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.

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.