# Underestimates of Grassland Gross Primary Production in MODIS Standard Products
## 5 Conclusions  
The GPP of grassland ecosystems plays a vital role in carbon sequestration, food production, and biodiversity [70]. In this study, we investigated and evaluated the performance and robustness of GPPMOD at grassland sites across the globe. We found that: (1) GPPMOD validation at 15 global eddy covariance sites suggested a high explanation capability of GPP variances (66%) but GPPMOD  
Figure 7: The relationships between Wscalar of MOD17A2H GPP products and GPPEC, respectively. The solid line is a linear regression. The short-dashed line is a 1:1 line. generally underestimated GPP about 34% for grassland ecosystems. (2) GPPmod performed better for the temperate grassland (\(R^{2}\) = 0.72 (0.68-0.75), \(RMSE\) = 2.40 (2.25-2.54) g Cm\({}^{-2}\) day\({}^{-1}\)) than the alpine (\(R^{2}\) = 0.64 (0.59-0.68), \(RMSE\) = 2.55 (2.30-2.79) g Cm\({}^{-2}\) day\({}^{-1}\)) and the tropical grasslands (\(R^{2}\) = 0.40 (0.27-0.52), \(RMSE\) = 2.45 (2.13-2.76) g Cm\({}^{-2}\) day\({}^{-1}\)). The inconsistent underestimates of GPPmod for the three grassland biomes may be rooted in the MODIS GPP algorithm, which underestimated \(\varepsilon_{max}\) for the different grassland biomes. The varied performances of the GPP standard products in different grassland types implied that the parameterization of the MOD17A2H GPP products (e.g., \(\varepsilon_{max}\), FPAR, and water scalar) could be improved to better capture changes in GPP. Conceptualization, Z.X., J.D. and Z.Z. ; Methodology, Z.X., Y.P. and Y.Z. ; Software, Z.X., Y.P., Y.Z. and G.Z. ; Validation, Z.X. and Y.P. ; Formal Analysis, Y.P. and Z.X. ; Data Curation, X.X. ; Writing-Original Draft Preparation, Z.X. ; Writing-Review & Editing, Y.P., J.D. and Z.X. ; Supervision, Y.Z., J.B., L.C., R.B.D., G.Z. and X.X. The authors declare no conflict of interest. [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Spatial and Temporal Variability of Net Primary Productivity (NPP) over Terrestrial Biosphere of India Using NOAA-AVHRR Based GloPEM Model. _J. Indian Soc. Remote Sens._**2011**, _39_, 345-353. [CrossRef]
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