## 4 Discussion
### Impacts of FPAR on GPP Estimations
In addition, we also found the same discrepancies (15.8% of RPE) of \(\varepsilon_{\text{max}}\) caused changes in GPP estimation models among two distinct FPAR levels through comparing changes of GPP estimation models from GPP_MODR1 to GPP_MODR4 (36.0% of RPE) and from GPP_MODR2 to GPP_MODR3 (51.8% of RPE) (Figure 6 and Table 3), which confirmed that the FPAR:\(\varepsilon_{\text{max}}\) caused 15.8% of RPE in GPP estimation based on the MOD17A2 algorithm compared to in insitu flux tower GPP (GPP_EC) in this alpine swamp meadow. savanna show that the main reason for the differences between MODIS and tower derived GPP is FPAR, followed by LUE and meteorological inputs [27]. This could be attributed to the different upstream inputs across multiple biomes, artificially high values of MODIS FPAR at low productivity site and relatively low default \(\epsilon_{\text{max}}\) values at high productivity sites [17], in the LUE estimation procedure [28]. Although GPP_MODR3 predicted the seasonal and annual GPP patterns well and could be treated as an alternative GPP estimation method on this alpine swamp, an approximate 10% of RPE offset in daily GPP estimations still existed (Figures 3-5 and Table 3). Three kinds of reasons could explain this offset and the lager offsets in other MODIS-based GPP estimation models in this study. One is the parameter inputs in MOD17A2 algorithm [34]. First, many relevant researches proposed that the VPD only represents part of the atmospheric evaporative demand but is not entirely representative indicator of water availability condition thus it does not adequately reflect the observational GPP [25; 26; 35]. Therefore, they advised that soil moisture [17], remote water index [4; 15], or precipitation [25] should be added as a stress factor in the MOD17 algorithm to improve GPP simulation [26]. However, in this study, the alpine swamp is waterlogged during the growing season thus the water limitation should not be the main reason for underestimation of MODIS-based GPP. Second, different cloudiness regulated the \(\epsilon_{\text{max}}\) value changes due to the change of solar radiation intensity, especially the diffuse radiation [75], which have direct or indirect influences on GPP estimation [17]. Moreover, previous studies suggested that light use efficiency should be different between sunlit and shaded leaf due to the distinct acceptance of direct and diffuse radiation [26; 35]. Whereas we assumed a constant \(\epsilon_{\text{max}}\) value for this alpine swamp throughout the year, it also may be a reason for the current offsets in MODIS-based GPP estimation. Another is the uncertainties in temporal-spatial matching between MODIS-based and ground measurements [19]. In the DAO with a spatial resolution of \(1^{\circ}\times 1.25^{\circ}\) is hard to perfectly match a 1 km pixel of MODIS products, which may cause large inaccuracies in GPP estimation at spatial scales [19; 28]. Besides, the footprint of GPP_EC is not the regular geographical pixel what the MODIS products provide, which implied the inconformity in GPP estimation [29]. In addition, MODIS products outputs an average LAI\({}_{\text{M}}\) with an 8-day time step while LAI\({}_{\text{G}}\) reflects the specific vegetation state in the measurement day, thus the errors of temporal match is also inevitable due to the vegetation activity [11]. The rest is the estimation errors of GPP_EC themselves. The EC measurement exist certain uncertainties caused by instrument malfunction, rainfall, dew formation, and human disturbances [76; 77]. Additionally, both the gap filling and NEE partitioning are based on the assumption that the relationship between ecosystem respiration and temperature is similar in all day, daytime and nighttime [57; 59; 63; 78], which may actually produce GPP_EC estimation error [21].