Camino C, Calderón R, Parnell S, Dierkes H, Chemin Y, Román-Écija M, Montes-Borrego M, Landa B B, Navas-Cortes J A, Zarco-Tejada P J, Beck P S A
European Commission (EC), Joint Research Centre (JRC), Ispra, Italy.
School of Environment and Life Sciences, University of Salford, Manchester, United Kingdom.
Remote Sens Environ. 2021 Jul;260:112420. doi: 10.1016/j.rse.2021.112420.
The early detection of () 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 -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 spread. We coupled a spatial spread model with the probability of 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 ( = 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), 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), 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 was assayed by qPCR ( = 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.
（）感染的早期检测对于在全球范围内管理这种危险的病原体至关重要。最近在不同尺度上使用遥感（RS）传感器进行的研究表明，感染（病原体）的橄榄树在可见光和红外区域（VNIR）具有独特的光谱特征。然而，需要进一步开展工作将遥感技术整合到植物病害流行的管理中。在此，我们研究不同组RS植物特征（即色素、结构或叶片蛋白质含量）所检测到的光谱变化如何有助于捕捉（病原体）传播的空间动态。我们将空间传播模型与由RS驱动的支持向量机（RS-SVM）模型预测的感染概率相结合。此外，我们分析了哪些RS植物特征对预测模型的输出贡献最大。为此，在受（病原体）影响的杏仁果园（n = 1426棵树）中，我们在进行空中作业的同时开展了实地活动，以收集可见光-近红外（VNIR，400 - 850 nm）和短波红外区域（SWIR，950 - 1700 nm）的高分辨率热图像和高光谱图像。表现最佳的RS-SVM模型（OA = 75%；kappa = 0.50）将叶片蛋白质含量、氮指数（NIs）、荧光和一个热指标（T）作为预测因子，同时还包括色素和结构参数。叶片蛋白质含量与氮指数对模型的解释力贡献了28%，其次是叶绿素（22%）、结构参数（叶面积指数和叶倾角分布函数）以及光合效率的叶绿素指标。将RS模型与流行病传播模型相结合提高了准确性（OA = 80%；kappa = 0.48）。在通过qPCR检测（病原体）存在情况的杏仁树（n = 318棵树）中，RS-传播组合模型的OA为71%，kappa = 0.33高于仅使用RS模型和目视检查（OA均为64 - 65%，kappa为0.26 - 31）。我们的工作展示了将空间流行病学模型与遥感相结合如何能够对植物病害的空间分布进行高度准确的预测。
