## 4 Discussion
### Limitation and Uncertainty  
In this study, we determined that predicting autumn crop production while using OCO-2 SIF product is feasible and not inferior to MODIS VIs, especially, the performance of SIF757 is best. However, some uncertainties and limitations remain. First of all, the SIF values of each province were obtained by point averaging due to the discrete of OCO-2 SIF product. Moreover, in some provinces, few data passed by their cropland and the mean pixel value of SIF product may not represent the SIF value of the entire cropland. This condition may lead to the inaccuracy of the SIF values. Figure 4 shows the higher the percentage of cropland covered by SIF data, the higher accuracy. This further illustrates that the discrete influences the estimation accuracy. Although the OCO-2 SIF product that we use has the highest spatial resolution available today, it is still relatively coarse. The coarse spatial resolution indicates that the SIF value of each pixel is a mixture of cropland and other vegetation types. The SIF value we used is inaccuracy, which leads to an inaccurate estimation. We found that the accuracy of autumn crop prediction is higher in low-elevation provinces with large farm size and flat cropland by comparing the spatial distribution of Chinese cropland topography with the deviation percentage of autumn crop production estimated using SIF757 (Figure 4) (e.g., Anhui, Hebei, Henan, and so on). This is also affected by the coarse spatial resolution. Moreover, the production of most provinces is between 500 and 4000 ten kilotons, which leads to higher estimation accuracy in these provinces. In addition, the spatial distribution of autumn crops is quite important for accurately estimating the crop yield. However, the data is lacked until now, and it remains hard to distinguish the different crop types using satellite remote sensing for a large area. Therefore, this study mixed all the autumn crops together. Actually, the relationships between SIF and crop production may vary among the different autumn crops due to the influence of the canopy structure, photosynthetic pathways, light energy utilization rate, and C-N carbohydrate conversion capacity, which may constrain the accuracy in estimating autumn crop yields. Besides, the study period (two years) is short due to the limitation of SIF product and statistical data, which may lead to inaccurate results. we still believed that the results were significant since the fitted model with p-values lower than 0.01.  
The results can be generalized to other regions and crops, due to the similarity of the SIF generation process. SIF is generated during the photosynthesis process, regardless of the region or crop, which provides a possibility for SIF to estimate crop production. More and more researchers pay attention to satellite-derived SIF, which promotes the development of satellite-derived SIF. New or future missions (e.g., GOSIIF; Tropospheric Monitoring Instrument, TROPOMI; Fluorescence Explorer, FLEX) with finer spatial resolution and continuous data could be used in a similar study of crop estimation.  
If possible, our future studies will be conducted in a larger region and more crops with considering more factors.