## 4 Conclusions  
There has been a drastic rise in NBS development in recent years [3]. Although the methods for evaluating NBS are advancing, there are still certain gaps that need to be addressed [2]. Previous studies found that most of the current methods for quantifying NBS benefits use biometric soil or tree survey data to estimate carbon stock changes. These methods do not enable seamless large-scale assessments, while associated regular measurements make the methods financially unsustainable for most players in the land sector. Modeling approaches based on Earth system models or terrestrial biosphere models, as well as inverse modeling based on CO\({}_{2}\) concentrations data, have spatial resolution too coarse to be used locally. The current remote-sensing-based approaches mainly rely on allometric data as ground truth and therefore involve challenges with capturing landscape heterogeneity and temporal variation that negatively impact scalability. As the result of the analysis from the recent studies, it is fair to say that the approaches based on observations from eddy covariance towers and remote sensing are the best fit to enable affordable, high spatial resolution, globally scalable CO\({}_{2}\) estimates. We analyzed the studies representing a vast amount of scientific efforts targeted on these approaches and identified the significant challenges. Here, we presented an improved approach to quantifying carbon fluxes by blending eddy covariance data with moderate resolution satellite data and weather data. Our method (1) is scalable due to the validation design based on a separate set of eddy covariance stations, (2) features a high spatial and temporal resolution of the Landsat data and set of meteorological variables, and (3) delivers robust and accurate  
Figure 5: Time series of observed NEE and the predicted NEE based on remote sensing and meteorological features models per each land cover: cropland (CRO); grassland (GRA); evergreen needleleaf forest (ENF); deciduous broadleaf forest (DBF); evergreen broadleaf forest (EBF); mixed forest (MF); wetland (WET); savanna (SAV). predictions due to improved data quality control, advanced machine learning techniques, and rigorous validation. Fluxnet towers are expected to estimate NEE values for homogeneous land covers that are stable over time. Therefore, in order to apply our method the regions should be thoroughly controlled for these parameters. In addition, models can only be applied to the areas with known land cover. Application to areas with different land cover may skew the results significantly. At the same time, the eddy covariance system captures the net ecosystem exchange, which accounts for total CO\({}_{2}\) fluxes, including those related to the heterotrophic respiration process. So if the AI remote sensing model was trained with EC NEE, it consequently accounts for heterotrophic respiration. In terms of global coverage, it is true that the sites we use may not be representative for all possible climate zones. However, since we use several validation folds that are blindly compiled (we do not know which station from which region is in which fold), we can assume that the regression we have constructed (ensemble of models) may be able to describe the underlying behavior processes for NEE. Such estimates then can be applied globally. A exact conclusion can be drawn using new EC data from independent sources and other climate zones. Further in the Discussion, we suggest the key reasons for these improvements and also list our opinion on the current limitations of the proposed approach. Our methodology employed eddy covariance data from the Fluxnet network. Since the network has a standardized methodology for data acquisition and processing, we collected a robust and consistent training dataset, typically unavailable for more traditional carbon accounting methods based on biomass and soil sampling.