## 1 Introduction  
Gross primary production (GPP) is an essential flux of the net ecosystem exchange (NEE) of CO\({}_{2}\) between the atmosphere and terrestrial ecosystems, and contributes to human welfare by regulating ecosystem functions [1; 2; 3; 4]. Estimates of the magnitude and variability of GPP and NEE are fundamental to understanding the biogeochemical dynamics of terrestrial ecosystems [5; 6]. Since the 1990s, the eddy covariance method has emerged as an important tool to investigate seasonal phasing and amplitudes of CO\({}_{2}\) fluxes between terrestrial ecosystems and the atmosphere [4; 7]. The net carbon fluxes from eddy covariance towers can be partitioned into the components GPP and ecosystem respiration (RE), which can be used to validate products developed by biogeochemical models and remotely sensed vegetation indices [4; 8]. However, it is a challenging task to extrapolate GPP and CO\({}_{2}\) flux measurements to the large scale due to significant spatial and temporal heterogeneity of ecosystems across complex landscapes. Two main methods exist for deriving large-scale estimates of GPP [9; 10; 11]: (1) the application of process-based, biogeochemical models that scale-up and extrapolate site-specific measurements [12; 13; 14]; and (2) the application of satellite data based on the concept of light use efficiency [9; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29]. The accuracy of satellite-derived GPP or NPP estimates have been recently highlighted as a matter of concern due to poor satellite data quality as well as lack of accurate ecosystem representation in satellite-based models [30]. In our previous work, an integrated strategy was presented that combined the remote sensing-based GLObal Production Efficiency Model (GLOPEM) and the ecosystem process-based model, the Carbon Exchange between Vegetation, Soil and Atmosphere model (CEVSA). The integrated GLOPEM-CEVSA model was shown to increase the accuracy of spatial and temporal estimates of terrestrial ecosystem GPP, RE, and NEE by taking into consideration the spatial heterogeneity of ecosystems [9]. The model was also shown to be suitable for estimating the spatial-temporal distribution of net primary production (NPP) for forest and grassland ecosystems [31,32]. A main component of the GLOPEM-CEVSA modeling framework is the satellite-derived fraction of photosynthetically active radiation absorbed by vegetation (FPAR), which can be derived from satellite normalized difference vegetation index (NDVI) data. The Advanced Very High Resolution Radiometer (AVHRR) sensor has the longest record of continuous satellite data, spanning 1981 to the present, and the data have been used to produce various versions of the GIMMS (Global Inventory Modeling and Mapping Studies) NDVI data product. For example, the previous GIMMS NDVI dataset has been widely used by the community [33] (GIMMS NDVI1g, hereafter). The newest version of the global GIMMS NDVI dataset version 3, termed NDVI3g, has been recently released and covers 31 years from July 1981-2011 with data sensed by AVHRR onboard NOAA 7, 9, 11,14, 16, 17 and 18 [34,35]. NDVI3g represents an important advance by extending the satellite record to more than 30 years. NDVI3g is a global product with a bi-monthly temporal resolution and ~8-km spatial resolution, and features improved data quality by accounting for biases such as calibration loss, orbital drift, volcanic eruptions, _etc._[36]. Further, NDVI3g data has been previously shown to represent real responses of vegetation to climate variability [37]; suggesting that significant potential exists for the NDVI3g dataset to contribute to long-term simulations of GPP. Our main objective here was to compare the newly available GIMMS NDVI3g dataset to similar existing data products in an effort to establish the potential for GIMMS NDVI3g to advance understanding of the terrestrial carbon cycle for the region of Southeast Asia.