%；kappa = 0.48）。在通过 qPCR 检测Xf存在的杏树（ n = 318 棵树）中，组合 RS 传播模型产生的 OA 为 71%，kappa = 0.33，高于仅 RS 模型和目视检查（ OA = 64–65% 且 kappa = 0.26–31)。我们的工作展示了如何将空间流行病学模型与遥感相结合，从而对植物病害空间分布进行高度准确的预测。