## 5 Discussion  
The identification of winter wheat was important for yield forecasting, especially for the government county-level crop statistics [70]. Remote sensing data with a high spatial resolution, such as 10 or 30 m, was used for the extraction of the crop area. The study extracted the winter wheat area of Hengshui City from Sentinel-2 images with an overall accuracy of 93.5%. Based on the MODIS EVI data and China's Environment Satellite (HJ-1) data, [PERSON] et al. [71] extracted the corn area over a regional scale with the support vector machine algorithm and the overall accuracy was 82.17%. This result was a little lower than our result of classification. Using the support vector machine algorithm as well, [PERSON] et al. [72] obtained the classification for corn, based on the MODIS EVI data and HJ-1 images over the whole of Northeast China, and the overall accuracy was 79%, which was  
Figure 12: Validation of the estimated winter wheat yield based on the GPP1, GPP2, and ACPM models at the harvest in Hengshui City in 2017, as follows: (**a**) GPP1 model in the pure pixels of the experimental plots in exp2; (**b**) GPP1 model in the pure and mixed pixels of the experimental plots in exp2; (**c**) GPP2 model in the pure pixels of the experimental plots in exp2; (**d**) GPP2 model in the pure and mixed pixels of the experimental plots in exp2; (**e**) ACPM model in the pure pixels of the experimental plots in exp2; and (**f**) ACPM model in the pure and mixed pixels of the experimental plots in exp2; \(e\) is the error. also a little lower than our result. Our classification result was available for a distribution map of the winter wheat yield. The higher deviation of environmental factors led to the increased error of yield estimation. The goal of the newly developed MRVI was reducing the RMSE and increasing the accuracy of \(\mathrm{C_{ab}}\) estimation. The results indicated that leaf \(\mathrm{C_{ab}}\) could be estimated using MODIS MRVI data with an accuracy of 8.34 \(\mathrm{\SIUnitSymbolMicro g}\)/cm\({}^{2}\). [PERSON] et al. (2016) obtained an RMSE of 4.2 \(\mathrm{\SIUnitSymbolMicro g}\)/cm\({}^{2}\) for the leaf \(\mathrm{C_{ab}}\) estimation, using the normalized area over the reflectance curve (NAOC) from the hyperspectral data of the airborne CASI data. [PERSON] et al. (2017) reported an RMSE of 4-5 \(\mathrm{\SIUnitSymbolMicro g}\)/cm\({}^{2}\) for the leaf \(\mathrm{C_{ab}}\) estimation with different canopy structures, using the different indexes, e.g., TCI/OSAVI and MTCI from the Sentinel-2 data as well. Those results were both better than our result. However, different sensors had different inversion effects. The higher resolution of the airborne and Sentinel-2 images could improve the accuracy. Using the physical model (The REGularized canopy reFLECtance model), [PERSON] et al. (2016) obtained the higher accuracies with the RMSE of 4.4 \(\mathrm{\SIUnitSymbolMicro g}\)/cm\({}^{2}\), based on the 1 m resolution aircraft and with an RMSE of 7.1 \(\mathrm{\SIUnitSymbolMicro g}\)/cm\({}^{2}\) based on the 10 m resolution SPOT-5 imagery. However, this approach was time-consuming. [PERSON] et al. (2017) also reported an RMSE of 9.35-13.36 \(\mathrm{\SIUnitSymbolMicro g}\)/cm\({}^{2}\) for the leaf \(\mathrm{C_{ab}}\) estimation, using hyperspectral indices, which were comparable with our result. [PERSON] et al. (2017) also obtained an RMSE of 11.5 \(\mathrm{\SIUnitSymbolMicro g}\)/cm\({}^{2}\) for the leaf \(\mathrm{C_{ab}}\) estimation, using the optimized soil-adjusted vegetation index (OSAVI), and the RMSE was slightly higher than our result. Furthermore, in comparison with the existing VIs from the previous literature, listed in Table 3, the MRVI obtained a better accuracy for the leaf \(\mathrm{C_{ab}}\) estimation in exp1 at the Yucheng Station. For detecting the leaf \(\mathrm{C_{ab}}\) distribution in Hengshui City in 2017, MRVI showed better results as well. Thus, it was proper that the N stress of the winter wheat was detected using a newly developed MRVI for the GPP estimation.