Analysis of spectral signatures and vegetation indices to assess the response of faba bean plants to varying zinc levels under sandy soil conditions

Introduction Zinc (Zn) deficiency is a major limiting factor for crop productivity, particularly in sandy soils where nutrient retention and bioavailability are poor. The field experiment was conducted at the Agricultural Experimental Station, Cairo University (Giza, Egypt) during the 2022–2024 winter seasons. This study aimed to analyze the spectral responses of faba bean (Vicia faba L.) under different foliar Zn concentrations (0, 0.5, 1.0, 1.5, and 2.0 g L −1 ZnSO 4 ·7H 2 O) using a Randomized Complete Block Design (RCBD) with three replications, and to identify vegetation indices sensitive to Zn nutrition. Methods Growth traits, biochemical attributes, seed yield, hyperspectral reflectance, and soil spectral properties were integrated to establish robust vegetation–soil indicators. Results and discussion Foliar Zn application increased chlorophyll content by 24.5%, protein by 18.3%, and seed yield by 27.6% compared with the control (p ≤ 0.05). Results revealed that foliar Zn application significantly enhanced chlorophyll content, protein accumulation, soluble sugars, and seed yield, with the optimum response observed at 2.0 g L −1 . Spectral signatures demonstrated that Zn enrichment decreased red reflectance (∼670 nm), sharpened the red-edge slope (700–750 nm), and increased near-infrared reflectance (∼800 nm), indicating improved canopy structure and photosynthetic activity. Vegetation indices such as Normalized Difference Vegetation Index (NDVI), Green NDVI (GNDVI), Photochemical Reflectance Index (PRI), Normalized Difference Red-Edge Index (NDRE), Red-Edge Position (REP), and Modified Chlorophyll Absorption Ratio Index (MCARI) were highly responsive to Zn levels, showing strong correlations with physiological traits and closely aligned with growth and yield performance. Soil-based spectral indices, including the Clay Ratio Index (CRI) and the Normalized Difference Soil Index (NDSI), also differentiated among soil samples, linking soil spectral features with Zn-associated crop responses. These findings confirm that hyperspectral reflectance and derived indices provide reliable, non-destructive tools for monitoring Zn nutrition in faba bean. The integration of spectral traits with agronomic and biochemical data emphasizes the potential of hyperspectral sensing for optimizing micronutrient management in sandy soils. Moreover, the outcomes highlight the role of hyperspectral tools in precision agriculture, enabling site-specific nutrient strategies that enhance productivity, resource-use efficiency, and sustainable food security.