The nitrogen nutrition index (NNI) is a reliable indicator for diagnosing crop nitrogen (N) status. However, there is currently no specific vegetation index for the NNI inversion across multiple growth periods. To overcome the limitations of the traditional direct NNI inversion method (NNI_T1) of the vegetation index and traditional indirect NNI inversion method (NNI_T2) by inverting intermediate variables including the aboveground dry biomass (AGB) and plant N concentration (PNC), this study proposed a new NNI remote sensing index (NNI_RS). A remote-sensing-based critical N dilution curve (N_c__RS) was set up directly from two vegetation indices and then used to calculate NNI_RS. Field data including AGB, PNC, and canopy hyperspectral data were collected over four growing seasons (2012–2013 (Exp.1), 2013–2014 (Exp. 2), 2014–2015 (Exp. 3), 2015–2016 (Exp. 4)) in Beijing, China. All experimental datasets were cross-validated to each of the NNI models (NNI_T1, NNI_T2 and NNI_RS). The results showed that: (1) the NNI_RS models were represented by the standardized leaf area index determining index (sLAIDI) and the red-edge chlorophyll index (CI_{red edge}) in the form of NNI_RS=CI_{red edge}/(a×sLAIDI^b), where “a” equals 2.06, 2.10, 2.08 and 2.02 and “b” equals 0.66, 0.73, 0.67 and 0.62 when the modeling set data came from Exp.1/2/4, Exp.1/2/3, Exp.1/3/4, and Exp.2/3/4, respectively; (2) the NNI_RS models achieved better performance than the other two NNI revised methods, and the ranges of R² and RMSE were 0.50–0.82 and 0.12–0.14, respectively; (3) when the remaining data were used for verification, the NNI_RS models also showed good stability, with RMSE values of 0.09, 0.18, 0.13 and 0.10, respectively. Therefore, it is concluded that the NNI_RS method is promising for the remote assessment of crop N status.