## 4 Discussion
### Limitations  
The limitations and uncertainties of the results in this study were highly influenced by the quality of the GPP datasets. The MODIS GPP produces were based on the LUE model. The LUE model relied on many types of ground parameter. Many inaccurate parameters were inputted into the LUE model, which may lead to errors in GPP products [40]. The GPP products obtained by the eddy covariance technique (EC, GPP flux tower) were considered as the most accurate GPP data [60]. However, the sampling area of the flux tower was small (<1 km\({}^{2}\)) and its distribution was not uniform, making it difficult to conduct a large-scale evaluation. The mismatch between the observation range of the flux-tower site and the monitoring range of the remote-sensing data was another important reason for the limitation of the GPP flux tower [36]. As more high-resolution satellites are launched and flux-tower data become available, more reliable GPP produces will be obtained in the future to eliminate uncertainties. Therefore, we propose to improve the LUE model or control the quality of the input parameters to improve the accuracy of the MODIS GPP produces. The more precise and reliable GPP products can be used in future research for further detailed analyses and to verify our results.  
The spatial resolution and time-series length of the SIF limit its application. GOSIF (0.05\({}^{\circ}\)) provides better spatial resolution than other SIF products, providing an unprecedented opportunity to explore the estimation of GPP on large scale. In this study, we explored the relationship between the GOSIF and the GPP/per-unit grain yield among different land-cover types and in different seasons. Based on our results, we believe that GOSIFs have great potential in GPP monitoring and per-unit grain-yield monitoring around the world. However, GOSIF also involves uncertainty, including over meteorological re-analysis data, the quality of OCO-2 SIF, the biases in the enhanced vegetation index, and imperfect modeling methods [36; 37]. There are currently no satellites dedicated to SIF observations. Therefore, it is necessary to obtain more accurate SIF data sets in the future. In the future, the availability of long-term SIF observations will help us to better understand the differences between the SIF and the vegetation index (such as NDVI) in reflecting GPP/per-unit grain yield, and improve our understanding of the function of ecosystems under climate change.  
In addition, our study only used a correlation analysis to study the relationship between the SIF/NDVI and the per-unit grain yield, and described per-unit grain-yield changes in drought years. Although we provided a link between the SIF/NDVI and per-unit grain yield, further analysis needs to be combined with biophysical models to reveal the potential mechanisms of drought affecting grain yields. Moreover, the responses of different crops to drought are not the same due to the differences in photosynthetic capacity, metabolic capacity, and the required growing periods. This study only briefly analyzed the application of NDVI and SIF in yield monitoring. Future studies can use a drought event as an example to analyze the effects of drought on specific crops (such as rice, corn, and wheat).