## 4 Conclusions
The availability of a large amount of consistent and uniform data allowed for setting a fairly conservative threshold of 80% for the NEE data coverage and resulted in substantially reduced noise. On the remote sensing side of the approach, the Landsat archive provided a consistent and robust set of data with great temporal depth and spatial resolution. The FLEXPART model was instrumental in mapping the area that contributed to the NEE values captured by flux stations, helping to match these with remote sensing products better to account for landscape heterogeneity. Utilization of the KRR and its parameterization tailored to the goals of this study, and strict feature selection, enabled high prediction accuracy. Still, the metrics of our regression estimators were not uniform and varied across the land-cover classes. The \(R^{2}\) for the DBF was the highest, likely due to clear seasonality patterns and relatively low heterogeneity within the class. The same metric for the EBF class was the lowest, likely due to other carbon exchange process drivers and lower data availability due to high cloud cover in tropical regions. Current results are presented for each land-cover class globally, providing visibility on the unified, scalable calculation approaches and the global accuracy baselines for various land covers (Figure 6). Overall, the achieved accuracy was in line with similar studies but outperformed most of those targeting global geographical coverage (overall land cover cross-validation \(R^{2}\) = 0.73, \(RMSE\) = 1.53 gC/m\({}^{2}\)/day). Daily data accuracy has the \(R^{2}\) range between 0.42 for EBF and 0.76 for DBF (monthly data accuracy is between 0.6 for GRA and 0.84 for DBF). The models have better accuracy in those parts of the world where the number of stations is higher and, conversely, slightly worse accuracy in areas of lower station density. To resolve or minimize these deficiencies and improve accuracy for specific cases or regions, the current system allows additional training (not a local re-training) in the presence of the additional local flux observations, as suggested by [2]. Another way to improve the confidence of model data is to add other multispectral, hyperspectral, and radar satellite products, e.g., Sentinel-1, 2, or EnMap. It is expected that these new data streams can improve the accuracy of the daily data slightly but can contribute significantly to the monthly and annual data improvement. Finally, since in this study we have trained \(N\) independent models for each land cover (where \(N=3\), 5, 7). In the future, it may be better to use ensemble predictions to allow additional characteristics such as mean, median, and standard deviation. These and other activities are currently in development but not advanced enough to be included in this manuscript. Given the urgent need to decarbonize the atmosphere and the critical role of NBS in doing so, a fusion approach of eddy covariance and remote sensing data proved to be an efficient and cost-effective solution for quantifying these efforts. We hope our methods could contribute to mitigating global climate change in a cost-effective and transparent way. Conceptualization, R.Z. and A.D.; methodology, R.Z. and A.D.; software, R.Z. and A.D.; validation R.Z. ; writing--original draft preparation, R.Z., A.D., A.L.D.d.S. and O.D. ; writing--review and editing, A.D., O.D. and G.B. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. We thank [PERSON] (Chief Technical Officer at CarbonSpace Ltd.) and the IT team of CarbonSpace for developing infrastructure and supporting our calculations. We thank [PERSON] (Head of Innovation at CarbonSpace Ltd.) for extremely valuable suggestions, contributions to the paper, and overall support of the work. We thank [PERSON] (Chief Marketing Officer at CarbonSpace Ltd.) for significant contribution to the draft preparation. We would like to thank the anonymous reviewers for their valuable comments and suggestions.