## 4 Discussion  
Recent studies have shown that satellite-based SIF retrieval is a powerful method for monitoring vegetation dynamics and estimating GPP at regional and global scales, and the findings of these studies indicated that SIF-based GPP models perform better than the traditional state-of-the-art carbon cycle models in cropland, grassland, and forests [24; 31; 33; 35; 55; 61]. However, comparative studies using SIF obtained from OCO-2 SIF and GOME-2 to estimate GPP for multiple cropping and grass areas were insufficient in previous studies, especially for cold and arid areas of China. In this study, we evaluated the performance of SIF obtained by OCO-2 and GOME-2 for GPP estimation in the Heihe River Basin, China. We demonstrated that OCO-2 SIF\({}_{757}\), SIF\({}_{771}\), GOME-2 SIF\({}_{740}\), RSIF, and APAR captured seasonal dynamics well in maize and alpine meadow, especially RSIF, APAR, and SIF\({}_{757}\). Similarly, GPP\({}_{\text{-}}\)VPM and GPP\({}_{\text{-}}\)RSIF had strong relationships with GPP\({}_{\text{-}}\)EC and they performed best in predicting GPP, followed by GPP\({}_{\text{-}}\)SIF\({}_{757}\) model, then GPP\({}_{\text{-}}\)SIF\({}_{771}\) and GPP\({}_{\text{-}}\)SIF\({}_{740}\). The GOME-2 observations used in our study were noisy, they were collected at various viewing angles, under various cloud conditions, and at different locations. RSIF was developed based on MODIS reflectance inputs, and it has a large improvement than the original GOME-2 SIF. As the MODIS data are less noisy than the original GOME-2 product, the far-red GOME-2 SIF largely reflect changes in APAR absorbed by chlorophyll. Figure 10: Relationships of GOME-2 SIF with APAR and the product of APAR with two environmental scalars (APAR \(\times\) T\({}_{\text{scalar}}\)\(\times\) W\({}_{\text{scalar}}\)) at a 16-d averaged timescale: (a) GOME-2 SIF versus APAR at the Daman site; (b) GOME-2 SIF versus APAR with two environmental scalars at the Daman site; (c) GOME-2 SIF versus APAR at the A’rou site; (d) GOME-2 SIF versus APAR with two environmental scalars at the A’rou site. Additionally, if only the single OCO-2 observation closest to the tower was used for comparison, it would probably be similarly noisy; if the OCO-2 SIF observations were spatially averaged, the average values would result in the degradation of the original resolution and the reduction of noise. Therefore, there is always a tradeoff with satellite data, e.g., spatial vs. spectral resolution or spatial vs. temporal resolution. GPP_VPM performed very well, because VPM was calculated from APAR, which is the product of fPAR and PAR, and fPAR accounted for a nonzero background and provided a mean value of scaling the otherwise unquantified EVI [11; 62]. Moreover, most parameters of VPM covered the total area of the 0.5\({}^{\circ}\) grid cell; OCO-2 SIF\({}_{757}\) and SIF\({}_{771}\) covered narrow strips, which only partially captured the signal of the grid cell, and the GOME-2 pixels covered the majority of the 0.5\({}^{\circ}\)grid cell. In previous studies, GOME-2 SIF retrievals were averaged to 8-d, 16-d, or monthly under the condition that at least five SIF retrievals were available [24; 33; 42; 63]. GOME-2 SIF performed well in GPP estimation; this strong performance was further confirmed in our study. Compared to OCO-2 average, GOME-2 SIF average (16-d) exhibited a general performance in GPP estimation, due to the coarse spatial resolution of the data. The spatial resolution and quality of the OCO-2 SIF data are greater than GOME-2 SIF data [44; 64]. The correlation coefficients between GPP predicted by the SIF (SIF\({}_{757}\), SIF\({}_{771}\), SIF\({}_{740}\), and RSIF) and tower GPP, and GPP predicted by VPM model and tower GPP were greater at the A'rou site than at the Daman site. Because Daman is more heterogeneous than A'rou, and the ecosystem in Daman is highly heterogeneous at spatial scales larger than 10 km [65; 66; 37].