Aridity, which is increasing worldwide because of climate change, affects the structure and functioning of dryland ecosystems. Whether aridification leads to gradual (versus abrupt) and systemic (versus specific) ecosystem changes is largely unknown. We investigated how 20 structural and functional ecosystem attributes respond to aridity in global drylands. Aridification led to systemic and abrupt changes in multiple ecosystem attributes. These changes occurred sequentially in three phases characterized by abrupt decays in plant productivity, soil fertility, and plant cover and richness at aridity values of 0.54, 0.7, and 0.8, respectively. More than 20% of the terrestrial surface will cross one or several of these thresholds by 2100, which calls for immediate actions to minimize the negative impacts of aridification on essential ecosystem services for the more than 2 billion people living in drylands.

Plant pathogens cause significant losses to agricultural yields and increasingly threaten food security, ecosystem integrity and societies in general. Xylella fastidiosa is one of the most dangerous plant bacteria worldwide, causing several diseases with profound impacts on agriculture and the environment. 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. X. fastidiosa can infect more than 350 plant species worldwide, and early detection is critical for its eradication. 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.

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With the advent of Sentinel-2, it is now possible to generate large-scale chlorophyll content maps with unprecedented spatial and temporal resolution, suitable for monitoring ecological processes such as vegetative stress and/or decline. However methodological gaps exist for adapting this technology to heterogeneous natural vegetation and for transferring it among vegetation species or plan functional types. In this study, we investigated the use of Sentinel-2A imagery for estimating needle chlorophyll (C a+b ) in a sparse pine forest undergoing significant needle loss and tree mortality. Sentinel-2A scenes were acquired under two extreme viewing geometries (June vs. December 2016) coincident with the acquisition of high-spatial resolution hyperspectral imagery, and field measurements of needle chlorophyll content and crown leaf area index. Using the high-resolution hyperspectral scenes acquired over 61 validation sites we found the CI chlorophyll index R 750 /R 710 and Macc index (which uses spectral bands centered at 680 nm, 710 nm and 780 nm) had the strongest relationship with needle chlorophyll content from individual tree crowns (r 2 = 0.61 and r 2 = 0.59, respectively; p < 0.001), while TCARI and TCARI/OSAVI, originally designed for uniform agricultural canopies, did not perform as well (r 2 = 0.21 and r 2 = 0.01, respectively). Using lower-resolution Sentinel-2A data validated against hyperspectral estimates and ground truth needle chlorophyll content, the red-edge index CI and the Sentinel-specific chlorophyll indices CI-Gitelson, NDRE1 and NDRE2 had the highest accuracy (with r 2 values >0.7 for June and >0.4 for December; p < 0.001). The retrieval of needle chlorophyll content from the entire Sentinel-2A bandset using the radiative transfer model INFORM yielded r 2 = 0.71 (RMSE = 8.1 μg/cm 2 ) for June, r 2 = 0.42 (RMSE = 12.2 μg/cm 2 ) for December, and r 2 = 0.6 (RMSE = 10.5 μg/cm 2 ) as overall performance using the June and December datasets together. This study demonstrates the retrieval of leaf C a+b with Sentinel-2A imagery by red-edge indices and by an inversion method based on a hybrid canopy reflectance model that accounts for tree density, background and shadow components common in sparse forest canopies.

The outbreaks of Xylella fastidiosa (Xf) in Europe are generating considerable economic and environmental damage, and the spread of this plant pest appears to continue. Detecting and monitoring the spatio-temporal dynamics of the symptoms of diseases caused by Xf at large scales is key to curtailing its expansion or mitigating its impacts. This study evaluates the temporal series of airborne hyperspectral and Sentinel-2 satellite images for monitoring Xf infection incidence in olive orchards integrating satellite and airborne data with radiative transfer modelling and field observations. We used time-series of Sentinel-2A images collected over a two-year period to assess the temporal trends of Xf-infected olive orchards located in the region of Apulia, Southern Italy. First, we evaluated the sensitivity of different physiological and structural vegetation indices (VIs) to the severity and incidence of Xf-induced disease observed in situ. The same relationships were then evaluated using a 3D radiative transfer model to account for the temporal variations of canopy structure, understory and soil background that affect the spectral reflectance of Sentinel-2 over a grid-planted orchard. Hyperspectral images, spanning the same 2-year period as the Sentinel-2 data collected in the Xf-infected zone in Italy, were used for validation along with field surveys comprising more than 3000 trees across disease severity (DS) classes in 16 orchards, with varying disease-incidence (DI) levels. Among a 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 2 ARVI=0.74, p<0.001).We estimated the difference in the spectral reflectance within each plot between 2016 and 2017 based on the VIs calculated from model simulations accounting for the temporal variations of the understory which confirm its impact, showing a Root Mean Square Error (RMSE) three times 2 lower than without temporal understory changes simulated. This analysis demonstrates the benefit of combining 3-D radiative transfer modelling accounting for the background variations with Sentinel-2 data to assess the spatio-temporal dynamics of Xf infections in olive orchards.The systematic retrieval of DI through model inversion and Sentinel-2 imagery can form the basis for operational damage monitoring worldwide. Furthermore, interpreting temporal variations of model retrievals is a critical step to detect anomalies in vegetation health.