The nitrogen nutrition index (NNI) is calculated from the measured N concentration and the critical nitrogen (N) curve. It can be used to determine the N required by a crop and is helpful for optimizing N application in the field. Our objectives were to validate the existing corn critical N curve for the northwestern plain of Shandong Province and to design a more accurate remote detection method for the NNI. For this purpose, field measurements were conducted weekly to acquire the biomass and N concentrations during the corn growing season of 2011. Additionally, nearly 60 corn canopy spectra were collected during field campaigns. First, limiting and non-limiting N points were selected from sampled data, and they were used to validate the existing critical N curve. Second, an NNI estimation model based on a Principal Component Analysis method and Back Propagation Artificial Neural Network (PCA-BP-ANN) model was established. The collected canopy spectra and corresponding NNI were used to compare the performances of the above mentioned method and other for NNI estimation. The results showed that the N curve proposed in the literature is suitable for the study region. Among the three remote detection methods, PCA-BP-ANN provided the best results with highest R value and lowest root mean square error value.