Raman spectroscopic characterisation of leaves for plant species discrimination

Abstract. Spectroscopic techniques are rapidly gaining traction in remote sensing due to their ability to detect subtle biochemical and structural variations in vegetation. Raman spectroscopy, in particular, provides molecular-level insight into plant tissues by capturing vibrational modes of constituent biomolecules. This study harnessed the power of a 532 nm excitation source to meticulously obtain Raman spectra from two distinct leaf samples, with a keen focus on uncovering their spectral differences that reflect the intricate biomolecular composition of green plant tissues. The spectral profiles revealed three prominent peaks situated around 1008 cm⁻¹, 1156 cm⁻¹, and 1520 cm⁻¹, each corresponding to the vibrational modes of carotenoid molecules that play a crucial role in photosynthesis. Notably, Leaf 1 exhibited significantly higher peak intensities compared to Leaf 2, suggesting an elevated concentration of carotenoids and, implicitly, a greater photosynthetic activity. These discernible spectral differences are not merely academic; they are crucial for identifying diverse forest species and monitoring crops. Raman spectroscopy, with its capacity for swift, in situ biochemical fingerprinting, contributes to the development of comprehensive spectral libraries. Such libraries are invaluable for species classification in ecologically rich environments. In agricultural contexts, this cutting-edge technique serves as an early warning system, enabling the monitoring of crop health and the detection of nutrient deficiencies long before visible symptoms appear, thereby enhancing precision agriculture practices. From a geospatial perspective, Raman spectroscopy emerges as a critical ground-truthing tool. It provides essential molecular data used to calibrate satellite-derived vegetation indices, which in turn bolsters the accuracy of machine learning models. When integrated with Geographic Information Systems (GIS) platforms, field-portable Raman devices can generate detailed biochemical maps that facilitate multi-scale vegetation monitoring, leveraging the strengths of UAVs and satellite sensors. The choice of a 532 nm laser is pivotal, as it significantly amplifies sensitivity to carotenoids, making Raman spectroscopy exceptionally well-suited for nuanced studies of green vegetation, whether in pristine natural ecosystems or cultivated managed landscapes. Overall, this innovative technique holds immense promise for advancing vegetation monitoring efforts and supporting informed, climate-smart decision-making in environmental and agricultural management.