## 4 Discussion
For example, GPP values may be influenced by the temperature and tower height. GPP values obtained by EC technology vary in quality. Therefore, when establishing the linear relationship of GOSIF, SIF, and MODIS with GPP\({}_{\text{EC}}\), inaccurate GPP\({}_{\text{EC}}\) values can also cause errors. For example, R\({}^{2}\) of GOSIF-GPP\({}_{\text{EC}}\) of US-Ro2 and US-ALQ is low. The reason of our analysis is that the accuracy of GPP\({}_{\text{EC}}\) value is low. In this study, we compared the SIF/57-GPP\({}_{\text{EC}}\), GOSIF-GPP\({}_{\text{EC}}\), and MYD-GPP\({}_{\text{EC}}\) within 25 km around each flux tower. However, SIF/57, GOSIF, and MYD have different spatial resolutions, which means that even within the same extraction range, SIF/57, GOSIF, and MYD obtain different amounts of data and the heterogeneity of pixels is different [23]. These factors also affect the accuracy of the results. Moreover, except for the uncertainty caused by the scale problem, the land cover and the quality of SIF/57 are also sources of uncertainty of GPP\({}_{\text{SIF}}\). The GOSIF data have a higher temporal and spatial resolution than the OCO-2 SIF dataset. Our results show that the GOSIF-GPP\({}_{\text{EC}}\) model is an effective method for estimating GPP. However, the seasonal cycle of GOSIF of GRA and ENF occurred earlier than GPP\({}_{\text{EC}}\). This phenomenon was also observed in the MODIS GPP dataset. To calculate the GOSIF data, a large number of EVI and PAR values from the MODIS dataset were used. In addition, the cubist regression tree model's input parameters from the MERRA-2 dataset have a coarse spatial resolution (\(0.5^{\circ}\times 0.625^{\circ}\)), and the data were resampled to \(0.05^{\circ}\)[18]. The resampling process may cause discontinuities between the datasets, creating jagged or smoothing effects, and thus leading to errors in the predictions of SIF [45]. We can conclude that inconsistent seasonal cycles of GRA and ENF between GPP estimates and GPP\({}_{\text{EC}}\) could be attributed to inaccurate data from the input model for MODIS or from the MERRA-2 data resampling. Therefore, we call for necessary improvements in the quality of input data for producing GOSIF datasets. When [PERSON] and [PERSON] developed GOSIF data, in order to improve the calculation efficiency of the model and to reduce the uncertainty of the data, only LAI, fPAR, NDVI, and the normalized difference water index (NDWI) were input into the cubist regression tree model [18]. However, change of land cover type has a great influence on the SIF value. The value of SIF varies with different vegetation types. Therefore, we suggest that land cover types should be added to the model input data to obtain more accurate GOSIF data. Based on our results, we believe that GOSIF data has great potential to estimate the GPP of different vegetation types globally. GOSIF data has a spatial resolution of 0.05 degrees and temporal resolution of eight days, which has important indicative significance for the study of the global carbon cycle using SIF data. At the same time, GOSIF data covering the whole world is of great significance to exploring the function of SIF in drought monitoring, vegetation phenology, and stress monitoring. MODIS GPP products are based on the LUE model. Many inaccurate ground parameters are input to the LUE model, resulting in errors of its GPP products [9; 46]. In addition, few land cover classifications are incorporated into the MYD17A2H algorithm. At a 500 m resolution, the MODIS GPP dataset can cause the overlapping of land cover types rather than classifying individual pixels [30]. In this study, we averaged all MYD17A2H pixels within 25 km. To some extent, it can reduce errors and smooth the MYD17A2H data. However, the average MODIS GPP may contain many land types, which could introduce additional errors. Many studies have shown that MODIS data (MOD17A2) underestimate the GPP [47]. Our study also found that MYD17A2H data underestimate the GPP.