Plant pathogens cause significant losses to agricultural yields and increasingly threaten food security¹, ecosystem integrity and societies in general^2,3,4,5. Xylella fastidiosa is one of the most dangerous plant bacteria worldwide, causing several diseases with profound impacts on agriculture and the environment⁶. Primarily occurring in the Americas, its recent discovery in Asia and Europe demonstrates that X. fastidiosa’s geographic range has broadened considerably, positioning it as a reemerging global threat that has caused socioeconomic and cultural damage^7,8. X. fastidiosa can infect more than 350 plant species worldwide⁹, and early detection is critical for its eradication⁸. In this article, we show that changes in plant functional traits retrieved from airborne imaging spectroscopy and thermography can reveal X. fastidiosa infection in olive trees before symptoms are visible. We obtained accuracies of disease detection, confirmed by quantitative polymerase chain reaction, exceeding 80% when high-resolution fluorescence quantified by three-dimensional simulations and thermal stress indicators were coupled with photosynthetic traits sensitive to rapid pigment dynamics and degradation. Moreover, we found that the visually asymptomatic trees originally scored as affected by spectral plant-trait alterations, developed X. fastidiosa symptoms at almost double the rate of the asymptomatic trees classified as not affected by remote sensing. We demonstrate that spectral plant-trait alterations caused by X. fastidiosa infection are detectable previsually at the landscape scale, a critical requirement to help eradicate some of the most devastating plant diseases worldwide.

In the present work, atmospheric mineral dust from a MACC-II short reanalysis run for 2 years (2007–2008) has been evaluated over northern Africa and the Middle East using satellite aerosol products (from MISR, MODIS and OMI satellite sensors), ground-based AERONET data, in situ PM₁₀ concentrations from AMMA, and extinction vertical profiles from two ground-based lidars and CALIOP satellite-based lidar. The MACC-II aerosol optical depth (AOD) spatial and temporal (seasonal and interannual) variability shows good agreement with those provided by satellite sensors. The capability of the model to reproduce the AOD, Ångström exponent (AE) and dust optical depth (DOD) from daily to seasonal time-scale is quantified over 26 AERONET stations located in eight geographically distinct regions by using statistical parameters. Overall DOD seasonal variation is fairly well simulated by MACC-II in all regions, although the correlation is significantly higher in dust transport regions than in dust source regions. The ability of MACC-II in reproducing dust vertical profiles has been assessed by comparing seasonal averaged extinction vertical profiles simulated by MACC-II under dust conditions with corresponding extinction profiles obtained with lidar instruments at M'Bour and Santa Cruz de Tenerife, and with CALIOP. We find a good agreement in dust layers structures and averaged extinction vertical profiles between MACC-II, the lidars and CALIOP above the marine boundary layer from 1 to 6 km. Surface dust daily mean concentrations from MACC-II reanalysis has been evaluated with daily averaged PM₁₀ at three monitoring stations of the Sahelian Dust Transect. MACC-II correctly reproduces daily to interannual surface dust concentration variability, although it underestimates daily and monthly means all year long, especially in winter and early spring (dry season). MACC-II reproduces well the dust variability recorded along the station transect which reflects the variability in dust emission by different Saharan sources, but fails in reproducing the sporadic and very strong dust events associated to mesoscale convective systems during the wet season.

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 (Tc), 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 LIDFa), 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.

More than 2 years of columnar atmospheric aerosol measurements (2006–2009) at the Tamanrasset site (22.79° N, 5.53° E, 1377 m a.s.l.), in the heart of the Sahara, are analysed. Aerosol Robotic Network (AERONET) level 2.0 data were used. The KCICLO (K is the name of a constant and ciclo means cycle in Spanish) method was applied to a part of the level 1.5 data series to improve the quality of the results. The annual variability of aerosol optical depth (AOD) and Ångström exponent (AE) has been found to be strongly linked to the convective boundary layer (CBL) thermodynamic features. The dry-cool season (autumn and winter) is characterized by a shallow CBL and very low mean turbidity (AOD ~ 0.09 at 440 nm, AE ~ 0.62). The wet-hot season (spring and summer) is dominated by high turbidity of coarse dust particles (AE ~ 0.28, AOD ~ 0.39 at 440 nm) and a deep CBL. The aerosol-type characterization shows desert mineral dust as the prevailing aerosol. Both pure Saharan dust and very clear sky conditions are observed depending on the season. However, several case studies indicate an anthropogenic fine mode contribution from the industrial areas in Libya and Algeria. The concentration weighted trajectory (CWT) source apportionment method was used to identify potential sources of air masses arriving at Tamanrasset at several heights for each season. Microphysical and optical properties and precipitable water vapour were also investigated.