In order to quickly, accurately, and nondestructively monitor leaf nitrogen content (LNC) of winter wheat in the field, the remote sensing estimation experiment of winter wheat were carried out in Taixing County of Taizhou City, Dafeng District of Yancheng City and Rugao County of Nantong City, Jiangsu Province. Based on the red band reflectance (REDref) and near-infrared band reflectance (NIRref) of winter wheat canopy and ten spectral indices (RVI, NDVI, DVI, SAVI, OSAVI, MSR, RDVI, EVI2, NLI, and SVI), a correlation analysis was performed between twelve remote sensing spectral indices and winter wheat LNC. The remote sensing spectral indices that showed good correlations with winter wheat LNC were selected as model input variables. Subsequently, the winter wheat LNC estimation model based on BP neural network was constructed, and the spatial distribution of winter wheat LNC in the county was monitored using GF-6/WFV satellite remote sensing images. The results showed that twelve remote sensing spectral indices had different degrees of correlation with winter wheat LNC, among which NDVI, RVI, MSR, OSAVI, and NLI had better correlations with winter wheat LNC (the correlation coefficient is not less than 0.65). The optimized five remote sensing spectral indicators were used as model input variables to construct a winter wheat LNC estimation model (LNC_BPEM) based on BP neural network. The estimation accuracy of the model can be illustrated: r2=0.866, RMSE=0.246%, and ARE=12.9%. By combining the LNC_BPEM estimation model and GF6/WFV image to monitor the LNC spatial information of winter wheat in Rugao county, the spatial distribution characteristics of winter wheat LNC (normal growth) were obtained, the planted area of winter wheat with LNC (normal growth) ranging from 0.9% to 2.0% in the study area was 29 693.3 hm2, which accounted for 74% of the total planted area of winter wheat. It is indicated that multiple remote sensing spectral indicators of GF-6/WFV satellite combined with neural network modeling can effectively estimate the leaf nitrogen content of winter wheat in county field. |