## 2 Materials and Methods
### Methodology  
Firstly, we established linear relationships between GPP\({}_{\mathrm{EC}}\) and three remotely sensed products, namely OCO-2 SIF products, MYD17A2H GPP (MYD), and GOSIF datasets at a single tower. As stated in Section 2.1, the spectra of two bands can be used to retrieve SIF products. Previous studies have indicated that SIF757 products are superior to SIF771 products in estimating GPP [13]. Consequently, we utilized SIF757 products to correlate with GPP\({}_{\mathrm{EC}}\) at different flux towers at a daily timescale. Before correlating GPP\({}_{\mathrm{EC}}\) with remotely sensed products, it is necessary to ensure their spatial and temporal consistencies. The date of available OCO-2 SIF products was used to screen daily GPP\({}_{\mathrm{EC}}\) measurements. Eight-day MYD and GOSIF data were interpolated into daily data using linear interpolation. Then, temporal matching was conducted with respect to the date. Regarding the spatial registration, it is necessary to use an extraction range to determine which data can be included in experiments. Remotely sensed products located in cycles, of which the centers are flux towers and the radius is the extraction range, were screened to correlate with GPP\({}_{\mathrm{EC}}\). Theoretically, a unified extraction range should be applied to different remotely sensed products to ensure fair inter-comparison experiments. However, spatial resolutions of OCO-2 SIF products, MYD17A2H GPP (MYD), and GOSIF datasets are different. The pixel heterogeneity of GOSIF and MYD data with finer spatial resolution will increase if the same extraction range is used. The amount of three kinds of data will directly affect the evaluation results of GPP. To ensure there are adequate data to establish robust relationships between GPP\({}_{\mathrm{EC}}\) and different remotely sensed products, we used different extraction ranges for three remotely sensed products. Besides, additional experiments using a unified extraction range are presented in the discussion to explore the effect of the extraction range. Owing to the sampling strategy and the retrieving method of OCO-2, there are many gaps and blanks in its SIF products. We thus utilized a large extraction range (25 km) to extract OCO-2 SIF products of nadir glint and target mode. Both MYD17A2H GPP (MYD) and GOSIF provide areal products with a full coverage over our study area. The spatial resolutions of MYD and GOSIF are 500 m and 0.05\({}^{\circ}\)(-5 km), respectively. Therefore, extraction ranges were set to 5 km and 0.1\({}^{\circ}\) for MYD and GOSIF, respectively. Finally, linear relationships between GPP\({}_{\mathrm{EC}}\) and three remotely sensed products were then established and were named SIF757- GPP\({}_{\mathrm{EC}}\), MYD-GPP\({}_{\mathrm{EC}}\), and GOSIF-GPP\({}_{\mathrm{EC}}\). We used the determination coefficient (R\({}^{2}\)) to evaluate the relationships between SIF757- GPP\({}_{\mathrm{EC}}\), MYD-GPP\({}_{\mathrm{EC}}\), and GOSIF-GPP\({}_{\mathrm{EC}}\). The closer R\({}^{2}\) is to 1, the better the ability of remotely sensed products to estimate GPP\({}_{\mathrm{EC}}\) is. Moreover, we used SIF757-GPP\({}_{\mathrm{EC}}\), GOSIF-GPP\({}_{\mathrm{EC}}\), and MYD-GPP\({}_{\mathrm{EC}}\) models to generate estimates of GPP for each vegetation type, abbreviated as GPP\({}_{\mathrm{SIF}}\), GPP\({}_{\mathrm{GOSIF}}\), and GPP\({}_{\mathrm{MYD}}\), respectively. Finally, we calculated the root mean square error (RMSE) of GPP\({}_{\mathrm{SIF}}\), GPP\({}_{\mathrm{GOSIF}}\), and GPP\({}_{\mathrm{MYD}}\) for different vegetation types. For each vegetation type, we analyzed the consistency of the seasonal cycle of the three GPP estimates and GPP\({}_{\mathrm{EC}}\) from 2015 to 2017. Afterwards, we utilized OCO-2 SIF products of different bands (SIF757 and SIF771) and observation modes (nadir, glint, and target) to establish SIF-GPP models, in order to explore the effects of bands and observation modes on performances ofmodeling GPP using OCO-2 SIF products.