Selection of Hyperspectral Vegetation Indices for Monitoring Yield and Physiological Response in Sweet Maize under Different Water and Nitrogen Availability

This study used hyperspectral reflectance data to evaluate the crop physiological parameters of sweet maize. Principal component analysis (PCA) was applied identify wavelengths that primarily contributed each selected PC. Correlation and multiple linear regression, with a stepwise algorithm, were select best-performing vegetation indices (VIs) for monitoring yield response maize grown under different water nitrogen availability. The spectral measurements crops taken during mid-season stage, two consecutive growing seasons. multivariate regression results showed red-edge group indices, such as CARI (Chlorophyll Absorption Reflectance Index), DD (Double Difference REIP (Red-Edge Inflection Point), Clred-edge Red-Edge) good predictors parameters, confirming crucial role region also emerged through PCA. Moreover, DD, REIP, VIs able discriminate transient temporary stress at well separate N levels. Therefore, can provide valid information growers, helping them between conditions.