Abstract: 【Objective】 To investigate the effects of different water and nitrogen treatments on multispectral collection of leaf information from spring maize. 【Methods】 Three levels of irrigation quotas (75%, 100%, 125% ETc) and four levels of N application (0, 200, 400, 600 kg/hm²) were set, Ground-based multispectral photography was used to obtain spectral information of spring maize leaves, and five vegetation indices were selected to study the effects of different water and nitrogen treatments on multispectral information acquisition. Combined with the measured data, the BP neural network with correlation analysis and particle swarm optimization was used to analyze the trend of the measured values and vegetation index. 【Results】 The results showed that the inversion of vegetation index NDVI was better for SPAD values at the middle of vegetation development, and both irrigation and nitrogen application affected the inversion of vegetation index for SPAD values. The inversions of vegetation indices OSAVI and SAVI for surface soil moisture under medium irrigation treatment (W₂) were superior, and the PSO-BP neural network modeling results of OSAVI with soil moisture data from 0 to 20 cm were better than those of SAVI for soil moisture from 10 to 30 cm. 【Conclusion】 In summary, it is more accurate to use NDVI with SOAVI for inversion of SPAD values and soil moisture at the surface 0-20 cm at 100% ETc irrigation level and above 400 kg/hm² N application level.