# Underestimates of Grassland Gross Primary Production in MODIS Standard Products
## 4 Discussion
### Attributing Underestimation in Grassland GPP and Its Implications
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Site ID** & \(\varepsilon_{max}\)**-EST** & \(\varepsilon_{max}\)**-BPLUT** & **RMSE After** & **RMSE Before** & \(\varepsilon_{max}\)** **bias** \\  & **g C/MJ** & **g C/MJ** & **g C\({}^{-2}\) day\({}^{-1}\)** & **g C\({}^{-2}\) day\({}^{-1}\)** & **g C/MJ** \\ \hline AT-Neu & 1.71 & 0.86 & 2.30 & 4.05 & 0.85 \\ AU-Dap & 0.87 & 0.86 & 3.30 & 3.31 & 0.01 \\ AU-Stp & 0.73 & 0.86 & 0.98 & 1.04 & \(-\)0.13 \\ CH\_Cha & 0.97 & 0.86 & 3.19 & 3.64 & 0.11 \\ CH-Fru & 1.28 & 0.86 & 2.24 & 3.19 & 0.42 \\ CH-Oe1 & 1.13 & 0.86 & 2.64 & 3.19 & 0.27 \\ CZ-Bk2 & 2.57 & 0.86 & 7.42 & 1.31 & 1.71 \\ DE-Gri & 1.09 & 0.86 & 1.53 & 1.90 & 0.23 \\ IT-Mbo & 0.98 & 0.86 & 2.24 & 2.36 & 0.12 \\ IT-Tor & 1.13 & 0.86 & 2.55 & 2.81 & 0.27 \\ NL-Hor & 0.93 & 0.86 & 2.04 & 2.05 & 0.07 \\ RU-Sam & 0.93 & 0.86 & 0.89 & 0.89 & 0.07 \\ US-SRG & 0.77 & 0.86 & 0.57 & 0.68 & \(-\)0.09 \\ US-Var & 0.76 & 0.86 & 1.52 & 1.72 & \(-\)0.10 \\ US-Wkg & 1.07 & 0.86 & 0.60 & 0.63 & 0.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The comparison of the \(\varepsilon_{max}\) between the estimated values based on the nonlinear model (\(\varepsilon_{max}\)-EST) and the values in the biome properties look-up table (\(\varepsilon_{max}\)-BPLUT) used in the MOD17 GPP algorithm. “RMSE after” and “RMSE before” referred to the RMSE between the GPP\({}_{\text{EC}}\) and GPP\({}_{\text{MOD}}\) with \(\varepsilon_{max}\)-EST and \(\varepsilon_{max}\)-BPLUT, respectively. Another previous study [55] compared MODIS-FPAR and GLASS-FPAR (derived from the product of the Global Land Surface Satellite (GLASS)). MODIS-FPAR performed well with a higher \(R^{2}\) and a lower bias for mixed forests and cropland. However, GLASS-FPAR performed better in grasslands with a higher \(R^{2}\) and a lower bias [55]. Furthermore, [PERSON] et al. [33] and [PERSON] et al. [51] pointed out that the adaptive Savitzky-Golay filtered FPAR data could have better performance compared with the FPAR data based on the linear interpolation approach used in the MODIS algorithm. The adaptive SG method could maintain some intrinsic seasonal variability and keep reliable values of FPAR. Further uncertainties also arise due to the canopy photosynthesis saturation effects on the FPAR [56]. In this study, GPPMOD products underestimated GPP in tropical grassland sites because of the saturation of canopy photosynthesis. A previous study showed that the MODIS light use efficiency approach does not account for saturation of canopy photosynthesis under clear sky conditions in a tropical forest site [50]. Saturation can result in the underestimation of GPP with low values of FPAR but overestimation with high FPAR values [50]. The tropical grassland received solar radiation for extended periods and it was easy for canopy photosynthesis saturation to occur [57] especially in periods of drought. Previous studies also reported that saturation of canopy photosynthesis might occur on clear-sky days at the hourly and daily timescales [58; 59; 25]. The species composition of grassland ecosystems could also cause uncertainty in GPP estimates. Grasslands are dynamic ecosystems that often have diverse mixtures of C3 and C4 species, which have different light use efficiencies. Furthermore, the ratio of C3 and C4 species within a grassland can have high intra-annual and inter-annual variability [31]. Such community composition data is not available for all the FLUXNET sites. The effects of the composition of C3 and C4 grasses on GPP simulation is an interesting question, but it is beyond the scope of this specific study. However, a recent GPP product considering the C3 and C4 photosynthesis pathways showed improved performances in grassland ecosystems [60]. The water stress scalar could also have uncertainty when using VPD as a proxy of water stress in the PSN model.