Effects of Various Levels of Water Stress on Morpho-Physiological Traits and Spectral Reflectance of Maize at Seedling Growth Stage

Water stress (drought and waterlogging) is one highly important factor affecting food security in China. Investigating the effects of soil moisture stress on the morphological and physiological characteristics of maize seedlings is crucial for ensuring food production. The use of spectral monitoring to observe crop phenotypic traits and assess crop health has become a focal point in field crop research. However, studies exploring the contribution of crop phenotypic and physiological data to the Normalized Difference Vegetation Index (NDVI) are still limited. In this study, a 35-day pot experiment was conducted with seven soil moisture gradients: 50%, 60%, 70%, 80% (control group, CK), 90%, 100%, and 110% treatment. In order to investigate the effects of soil moisture stress on seedling phenotypes, antioxidant enzyme activities, and NDVI, an ASD FieldSpec 4 Hi-Res NG portable spectrometer was used to collect spectral data from maize (Zea mays L. B73) leaves. The contributions of maize phenotypic and physiological traits to NDVI were also examined. The results indicated that (1) the 50% and 110% treatments significantly affected maize seedling phenotypes compared to the CK group; (2) the activities of superoxide dismutase (SOD) and peroxidase (POD) in the leaves increased under water stress, while the activities of glutathione peroxidase (GSH-PX) and ascorbate peroxidase (APX) decreased; (3) soil moisture stress (drought and waterlogging) reduced photosynthetic pigments, chlorophyll content (SPAD), and NDVI, with inhibitory effects intensifying as the stress level increased; (4) Redundancy analysis showed that antioxidant enzymes explained 69.87% of the variation in seedling height, leaf area, and NDVI. Soil moisture stress, chlorophyll, and SPAD explained 58.14% of the variation in these parameters. The results demonstrated that maize seedlings were highly sensitive to soil moisture changes, and the SPAD value contributed significantly to NDVI (p < 0.01). This study provides valuable insights for future research in precision agriculture management