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Título: | Monitoring the incidence of Xylella fastidiosa infection in olive orchards using ground-based evaluations, airborne imaging spectroscopy and Sentinel-2 time series through 3-D radiative transfer modelling |
Autor: | Hornero, Alberto CSIC ORCID; Hernández-Clemente, Rocío; North, Peter R. J.; Beck, P. S. A.; Boscia, Donato; Navas Cortés, Juan Antonio ; Zarco-Tejada, Pablo J. CSIC ORCID | Palabras clave: | Sentinel-2 Hyperspectral Xylella fastidiosa Temporal change Radiative transfer |
Fecha de publicación: | ene-2020 | Editor: | Elsevier | Citación: | Remote Sensing of Environment 236: 111480 (2020) | Resumen: | Outbreaks of Xylella fastidiosa (Xf) in Europe generate considerable economic and environmental damage, and this plant pest continues to spread. Detecting and monitoring the spatio-temporal dynamics of the disease symptoms caused by Xf at a large scale is key to curtailing its expansion and mitigating its impacts. Here, we combined 3-D radiative transfer modelling (3D-RTM), which accounts for the seasonal background variations, with passive optical satellite data to assess the spatio-temporal dynamics of Xf infections in olive orchards. We developed a 3D-RTM approach to predict Xf infection incidence in olive orchards, integrating airborne hyperspectral imagery and freely available Sentinel-2 satellite data with radiative transfer modelling and field observations. Sentinel-2A time series data collected over a two-year period were used to assess the temporal trends in Xf-infected olive orchards in the Apulia region of southern Italy. Hyperspectral images spanning the same two-year period were used for validation, along with field surveys; their high resolution also enabled the extraction of soil spectrum variations required by the 3D-RTM to account for canopy background effect. Temporal changes were validated with more than 3000 trees from 16 orchards covering a range of disease severity (DS) and disease incidence (DI) levels. Among the wide range of structural and physiological vegetation indices evaluated from Sentinel-2 imagery, the temporal variation of the Atmospherically Resistant Vegetation Index (ARVI) and Optimized Soil-Adjusted Vegetation Index (OSAVI) showed superior performance for DS and DI estimation (r2VALUES>0.7, p < 0.001). When seasonal understory changes were accounted for using modelling methods, the error of DI prediction was reduced 3-fold. Thus, we conclude that the retrieval of DI through model inversion and Sentinel-2 imagery can form the basis for operational vegetation damage monitoring worldwide. Our study highlight the value of interpreting temporal variations in model retrievals to detect anomalies in vegetation health. | Versión del editor: | http://doi.org/10.1016/j.rse.2019.111480 | URI: | http://hdl.handle.net/10261/227684 | DOI: | 10.1016/j.rse.2019.111480 | Identificadores: | doi: 10.1016/j.rse.2019.111480 issn: 0034-4257 |
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