The leaf area index (LAI) is a direct indicator of vegetation activity, and its relationship with the normalized difference vegetation index (NDVI) has been investigated in many research studies. Remote sensing makes available NDVI data over large areas, and researchers developed specific equations to derive the LAI from the NDVI, using empirical relationships grounded in field data collection. We conducted a literature search using “NDVI” AND “LAI” AND “crop” as the search string, focusing on the period 2017–2021. We reviewed the available equations to convert the NDVI into the LAI, aiming at (i) exploring the fields of application of an NDVI-based LAI, (ii) characterizing the mathematical relationships between the NDVI and LAI in the available equations, (iii) creating a software library with the retrieved methods, and (iv) releasing a publicly available software as a service, implementing these equations to foster their reuse by third parties. The literature search yielded 92 articles since 2017, where 139 equations were proposed. We analyzed the mathematical form of both the single equations and ensembles of the NDVI to LAI conversion methods, specific for crop, sensor, and biome. The characterization of the functions highlighted two main constraints when developing an NDVI-LAI conversion function: environmental conditions (i.e., water and light resource, land cover, and climate) and the availability of recurring data during the growing season. We found that the trend of an NDVI-LAI function is usually driven by the ecosystem water availability for the crop rather than by the crop type itself, as well as by the data availability; the data should be adequate in terms of the sample size and temporal resolution for reliably representing the phenomenon under investigation. Our study demonstrated that the choice of the NDVI-LAI equation (or ensemble of equations) should be driven by the trade-off between the scale of the investigation and data availability. The implementation of an extensible and reusable software library publicly queryable via API represents a valid mean to assist researchers in choosing the most suitable equations to perform an NDVI-LAI conversion.

Grapevine simulation models are mostly used to estimate plant development, growth and yield at plot scale. However, the spatial variability of pedologic and micro-climatic conditions can influence vine growth, leading to a sub-field heterogeneity in plant vigor and final yield that may be better estimated through the assimilation of high spatial resolution data in crop models. In this study, the spatial variability of grapevine intercepted radiation at fruit-set was used as input for a grapevine simulation model to estimate the variability in biomass accumulation and yield in two Tuscan vineyards (Sites A and B). In Site A, the model, forced with intercepted radiation data as derived from the leaf area index (LAI), measured at canopy level in three main vigor areas of the vineyard, provided a satisfactory simulation of the final pruning weight (r² = 0.61; RMSE = 19.86 dry matter g m⁻²). In Site B, Normalized Difference Vegetation Index (NDVI) from Sentinel-2A images was firstly re-scaled to account for canopy fraction cover over the study areas and then used as a proxy for grapevine intercepted radiation for each single pixel. These data were used to drive the grapevine simulation model accounting for spatial variability of plant vigor to reproduce yield variability at pixel scale (r² = 0.47; RMSE = 75.52 dry matter g m⁻²). This study represents the first step towards the realization of a decision tool supporting winegrowers in the selection of the most appropriate agronomic practices for reducing the vine vigor and yield variability at sub-field level.

Giant reed is a promising perennial grass providing ligno-cellulosic biomass suitable to be cultivated in marginal lands (MLs) and converted into several forms of renewable energy. This study investigates how much energy, in the form of biomethane, bioethanol, and combustible solid, can be obtained by the cultivation of this species in marginal land of two Italian regions, via the spatially explicit application of the Arungro crop model. Arungro was calibrated in both rainfed/well-irrigated systems, under non-limiting conditions for nutrient availability. The model was then linked to a georeferenced database, with data on (i) current/future climate, (ii) agro-management, (iii) soil physics/hydrology, (iv) land marginality, and (v) crop suitability to environment. Simulations were run at 500 × 500 m spatial resolution in MLs of Catania (CT, Southern Italy) and Bologna (BO, Northern Italy) provinces, characterized by contrasting pedo-climates. At field scale, Arungro explained 85% of the year-to-year variability of measured carbon accumulation in aerial biomass. At the provincial level, simulated energy yields progressively increased from bioethanol, to biomethane, and finally to combustible solid, with average values of 92-115-264 GJ ha⁻¹ in BO and 105-133-304 GJ ha⁻¹ in CT. Mean energy yields estimated for 2030 remained unchanged compared to the baseline, although showing large heterogeneity across the study area (changes between −6/+15% in BO and −16/+15% in CT). This study provides site-specific indications on giant reed current productions, energy yields, and natural water consumption, as well as on their future trends and stability, ready-to-use for multiple stakeholders of the agricultural sector involved in bioenergy planning.

Practical strategies for bioenergy planning in the face of climate change should rely on ready‐to‐use yield projections. Perennial grasses grown in marginal lands (MLs) provide abundant feedstocks to be converted into different energy vectors. The aim of this study was to provide a model‐based assessment of how much energy, in the form of biomethane and bioethanol, can be achieved by Miscanthus and giant reed across Italy. Marginal lands were here conceived as low profitable non‐irrigated areas, without mechanization and/or nature conservation constraints. Marginal lands eligible for simulations were selected crossing environmental factors and ecological requirements of the two crops. The biophysical model Arungro was calibrated considering rainfed/full‐irrigated systems using multiple‐site and multiple‐year datasets. The model was connected to a georeferenced database, with information on (i) current/future climate, (ii) agronomic practices, (iii) soil physics/hydrology, (iv) MLs, and (v) crop suitability to environment and simulations were performed at 500 × 500 m spatial resolution across all Italian regions. Under baseline conditions (i.e., 1981–2010), the total area of MLs available for energy crops (i.e., 49,100 km²) allowed to obtain 23,500 (giant reed) and 23,700 (Miscanthus) Giga‐m³ CH4‐STP of biomethane and 18,600 (giant reed) and 24,400 (Miscanthus) Giga‐liters of bioethanol. While the amount of energy carriers is expected to increase, on average, of +4.6% in 2055 and + 0.4% (mean of +9.2%—South, −2.4%—Center, −5.4%—North Italy) in 2085 for Miscanthus, giant reed‐based productions are projected to be more stable across the country and time frames (+6.7% in 2055; +2.8% in 2085). This study contributed to define a modular and detailed procedure aimed at quantifying attainable energy yields from bioenergy grasses in MLs. The consideration of fine‐resolution multiple‐scale heterogeneity allowed for an in‐depth investigation of biomass productivity, attainable energy yields, and related stability under current/climate change scenarios, highlighting critical spots and opportunities within the country.