# Improving the Gross Primary Production Estimate by Merging and Downscaling Based on Deep Learning
## 1 Introduction  
Global climate change, especially the impact of climate warming on the human living environment, has attracted more and more attention [1]. With the continuous development of the global industry, the use of fossil fuels is increasing. This process releases large amounts of carbon dioxide and changes the proportions of greenhouse gases, causing global climate problems such as the intensification of the greenhouse effect. The gross primary productivity (GPP), formed by terrestrial vegetation through photosynthesis to fix CO2 and energy in the biosphere, is the initial stage of entering the carbon cycle process and the basis of the ecosystem carbon cycle [2]. Terrestrial ecosystems can influence global climate change through the carbon cycle [3]. Therefore, the research on the carbon cycle of global terrestrial ecosystems has received widespread attention and has become a hot spot in global change research [4]. An essential indicator of terrestrial ecosystem carbon cycle research is GPP. The gross primary productivity, that is, the total photosynthetic uptake or carbon assimilation by plants, is a key component of terrestrial carbon balance [5]. It is the main driving factor of the terrestrial carbon sink, which is responsible for absorbing about 30% of anthropogenic carbon dioxide emissions [6]. In general, it can be said that GPP plays a vital role in the global carbon cycle [7; 8; 9; 10]. Characterizing the spatiotemporal changes and trends of GPP is essential for a deeper understanding of the carbon cycle between terrestrial ecosystems andthe atmosphere [11]. Therefore, quantifying the GPP is crucial to understand the impact of climate change and atmospheric carbon dioxide concentration changes on the terrestrial carbon cycle [12; 13]. The accurate estimation of GPP at regional and even global scales contributes to a more comprehensive and in-depth understanding of ecosystem functions and the global carbon cycle [14]. However, with the intensification of climate change, there is significant uncertainty in estimating GPP [15]. In recent decades, significant progress has been made in estimating GPP, and many global GPP datasets have been published [16]. These products differ in GPP estimates in different ecosystems and external environments, with advantages and disadvantages in various studies [17; 18; 19]. Data-driven GPP estimation is the primary tool for GPP research and model evaluation. It mainly includes the following three types. The first type uses eddy covariance (EC) technology to obtain flux tower observation and upscaling [20]. Process-based models such as the Community Land Model (CLM) are commonly considered in the revision of GPP products [21]. However, the distribution of flux towers is sparse and uneven, and the coverage period is different and limited [22], which makes the derivation of global GPP complicated. When estimating GPP products by scaling up, there will be a more significant deviation near the sparse flux tower [22]. The second type is the machine learning (ML) method that uses satellites and other meteorological data as input [23; 24; 25; 26; 27; 28]. ML products are widely used for benchmarking and depend more on the spatial representation of flux sites [15; 24]. The third type is based on the principle of light use efficiency (LUE), which is used to derive the GPP based on optical remote sensing variables [22; 27; 29; 30; 31]. LUE products are good at detecting the spatial distribution pattern of GPP; however, they usually perform poorly in seasonal estimation and overestimate GPP under dry and cold conditions [28; 32; 33; 34; 35]. At the regional and even global scale, the land surface model (LSM) and ecosystem model [36; 37; 38] are also effective methods to simulate GPP. The process models consider plant physiological and ecological processes, which have solid theoretical significance and can simulate future productivity changes.