In this paper we present a comprehensive inventory of the non‐native vascular flora of Italy, which was produced within the project “A survey of the Italian non‐native flora”, funded by the Italian Ministry for the Environment. Previously published floristic accounts were the main source of information. Historical records were critically revised and integrated with recent literature, data from herbaria and some unpublished information, so as to obtain a complete, up‐to‐date catalogue of the non‐native vascular plant species that occur spontaneously in Italy. The inventory lists 1023 non‐native species and subspecies, which account for 13.4% of all the Italian flora. The Italian non‐native flora was divided, according to its residence time, into 103 archaeophytes and 920 neophytes. According to its current invasion status, it was classified into 437 casual (42.7% of all non‐native) and 524 established taxa, the latter being divided into 361 naturalized non‐invasive (35.3%) and 163 invasive taxa (15.9%). The inventory includes a group of 62 species (6.1%) that lack recent records (i.e. since 1950). By combining local expertise into a unified, nationwide scheme using a standardized method and terminology, the inventory provides the essential scientific basis for the development of plant invasion research and management in the country.

This study investigates the performances in a terrestrial ecosystem of gross primary production (GPP) estimation of a suite of spectral vegetation indexes (VIs) that can be computed from currently orbiting platforms. Vegetation indexes were computed from near-surface field spectroscopy measurements collected using an automatic system designed for high temporal frequency acquisition of spectral measurements in the visible near-infrared region. Spectral observations were collected for two consecutive years in Italy in a subalpine grassland equipped with an eddy covariance (EC) flux tower that provides continuous measurements of net ecosystem carbon dioxide (CO₂) exchange (NEE) and the derived GPP. Different VIs were calculated based on ESA-MERIS and NASA-MODIS spectral bands and correlated with biophysical (Leaf area index, LAI; fraction of photosynthetically active radiation intercepted by green vegetation, fIPAR_g), biochemical (chlorophyll concentration) and ecophysiological (green light-use efficiency, LUE_g) canopy variables. In this study, the normalized difference vegetation index (NDVI) was the index best correlated with LAI and fIPAR_g (r = 0.90 and 0.95, respectively), the MERIS terrestrial chlorophyll index (MTCI) with leaf chlorophyll content (r = 0.91) and the photochemical reflectance index (PRI₅₅₁), computed as (R₅₃₁-R₅₅₁)/(R₅₃₁+R₅₅₁) with LUE_g (r = 0.64). Subsequently, these VIs were used to estimate GPP using different modelling solutions based on Monteith's light-use efficiency model describing the GPP as driven by the photosynthetically active radiation absorbed by green vegetation (APAR_g) and by the efficiency (ε) with which plants use the absorbed radiation to fix carbon via photosynthesis. Results show that GPP can be successfully modelled with a combination of VIs and meteorological data or VIs only. Vegetation indexes designed to be more sensitive to chlorophyll content explained most of the variability in GPP in the ecosystem investigated, characterised by a strong seasonal dynamic of GPP. Accuracy in GPP estimation slightly improves when taking into account high frequency modulations of GPP driven by incident PAR or modelling LUE_g with the PRI in model formulation. Similar results were obtained for both measured daily VIs and VIs obtained as 16-day composite time series and then downscaled from the compositing period to daily scale (resampled data). However, the use of resampled data rather than measured daily input data decreases the accuracy of the total GPP estimation on an annual basis.