## 1 Introduction  
Drylands, including arid and semi-arid ecosystems, cover 30%-45% of the Earth's land surface [1; 2], and play an important role in the global carbon cycle and future carbon sequestration [3; 4]. Accurate and continuous monitoring of terrestrial ecosystem production in arid and semi-arid regions is of great importance to improve the understanding of the role of arid terrestrial ecosystems in the global carbon cycle. However, the worldwide paucity of measurement of carbon exchange in arid ecosystems has hindered the full understanding of the magnitude of carbon sequestration and the accurate prediction of the carbon cycle [5; 6]. Terrestrial gross primary production (GPP) is the largest component of the global carbon cycle and is essential to understand and quantify the contribution of terrestrial ecosystems to the global carbon cycle [7]. Satellite remote sensing provides continuous and temporally repetitive observation of land surfaces and has advanced tremendously over the past few decades that has become a useful tool in estimating the terrestrial ecosystem production across broad temporal and spatial scales. Production efficiency models (PEMs), developed for predicting global GPP with remote sensing, have been widely used to quantify the spatial and temporal variation of terrestrial ecosystem productivity [8; 9; 10]. In the absence of widespread ground observations, remote sensing models are also commonly used to estimate dryland CO\({}_{2}\) exchange [4; 11]. Previous data and remote sensing models comparisons have only included a few dryland sites [12]. Thus, there is a need to understand how well commonly used remote sensing models capture the magnitude and inter-annual variability of measured CO\({}_{2}\) exchange [13]. Since 2000, satellite-based GPP estimation have increasingly used data from the Moderate Resolution Imaging Spectroradiometer (MODIS) due to its continuous worldwide availability [8]. The MODIS GPP algorithm (i.e., MOD17) is a type of PEM, which provides high frequency worldwide observations of GPP [14; 15]. To date, MODIS has issued multiple versions of GPP [14; 16]. Currently, the MOD17 product has been updated to Collection 6 (C6), which has improved the algorithm parameters and forcing data of previous collections [15; 17], as the spatial resolution has increased from 1000 m to 500 m. A large number of studies have validated the capacity of MODIS GPP products with eddy covariance (EC) measurements across multiple biomes, such as forests [18; 19], shrulands [20], grasslands [21; 22], savanna [23], croplands [24], and across biomes [25; 26; 27]. However, most of these studies validated previous versions of MODIS GPP products (i.e., Collection 4 and 5). Comprehensive evaluation of the performance of MODIS GPP C6 products in arid regions of China remains limited to this date [1]. Previous studies showed no consistent results in the validation of Collection 4 and 5 of MODIS GPP products. MODIS GPP may underestimate at some sites, such as at cropland sites [24], overestimate at some low productivity sites [25; 28], or agree well [26] with tower-based GPP. Meanwhile, the MODIS GPP Collection 6 products (i.e., MOD17A2H) also tend to overestimate GPP in alpine meadows of the Tibetan Plateau [22] and underestimate flux-derived GPP at most sites across the globe [27]. However, because of inadequate observations in arid regions compared with other regions, it remains uncertain whether these biases also exist in other ecosystems in arid regions for the improved Collection 6 GPP products. Therefore, it is necessary to validate the performance of the latest version of MODIS products in arid regions. The overall uncertainty of carbon flux modelling includes uncertainty of input variables, model structure, and model parameters [29], which can significantly impact carbon flux at regional scales. Several attempts have been made to address the uncertainties of the PEM algorithm [26; 27; 30; 31].