**Haibo Wang 1, Xin Li 2,3,\*, Mingguo Ma <sup>4</sup> and Liying Geng <sup>1</sup>**


Received: 18 December 2018; Accepted: 21 January 2019; Published: 22 January 2019

**Abstract:** Accurate and continuous monitoring of the production of arid ecosystems is of great importance for global and regional carbon cycle estimation. However, the magnitude of carbon sequestration in arid regions and its contribution to the global carbon cycle is poorly understood due to the worldwide paucity of measurements of carbon exchange in arid ecosystems. The Moderate Resolution Imaging Spectroradiometer (MODIS) gross primary productivity (GPP) product provides worldwide high-frequency monitoring of terrestrial GPP. While there have been a large number of studies to validate the MODIS GPP product with ground-based measurements over a range of biome types. Few studies have comprehensively validated the performance of MODIS estimates in arid and semi-arid ecosystems, especially for the newly released Collection 6 GPP products, whose resolution have been improved from 1000 m to 500 m. Thus, this study examined the performance of MODIS-derived GPP by compared with eddy covariance (EC)-observed GPP at different timescales for the main ecosystems in arid and semi-arid regions of China. Meanwhile, we also improved the estimation of MODIS GPP by using in situ meteorological forcing data and optimization of biome-specific parameters with the Bayesian approach. Our results revealed that the current MOD17A2H GPP algorithm could, on the whole, capture the broad trends of GPP at eight-day time scales for the most investigated sites. However, GPP was underestimated in some ecosystems in the arid region, especially for the irrigated cropland and forest ecosystems (with R2 = 0.80, RMSE = 2.66 gC/m2/day and R2 = 0.53, RMSE = 2.12 gC/m2/day, respectively). At the eight-day time scale, the slope of the original MOD17A2H GPP relative to the EC-based GPP was only 0.49, which showed significant underestimation compared with tower-based GPP. However, after using in situ meteorological data to optimize the biome-based parameters of MODIS GPP algorithm, the model could explain 91% of the EC-observed GPP of the sites. Our study revealed that the current MODIS GPP model works well after improving the maximum light-use efficiency (εmax or LUEmax), as well as the temperature and water-constrained parameters of the main ecosystems in the arid region. Nevertheless, there are still large uncertainties surrounding GPP modelling in dryland ecosystems, especially for desert ecosystems. Further improvements in GPP simulation in dryland ecosystems are needed in future studies, for example, improvements of remote sensing products and the GPP estimation algorithm, implementation of data-driven methods, or physiology models.

**Keywords:** terrestrial ecosystem; MODIS GPP product; calibration; arid region; oasis-desert ecosystem

#### **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 [8–10]. In the absence of widespread ground observations, remote sensing models are also commonly used to estimate dryland CO2 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 CO2 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], shrublands [20], grasslands [21,22], savanna [23], croplands [24], and across biomes [12,25–27]. 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]. For the MOD17 products, inaccuracies in the parameterization of model parameters (such as maximum light-use efficiency (εmax or LUEmax)) were found to be one of the most important factors attributed to the bias of MODIS GPP [12,20]. The current MOD17 algorithm uses the constant maximum LUE and other parameters for one ecosystem [18], which is not suitable for variability of climate conditions and ecosystems. Previous studies found that the LUE parameter in the MOD17 algorithm was underestimated [26]. Several attempts have been made to calibrate the maximum LUE parameters

and to improve the performance of MODIS GPP estimation [24,26,32]. However, most of these studies overlooked the potential impacts of other model parameters' uncertainty on the estimation of GPP, e.g., water-limited factors, which are important factors for GPP estimation, especially for ecosystems in the arid region.

Research community have established that by adjusting the key parameters of the model can improve GPP estimation using MODIS GPP algorithm, which can compensate for the errors introduced by the model structures [23]. A model–data fusion approach provides powerful tools for optimizing the model parameters and quantifying the influence of uncertainties, and is being increasingly used to estimate the parameters of ecological models [33–38]. Model–data fusion approaches include Bayesian and non-Bayesian approaches. Non-Bayesian approaches, such as global optimization algorithms, can efficiently determine the optimal parameter solutions by minimizing (or maximizing) objective functions [36], but cannot quantify uncertainty. In contrast, the Bayesian approach can be employed to update the parameter distributions when new information becomes available [37], and produce reliable estimates of parameter and predictive uncertainty [38]. Some past studies have strengthened the importance of parameters estimation in carbon cycle models [32,39], but have mainly focused on single site to constrain the parameters of a given plant functional type (PFT), in addition, few studies have assessed the variability of parameters within a PFT [40]. For MODIS GPP validation, since the PFT parameters in the MOD17 algorithm are obtained from flux towers worldwide, they are not appropriate for specific regions such as the arid regions of China.

Thus, this study aims to examine the performance of newly released MODIS GPP C6 products and MOD17 algorithms in predicting GPP in a typical arid region of China. The overall goals of this study are to: (1) Evaluate the model performance of the MODIS GPP Collection 6 products at eight-day to annual time scale across various ecosystem types in a typical arid region of China; (2) analyze the uncertainty of remote sensing models in simulating GPP in typical arid regions; and (3) quantify the parameter uncertainties in GPP estimation for the main ecosystem types in arid regions of China by using a Bayesian approach with calibration of maximum LUE and water and temperature-limited factors. This research will contribute to the development and improvement of GPP estimates in arid regions.
