A bridge between soil science and photonics: a novel framework for urban green space assessment

Urban green spaces mitigate key urbanization impacts such as pollution and climate change exacerbation; although optical technologies have predominantly focused on documenting plant stress responses to atmospheric drivers, the critical role of soil quality in mediating vegetation's physiological adaptations - spanning both primary, and secondary metabolism - remains understudied. The work develops an integrated methodology combining portable photonic tools (an NDVI sensor, a Dualex analyzer, and a Raman spectrometer) with soil analysis to assess Quercus Ilex L. metabolic status in the urban, periurban and rural areas. Our approach cross-validates optical measurements of physiological changes with soil variable analysis, enabling comprehensive plant health assessment. Preliminary results demonstrate significant soil-plant physiological correlations across both biometric and photonic parameters. The combined analysis enhances data robustness, confirming photonic measurements through soil diagnostics. This dual-method approach addresses a critical research gap in urban ecology by synergizing optical monitoring with edaphic factor analysis. The methodology provides deeper insight into urban ecosystems where plants face complex, unregulated stressors. Key findings include that soil quality significantly influences photonic-derived physiological indicators, optical measurements gain reliability when correlated with soil parameters, and urban vegetation management requires to be integrated soil-plant monitoring. This study provides insights to guide the implementation of photon-based tools in urban green space management. The integrated approach supports evidence-based policies for sustainable urban resilience, optimizing vegetation maintenance through combined optical and soil diagnostics. Our methodology offers a practical framework for monitoring urban tree status under multiple stress factors while maintaining scientific rigor in data collection and interpretation.