• leaf optical properties
• leaf trait prediction
• spectral signature analysis

Healthy vegetation supports diverse biological communities and ecosystem processes, and provides crops, ecological services, forest products, forage, and countless other benefits. Advancements in techniques capable of detecting and monitoring plant responses to environmental constraints are mandatory to increase crop yield and quality, and optimize management and input efforts to cope with growing threats, such as climate change.
The present research aims to highlight the potential of using vegetation spectroscopy for these purposes, nested in the Digital Agriculture framework. First, it briefly reports basic concepts of vegetation spectroscopy. Then, it reports the approaches for exploiting spectral data, in particular the detection and monitoring of diseases and abiotic stress conditions. Today, many are the instruments and platforms available to acquire spectroscopic data at multiple corresponding spatial, temporal and spectral scales. Numerous studies highlight the capability of spectral data to accurately detect vegetation status and monitor specific plant responses to stress conditions, even prior to the onset of visual symptoms. Furthermore, they show that vegetation spectroscopy can be a rapid, non-destructive, and relatively inexpensive tool to accurately estimate an array of leaf physiological, biochemical and morphological parameters commonly investigated to monitor plant/stress interactions, using spectral data.