## 4 Results  
The corresponding leaf, TN, and canopy spectrum with different N levels were analyzed during the growth period of the winter wheat in exp1, as well as the accuracy of the MRVI for detecting the leaf C\({}_{\text{ab}}\) were validated in exp1 and exp2, respectively. The GPP estimation models were run for each winter wheat pixel (1 km) in Hengshui City in 2017. The situ yield and DAM, with the estimated values in the corresponding pixel, were compared in exp2 and the accuracy of the models for the yield estimation was validated.  
Variation of Leaf Chlorophyll Content (C\({}_{\text{ab}}\)), Total Nitrogen Content (TN), and Canopy Spectrum at Different N Level  
As shown in Figure 4, the winter wheat leaf C\({}_{\text{ab}}\) exhibited a significant linear relationship with the leaf TN distribution, with a high correlation coefficient of 0.8 in exp1. It demonstrated that the variation of the leaf C\({}_{\text{ab}}\) that exposed the winter wheat leaf TN trend during the whole winter wheat growth period.  
The variation of the winter wheat canopy reflectance with wavelength at a different date and with different N levels is shown in Figure 5a-d. The variation of leaf C\({}_{\text{ab}}\) with time at different N levels was shown in Figure 5e. As shown in Figure 5, the winter wheat canopy reflectance and leaf C\({}_{\text{ab}}\) all varied with different N rates during the winter wheat growth period, especially at the NIR band. The reflectance at the NIR band obviously increased with the increase of the N level, but the difference of the reflectances at the NIR band gradually decreased with N level >140 kg N/ha. Furthermore, the leaf C\({}_{\text{ab}}\) also increased with the increase of the N level, and the difference of the leaf C\({}_{\text{ab}}\) under different treatments was still obvious at 140-280 kg N/ha.  
The variation of the winter wheat canopy reflectance with the leaf C\({}_{\text{ab}}\) is shown in Figure 6. Linear correlation analyses were conducted and the correlation coefficients were significant (\(p\) < 0.01), shown in Figure 6a-f. As shown in Figures 5 and 6b, the reflectance at the NIR band was the most sensitive to different N levels and the variation of C\({}_{\text{ab}}\), with the highest R\({}^{2}\) being 0.49 and lowest RMSE being 9.47 \(\text{\mu g}\)/cm\({}^{2}\). The reflectance at the red and green bands were more sensitive to the variation of C\({}_{\text{ab}}\), but tended to change little when the C\({}_{\text{ab}}\) was larger than 40 \(\text{\mu g}\)/cm\({}^{2}\) (Figure 6a,d). The reflectance at the blue band was lower and tended to change only a little as well, when C\({}_{\text{ab}}\) was larger than 40 \(\text{\mu g}\)/cm\({}^{2}\). For highlighting the difference of the red, blue, and green bands, we used the mathematical transformation. As shown in Figure 6e-h, the linear relationships between \(\rho_{\text{green}}/\left(\rho_{\text{green}}-\rho_{\text{blue}}\right)^{2}\), \(\rho_{\text{blue}}/\left(\rho_{\text{green}}-\rho_{\text{blue}}\right)^{2}\) and the leaf C\({}_{\text{ab}}\) were higher with the R\({}^{2}\) of > 0.5. By contrast, the \(\rho_{\text{red}}/\left(\rho_{\text{red}}-\rho_{\text{green}}\right)^{2}\) and \(\rho_{\text{green}}/\left(\rho_{\text{red}}-\rho_{\text{green}}\right)^{2}\) had several outliers because the lower reflectance in the red band and the lower R\({}^{2}\) of <0.2 were obtained (\(p\) > 0.05). Furthermore, \(\rho_{\text{blue}}/\left(\rho_{\text{green}}-\rho_{\text{blue}}\right)^{2}\) showed a lower dispersion than \(\rho_{\text{green}}/\left(\rho_{\text{green}}-\rho_{\text{blue}}\right)^{2}\), with the RMSE of 7.97. The new vegetation index served the new algorithms of the GPP estimation. The lower dispersion of results, the better. Thus, we used the NIR band and \(\rho_{\text{blue}}/\left(\rho_{\text{green}}-\rho_{\text{blue}}\right)^{2}\) to develop the new vegetation index (MRVI) for monitoring the C\({}_{\text{ab}}\) of winter wheat.