## 1 Introduction
Reference [14] used three different models to estimate GPP in a local deciduous forest in the US in combination with flux measurements and remote sensing data. Comparisons were made yearly, but on average, the correlation coefficient was much better, at 0.91. Reference [15] used two enhanced vegetation index (EVI)-based models to estimate the GPP in 15 sites across the USA. The model's results vary according to the vegetation; in some cases, coefficients of determination can vary from 0.27 up to 0.94 for specific sites. Using remote sensing data, disturbance information, and a support vector regression model, [16] upscaled the CO\({}_{2}\) fluxes from a network of 21 EC towers to estimate the regional-scale Alaskan CO\({}_{2}\) budget from 2000 to 2011 and compared the results with an inverse model. The predicted values were consistent with the field observations (\(R^{2}\) = 0.79 for NEE) and the magnitude of the inverse model. However, the interannual variations of the results achieved by the remote-sensing-based model and those of the inverse model were inconsistent and therefore require additional validation. Reference [17] used three remote sensing data-driven models to evaluate and understand GPP estimations in 119 EC sites in the northern hemisphere. Considering all the study sites, the mean \(R^{2}\) was 0.63. However, the values can vary significantly depending on the model used and the vegetation, and in some cases, they can reach 0.78. Usually, the highest values were found for deciduous broadleaf and mixed forest. Reference [18] mapped the soil organic carbon (SOC) in Italy using ground observations and ancillary remote sensing data. Compared with all observations, the geographically weighted regression using MODIS normalized difference vegetation index (NDVI) obtained an \(R^{2}\) of 0.45 for SOC. Reference [19] conducted comprehensive work generalizing and mapping NEE, GPP, and respiration data for all types of classification as a collaborative FLUXCOM initiative. The overall coefficients of determination for GPP and respiration were more than 0.71 and 0.64, respectively, while about 0.48 for NEE. As seen in the examples above, despite the significant advances in the use of remote sensing for CO\({}_{2}\) flux estimations, the results point to mixed success and lack of consistency, so there is room for major improvements. The key areas of such improvements include increasing spatial resolution, capturing temporal variability, improving the data quality (primarily by reducing uncertainties and increasing consistency) and developing better algorithms to enable the simultaneous use of multiple relevant parameters and different spatial and temporal scales. In order to achieve such improvements, we propose to rely on the significant advances which have been made in recent years in the fields of machine learning, artificial intelligence, and neural networks to extract patterns and insights from remote sensing data, processing large volumes of remotely sensed data with minor human interference [20]. Here, we present the successful use of one such approach for globally scaled remote-sensing-based carbon accounting models anchored by the direct carbon flux measurements using eddy covariance flux stations. We use input data on the net ecosystem exchange (NEE, [21; 22; 23; 24]) as targets for the training process and then combine the remote sensing and meteorological variables [19] as predictors to reproduce variations of NEE on a daily and monthly scale to demonstrate the increased predictive power of the proposed approach over more traditional methods. An overarching goal of this work is to bridge the domains of science and technology by integrating multi-scale remote sensing and eddy covariance measurements to support projects aimed at avoiding GHG emissions or to remove CO\({}_{2}\) from the atmosphere.