# A 1 km Global Carbon Flux Dataset Using In Situ Measurements and Deep Learning
## 1 Introduction  
Carbon dioxide in the atmosphere is an important factor influencing climate [1; 2]. Carbon dioxide exchanged between the atmosphere and land, commonly referred to as carbon flux, is a major contributor to global climate change and warming [3; 4]. Gross primary production (GPP), terrestrial ecosystem respiration (RECO), and net ecosystem exchange (NEE) are the most common carbon flux variables used to measure the carbon exchange process between the land and the atmosphere [5; 6]. Continuous measurements of these data are provided by eddy covariance flux observation towers around the world [7]. FLUXNET is an example of the global observation network [8]. However, despite the large number of sites, the measured values at these sites can only represent the regional carbon fluxes in the absence of global distribution information [9; 10; 11; 12]. Thus, it is necessary to estimate spatial-temporal continuous distribution of global carbon flux. In order to make effective estimates of carbon flux in various regions of the earth, reliable upscaling methods should be adopted and applied [13]. There are several kinds of technologies available to calculate or estimate global carbon fluxes [14]. Data-driven machine learning methods are crucial in these approaches. These methods typically make use of satellite remote sensing records, meteorological data, and other datasets related to the carbon flux. Several studies and projects used various machine learning models to predict carbon fluxes. The most widely used of such projects is FLUXCOM ([[http://www.fluxcom.org/](http://www.fluxcom.org/)]([http://www.fluxcom.org/](http://www.fluxcom.org/)), accessed on 31 January 2023) [15]. The model tree ensemble method employing remote sensing data and a meteorological dataset was the benchmark approach for FLUXNET upscaling [16]. The datasets obtained can be used to assess land surface process models and the state of the biosphere [17]. The recent initiative further developed models based on machine learning algorithms in carbon flux prediction. [PERSON] et al. [18] evaluated the random forest model's performance in various regions for various target variables. Their study used 11 machine learning methods in four categories, including tree method, kernel function method, neural network, and regression splines, and compared the outputs with results of the existing models [9; 19]. Using the same models, [PERSON] et al. [20] predicted the global gridded data of energy flux, including radiation, latent heat, and sensible heat. Other studies also used some machine learning methods for carbon flux estimation. Among these methods, random forest and some other similar tree models showed acceptable outcomes for predicting carbon flux. The prediction results using random forest showed that it outperformed MODIS products in several locations [21]. [PERSON] et al. [22] used a modified regression tree to predict the net ecosystem carbon exchange in the US. Cubist regression tree model was used to estimate global SIF (solar-induced chlorophyll fluorescence) and GPP based on MODIS remote sensing data and meteorological reanalysis data [23]. The tree ensemble method was applied to develop a dataset nationwide over China [24]. [PERSON] et al. [25] used the random forest model to upscale GPP, RECO, and NEE data of FLUXNET2015 to obtain global carbon flux products from 1999 to 2019. In addition to tree models, the Neural Network model has also been used to estimate global GPP with remotely sensed SIF and other radiation and meteorological data as predictors [26]. The Support Vector Regression model could also reproduce GPP and NEE at site level and spatial level in Asia [27]. The potential for deep learning applications in environmental modelling is growing [28]. For example, [PERSON] et al. used four deep learning models, including deep neural network, convolutional neural network, back propagation neural network, and recurrent neural network to downscale GLASS GPP and NPP data to a higher resolution [29].