# Underestimates of Grassland Gross Primary Production in MODIS Standard Products
## 1 Introduction  
Gross primary productivity (GPP), which is also known as the rate of photosynthesis, is the biggest carbon flux of terrestrial ecosystems [1]. This carbon flux plays an important role in the terrestrial carbon cycle. GPP is also the basis for ecosystem services such as food, fuel, and wood products [2]. The ability to accurately track the spatial and temporal variability of GPP is fundamental for understanding the biogeochemical dynamics of terrestrial ecosystems [3; 4]. Therefore, it is critical for us to accurately estimate GPP and further understand the trends and variations of global and regional carbon uptake. However, there still exist considerable uncertainties in GPP estimation, which has attracted plenty of attention [5; 6; 7]. Satellite remote sensing provides an unprecedented and practical opportunity to estimate ecosystem GPP at large scales by using a diagnostic approach. Numerous remote sensing models have been proposed including the Global Production Efficiency Model (GLOPEM) [8], the Carnegie-Ames-Stanford Approach model (CASA) [9], the Vegetation Photosynthesis Model (VPM) [10; 11], the light use efficiency model (EC-LUE) [12], and the net photosynthesis model (PSN) [13]. The Moderate Resolution Imaging Spectroradiometer (MODIS) primary production products (MOD17A2) based on the PSN model are open access datasets with high temporal and spatial resolutions that allow for the monitoring of global GPP at the 1-km resolution every eight days [14]. Previous studies using different light use efficiency (LUE) models estimated global GPP ranging from 105 to 177 P g C a\({}^{-1}\)[15; 16] with a considerable uncertainty. [PERSON] et al. [17] compared seven LUE models and they found that only two models (EC-LUE and CFlux) showed higher correlations between GPP derived from eddy covariance (GPP\({}_{\text{EC}}\)) and modeled GPP and performed better in simulating inter-annual variability of GPP than others. Furthermore, the performances of different models are not the same among all vegetation types or biomes [18; 19]. As the only official global GPP products (MOD17A2/MOD17A2H), the MODIS GPP (GPP\({}_{\text{MOD}}\)) products have been widely used in addressing scientific questions on terrestrial carbon uptake and carbon cycle [20; 21]. For example, MOD17A2 has been used to understand the drought impact on global carbon uptake of terrestrial ecosystems [22]. However, there is still a great discrepancy about the reliability of GPP\({}_{\text{MOD}}\) when compared with in-situ data collected in different locations [23]. Due to the unavailability of directly measured GPP data, the validation of MODIS GPP products is still challenging. The eddy covariance technique is an effective approach to evaluate GPP on the landscape scale. A previous study found that the GPP\({}_{\text{MOD}}\) was reliable and matched well with GPP\({}_{\text{EC}}\) in the forest biome at Vancouver Island, Canada [24]. However, there are many studies that have highlighted the uncertainties of GPP\({}_{\text{MOD}}\) data in different ecosystem types. For example, [PERSON] et al. [25] pointed out that the GPP\({}_{\text{MOD}}\) underestimated GPP at hardwood forest sites and, yet, agreed well with GPP\({}_{\text{EC}}\) data at boreal forest sites. In terms of savanna ecosystems, [PERSON] et al. [26] found that the GPP\({}_{\text{MOD}}\) performed differently for tropical wet/dry savannas. It overestimated GPP during the dry season and arid summer but matched very well during the two wet seasons. Consistent with that in [PERSON] et al. [26], GPP\({}_{\text{MOD}}\) underestimated GPP at dry sites located in the Sahel region, according to a study at 12 African sites of savanna ecosystems [27] while the product agreed well with GPP\({}_{\text{EC}}\) at wet sites. It showed that generally GPP\({}_{\text{MOD}}\) tend to be underestimated in drier forest ecosystems or savanna ecosystems.