## 1 Introduction  
Gross primary production (GPP), which is the amount of light energy from the sun converted to chemical energy, determines the thermal, water and biogeochemical cycles in terrestrial ecosystems. However, even when eddy covariance (EC) flux data and remote sensing data are conjunctively introduced into various diagnostic models, uncertainties in modeled GPP are still vast. Current estimates of global GPP range between 102 and 165 petagrams of carbon per year, where uncertainties are likely from errors of input data, poor parameterization and model structure [1; 2; 3; 4; 5; 6]. Among the methods of reducing uncertainties in GPP simulation, parameter adjustment and structural modification are adopted, and the former method could compensate for the errors introduced by a model structure [2; 7; 8]. Many concepts were developed to simulate carbon assimilation [9; 10; 11]. One of the widely accepted approaches is the light use efficiency model because of both its simple structure--which assumes that a fraction of the photosynthetically active radiation (PAR) absorbed by the vegetation canopy is used for plant primary production [9]--and its large amount of available input data, including EC measurements [12; 13; 14; 15] and remotely sensed data [16; 17; 18]. One application of this kind of model is the MODIS GPP algorithm [19]. Its latest product MOD17A2 Collection 5 ([[https://lpdaac.usgs.gov/products](https://lpdaac.usgs.gov/products)]([https://lpdaac.usgs.gov/products](https://lpdaac.usgs.gov/products))) is forced by the National Center for Environmental Prediction--Department of Energy (NCEP-DOE) reanalysis II data and the default parameters are derived from the Biome Property Look-Up Table (BPLUT) [20]. Based on the algorithm and its product, many evaluations have been made. Early studies focused on uncertainties introduced by input data [5; 16; 21] and simulations using different input data [17; 22; 23]. A direct comparison between the NCEP-DOC reanalysis II data and EC tower measurements at 12 African sites suggested that these two forcing data with different spatial resolutions were comparable in air temperature and atmospheric vapor pressure deficit (VPD), but the relationship was scattered in incoming PAR [14]. Recently, many studies paid attention to structural errors of the MODIS GPP algorithm. [PERSON] _et al._[24] compared the MODIS GPP product with the estimates using a process-based ecosystem model (Boreal Ecosystem Productivity Simulator). They noted that the MODIS GPP algorithm cannot properly treat the contribution of shaded leaves to canopy-level GPP. [PERSON] _et al._[13; 15] developed a two-leaf light use efficiency model for improving the calculation of GPP and validated its application in six ecosystems. Previously, the two-leaf upscaling strategy had been well documented in the Farquhar model, as compared with the one-leaf strategy [25; 26]. Aside from model restructure, adjusting biome parameters is another way to improve simulation. Evaluations of MODIS GPP using EC data indicated that adjusting the maximum light use efficiency (\(\varepsilon_{max}\)), which was derived from the BPLUT [19; 20], in the algorithm might be needed to better estimate GPP in both Africa [14] and northern China [27]. As a key parameter in the algorithm, \(\varepsilon_{max}\) was further affected by the scales of daily minimum temperature (\(T_{MIN}\)) and VPD [19]. However, the \(T_{MIN}\) function was observed not toconstrain MOD17A2 GPP much [8, 14] and both functions could be corrected using flux tower measurements [8]. Moreover, some studies noted that optimizing the algorithm using the soil water content can improve agreement with the measurements [28], but only in dry regions [14, 29, 30] or throughout the growing season [16]. As mentioned above, both adjusting the key parameters and modifying the model structure can improve GPP estimation using the MODIS algorithm, and the former can compensate for errors introduced by the latter [2, 7, 8].