## 2 Data and Methods
### Datasets  
In this study, monthly SIF data recorded from 2001-2019 were obtained from a high-resolution GOSIF product ([[https://globalecology.unh.edu//data/GOSIF.html](https://globalecology.unh.edu//data/GOSIF.html)]([https://globalecology.unh.edu//data/GOSIF.html](https://globalecology.unh.edu//data/GOSIF.html)), accessed on 21 October 2021). The GOSIF records were retrieved from the original Orbiting Carbon Observatory (OCO)-2 SIF retrievals using a machine-learning model and other finer-scale data as inputs. Compared with the original OCO-2 SIF data, the spatial distribution of and seasonal variation in the GOSIF dataset were consistent with the corresponding characteristics of the coarse SIF data directly collected by OCO-2 SIF. However, the GOSIF data had a higher temporal resolution (8 days) and a higher spatial resolution (0.05\({}^{\circ}\)), which was of great significance for using SIF data to study the global carbon cycle. Meanwhile, GOSIF data covering the world were of great significance for exploring the role of SIF in drought monitoring, vegetation phenology, and stress monitoring [36; 37]. For a detailed description of the GOSIF dataset, please refer to [PERSON] and [PERSON] [14]. In East Asia, the eddy correlation flux-tower measurements are less readily available [38]. Since the flux-tower distribution is uneven and sparse, it is impossible to obtain high-precision GPP in global range. Many scholars have evaluated the MODIS GPP through ground observation sites [14; 36]. Although the MODIS GPP may overestimate or underestimate the tower eddy-flux network measurements, the MODIS product can reproduce seasonal and spatial changes well [39]. Since the purpose of this study was to evaluate the spatial monitoring performance of GOSIF in fine resolutions, we chose MODIS GPP (MOD17A2H) in this study. GPP calculation was based on the LUE Model. The specific calculation process can be referred to [PERSON] et al. [40]. MODIS GPP (MOD17A2H) produces (2001-2019) were obtained from MODIS ([[https://modis.gsfc.nasa.gov/data/](https://modis.gsfc.nasa.gov/data/)]([https://modis.gsfc.nasa.gov/data/](https://modis.gsfc.nasa.gov/data/)), accessed on 21 October 2021). GPP data had a spatial resolution of 500 m and a temporal resolution of 8 days. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Vegetation Type** & **Abbreviation** \\ \hline
**Class 1** & **Class 2** & \\ \hline \multirow{6}{*}{Forest (17.85\%)} & Evergreen needleleaf forest (3.22\%) & ENF \\  & Evergreen broadleaf forest (12.43\%) & EBF \\  & Deciduous needleleaf forest (1.20\%) & DNF \\  & Deciduous broadleaf forest (2.04\%) & DBF \\  & Mixed forest (81.11\%) & MF \\ \hline \multirow{2}{*}{Shrubland (10.21\%)} & Closed shrublands (0.69\%) & CS \\  & Open shrublands (99.31\%) & OS \\ \hline \multirow{2}{*}{Grassland (24.05\%)} & Woody savannas (14.51\%) & WS \\  & Savannas (0.24\%) & SA \\ \cline{1-1}  & Steppes (85.25\%) & ST \\ \hline \multicolumn{2}{c}{Croplands (23.28\%)} & CR \\ \hline Urban and built-up areas (0.81\%) & UB \\ \hline \multicolumn{2}{c}{Barren or sparsely vegetated areas (23.80\%)} & BSV \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Proportions of different land-cover types. In this study, we used the ability of NDVI to monitor vegetation dynamics, GPP and drought [5; 41; 42] to compare the application potential of GOSIF. NDVI captured the contrast in reflectance between the red and near-infrared radiance wavelengths. NDVI (MOD13C2) data (2001-2019) were obtained from MODIS ([[https://modis.gsfc.nasa.gov/data/](https://modis.gsfc.nasa.gov/data/)]([https://modis.gsfc.nasa.gov/data/](https://modis.gsfc.nasa.gov/data/)), accessed on 21 October 2021). The NDVI data had a spatial resolution of 0.05\({}^{\circ}\) and a temporal resolution of 1 month. Per-unit grain yields were obtained by dividing grain yield and cropland area.