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

REMOTE SENSING OF ENVIRONMENT

卷:236

Article

Hornero, A.¹  Hernandez-Clemente, R.¹  North, P. R. J.¹  Beck, P. S. A.²  Boscia, D.³  Navas-Cortes, J. A.⁴  Zarco-Tejada, P. J.^2,4,5,6 

[1] Swansea Univ, Dept Geog, Swansea SA2 8PP, W Glam, Wales

[2] EC, JRC, Directorate D Sustainable Resources, Via E Fermi 2749 TP 261,26a-043, I-21027 Ispra, VA, Italy

[3] CNR, IPSP, Via Amendola 122-D, I-70126 Bari, Italy

[4] CSIC, IAS, Ave Menendez Pidal S-N, Cordoba 14004, Spain

[5] Univ Melbourne, Fac Vet & Agr Sci, Sch Agr & Food, Melbourne, Vic, Australia

[6] Univ Melbourne, Melbourne Sch Engn, Dept Infrastruct Engn, Melbourne, Vic, Australia

关键词: Sentinel-2;    Hyperspectral;    Xylella fastidiosa;    Temporal change;    Radiative transfer;   

DOI  :  10.1016/j.rse.2019.111480

来源: Elsevier

【 摘 要 】

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 (r(VALUES)(2) > 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.

【 授权许可】

Free