## 1 Introduction  
The evaluation of terrestrial ecosystem carbon and water dynamic is a key issue in global climate change research [1, 2]. Gross primary production (GPP) and evapotranspiration (ET) can provide useful information for water and soil resource management, global carbon-water cycle analysis and environmental change monitoring. To understand the mechanisms of climate change and improve the prediction of possible future climate change, we require accurate quantifications of carbon and water processes in terrestrial vegetation [3]. The mechanism of GPP and ET variations have been successfully interpreted in several studies [4, 5]. However, substantial differences of GPP and ET are observed among estimated results from different models, which prevent us from fully understanding global carbon-water cycle. Therefore, the uncertainties of estimated GPP and ET still need to be addressed at global and regional scales. Satellite image offers a series of time-continuous vegetation and ecosystem observation data. Based on a combination of satellite data and ground measurements, several remote sensing methods are proposed to estimate GPP and ET. In the past decades, the spatial and temporal patterns of GPP and ET were explained by exploiting their good correlations with normalized differential vegetation index (NDVI), enhanced vegetation index (EVI), land surface temperature (LST), leaf area index (LAI) and the fraction of photo-synthetically active radiation absorbed by plant canopies (FPAR) [6]. Many remote sensing GPP and ET models presented better estimating accuracy at regional scale, such as the GPP models: the temperature and greenness (TG) model [7], the greenness and radiation (GR) model [2] and vegetation photosynthesis model (VPM) [8], _etc._ and the ET models: surface energy balance algorithm for land (SEBAL) [9] and air/surface temperature-vegetation index (T-VI) [10, 11], _etc._ Compared with the above models, the main advantages of MODIS-GPP (MOD17A2) and -ET (MOD16A2) are that they provide continuous global GPP and ET products and these products can be freely shared. Scientists can use the model results to compare with the MODIS products [12, 13]. MODIS-GPP and -ET have been widely validated against measurements from AmeriFlux, Fluxnet-Canada and EuroFlux [14, 15, 16, 17], but many observation sites are still not included around the world. Therefore, many validation studies have been carried out in other regions [17], so the performances of MODIS-GPP and -ET estimate in China still need to be validated. The MODIS-GPP and -ET algorithms employ meteorological measurements, LOOKUP tables based on land cover types and remote sensing data. In many studies, the uncertainties of weather data for model simulations have always been controversial [16, 17]. Since meteorological measurements are often not available at the same spatial scale as the remote sensing data, they can introduce substantial errors into carbon-water exchange simulations [7, 18], for example, the downscaling question about coarse resolution metrological data. [PERSON] _et al._[16] has discussed about the uncertainties of meteorological data used for driving MODIS algorithms, but little has been conducted on further exploring the impact of uncertainties of remote sensing data on simulations [17]. Compared with the meteorological data, does the quality of remote sensing data have more influence for estimating results of remote sensing model? Therefore,it is necessary to assess the sensitivity of MODIS-GPP and -ET to remote sensing input data. In addition, assessing uncertainties of GPP and ET models is useful for improving the model accuracy. Eddy covariance technique provides the best approach for measuring net CO\({}_{2}\) exchange and hydrothermal fluxes at the ecosystem scale that can be used for GPP and ET calculation. Many studies have confirmed that flux observation data at a long time scale provided favorable information for validation of GPP and ET models [19].