## 4 Discussion  
Climate-induced issues must be mitigated by diverse stakeholders such as the policymakers, investors, companies, scientists, and the public [32]. In response to the changing climate, currently, more and more governments are implementing their aims to combat global climate warming [50]. In the fall of 2020, the Chinese government also made a specific target to reach its carbon peak by 2030 and then realizing carbon neutrality by 2060. Carbon neutrality means that one region or country gradually achieves net-zero CO\({}_{2}\) emissions by adopting diverse management measures. Terrestrial vegetation GPP represents the largest carbon flux amongst global carbon cycles and supplies important ecosystem services for human society [51; 52]. Both the Paris Agreement and the IPCC reports admitted that the targets of climate change mitigation could not be realized without the important role of terrestrial forests, whereas the rate of contribution by forests in reducing atmospheric greenhouse gas concentrations remains a challenge [53; 54]. The traditional vegetation monitoring from the satellite-based Earth observation (EO) technique is usually constrained by the vegetation structure and the sensitivity to aboveground biomass density, especially for the complicated forest ecosystems [55; 56; 57], leading to the spatial pattern and long-term changes of forest GPP being poorly understood. Vegetation GPP can be quantified using satellite remote sensing and ecological models, but  
Figure 9: Comparisons of multi-year mean tower-based GPP observations (GPP\({}_{\text{EC}}\)) and three different satellite-based GPP products including the SIF-based GPP (GPP\({}_{\text{SIF}}\)), MODIS GPP (GPP\({}_{\text{MOD}}\)) and BESS GPP (GPP\({}_{\text{BESS}}\)) at the two subtropical forest flux sites (QYZ and DHS) during 2003 and 2010. The error bars represent \(\pm\)1 standard error. [PERSON] et al. [42] revealed that the widely used MODIS product was unable to capture the long-term dynamics of global karst ecosystem GPP. In this study, the contrasting performance of the MODIS GPP and BESS GPP products were revealed when monitoring GPP of the evergreen needleleaf forest and evergreen broadleaf forest (Figure 6). MODIS GPP exhibited better performance in the broadleaved forest (Figure 6b), whereas a large underestimation was found for the coniferous forest (Figure 6a). [PERSON] et al. [20] systematically evaluated the latest MODIS GPP products across multiple biomes using global eddy covariance flux data and revealed that the \(\epsilon_{\text{max}}\) of MOD17A2H have the largest underestimation reaching up to 31% for the evergreen needleleaf forest. In contrast, BESS GPP showed better performance in the coniferous forest (Figure 6c), which was consistent with the results by [PERSON] et al. [58], with the \(R^{2}\) and RMRE being 0.76 and 1.66 g C m\({}^{-2}\) d\({}^{-1}\), respectively, for the evergreen needleleaf forest, and \(R^{2}\) and RMRE being 0.35 and 2.45 g C m\({}^{-2}\) d\({}^{-1}\), respectively, for the evergreen broadleaf forest. Fortunately, scientists found that when vegetation chlorophyll is exposed to solar radiation, SIF will be emitted as a by-product of vegetation photosynthesis activity, which is different from the reflectance-based biophysical parameters. The satellite-based sun-induced fluorescence technique has been developed in recent years and recognized as a rapidly advancing front on the terrestrial vegetation science [59; 26]. SIF showed more advantages in monitoring terrestrial GPP by comparison with other remotely sensed indicators such as vegetation indices. Because SIF is related to both the amount of absorbed photosynthetic active radiation (APAR) as well as the efficiency of APAR used to carry out photosynthesis [60], satellite SIF retrievals thus have great potential to monitor photosynthetic activity, which has also been confirmed by our study (Figures 5 and 6).