Integration of Sentinel-3 OLCI Land Products and MERRA2 Meteorology Data into Light Use Efficiency and Vegetation Index-Driven Models for Modeling Gross Primary Production  
[PERSON]  
1 Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, China; [EMAIL_ADDRESS]  
[PERSON]  
3 School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China; [EMAIL_ADDRESS]  
[PERSON]  
1 Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, China; [EMAIL_ADDRESS]  
[PERSON]  
1 Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, China; [EMAIL_ADDRESS]  
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of EC towers and their spatial distribution, mean that EC is not suitable for quantifying and monitoring GPP on regional, continental, and global scales [6; 7; 8]. Therefore, many models were developed for estimating GPP with various spatial scales, and they were categorized as follows: meteorological statistical models, dynamic global vegetation models (DGVMs), and remote sensing (RS)-driven models [9]. Meteorological statistical models [10; 11; 12] establish a simple statistical regression model between GPP and climate factors through a large amount of measured data. Due to the lack of a strict ecological mechanism, these models have great uncertainty and can only be used for interannual GPP estimation [13]. DGVMs simulate changes in the various processes, structures, and functions of the ecosystem [14; 15; 16]. However, their complex model structure and the uncertainty of the input data and parameters have caused these models to produce large errors when estimating GPP [17; 18]. With a simple theoretical basis and practicality, RS-driven models were developed for continuously observing spatial\(-\)temporal variations in GPP [19; 20]. At present, the most frequently used RS-driven models can be divided into light use efficiency (LUE) models [21; 22] and vegetation index (VI)-driven models [23; 24]. LUE models were developed according to [PERSON]'s light use efficiency concept, utilizing absorbed photosynthetically active radiation (APAR) and LUE to estimate GPP [25; 26]. A large number of LUE models are widely applied in GPP estimation due to their clear mechanism and simple structure [27; 28; 29; 30; 31; 32]. The fraction of absorbed photosynthetically active radiation (FAPAR) is a significant input parameter within LUE models [33; 34; 32]. VI-driven models estimate GPP by building an empirical relationship between in situ measurement GPP and vegetation indices (VIs) [35]. However, numerous VIs have been used to estimate GPP [36; 37; 38; 39]. EVI is the most widely used VI amongst them [40]. For example, [PERSON] et al. [41] used EVI to replace the chlorophyll content to estimate GPP in the greenness and radiation (GR) model. The VI model [42] was applied to estimate GPP using several VIs and EVI was proved the best indicator of LUE and FAPAR. Moreover, the temperature and greenness (TG) model [23] was employed to estimate GPP with the combination of EVI and land surface temperature (LST). Although LUE and VI-driven models are widely used, the application of these models still has several shortcomings. For example, MODIS GPP products (MOD17), which are based on the MODIS GPP algorithm and MODIS FAPAR products (MOD15A2H), are cumulative eight-day composite products with a 500-m pixel size [43; 44; 45]. The standard EVI product is MODIS EVI, and its temporal is 16 days.