## 1 Introduction  
Nature-based solutions (NBS) are increasingly recognized as an essential tool in combating global warming as they remove greenhouse gases (GHG) from the atmosphere [1], reduce GHG emissions through sustainable land use practices, and store carbon in the soil and biomass [2]. While NBS are becoming quite advanced and continuously going through significant developments, the methods for accurately quantifying their resulting benefits are still lagging [3]. A vast majority of the existing projects use biometric soil or tree survey data to estimate carbon stock changes [4]. These methods provide solid estimates of the sequestered carbon but come with multiple important limitations. They typically describe either soil layer or partial canopy layer (e.g., above ground), with time resolution limited to multiple months and years and spatial resolution limited to a particular plot where such measurements have been performed. As a result of such limitations, these methods may not be best suited for large-scale ongoing NBS quantification [2]. Moreover, the measurements required by these methods for a meaningful time-resolved, large-scale assessment are expensive and unaffordable for most users. A pure modeling approach employing either Earth system models or terrestrial biosphere models has been used as an alternative to direct measurements on a large scale [5; 6], but the spatial resolution of these models is too coarse, while the uncertainty remains high due to their inability to respond timely to actual changes in the ecosystem. Likewise, satellites that measure carbon dioxide (CO\({}_{2}\)) concentrations and are used in inverse modeling to map GHG emission and sequestration [7; 8] have coarse spatial resolution and hence can hardly be used for NBS assessment at the farm or forest stand level. Optical, radar, and lidar satellites provide a scalable solution for mapping soil carbon [9] and carbon fluxes at a much finer spatial resolution [10; 11] and do have the potential to help large-scale space- and time-resolved NBS assessments if anchored in accurate ground measurements. The current remote-sensing-based studies typically use allometric/biometric data for machine learning (ML) model training [2]. The amount of ground data required for capturing the landscape heterogeneity and temporal variation is challenging to sustain and scale. The models thus inherit the disadvantages of the biometric measurements. There is, however, another potential solution that combines many advantages of the above methods while overcoming some of their limitations and can result in a high spatial resolution, scalability to the regional level and practical affordability. This can be achieved by blending the direct highly time-resolved small-scale observations from the numerous eddy covariance flux towers [2] and the less direct but large-scale remote sensing methods. Considerable efforts have been made in this area lately to improve the quantification of the spatiotemporal CO\({}_{2}\) patterns, employing multiple different approaches vs. existing estimates to help resolve the majority of issues with current carbon accounting methods in the land sector. For example, [12] evaluate how seven light-use-efficiency (LUE) models captured the spatiotemporal gross primary productivity (GPP) variations from the LaThuile FLUXNET dataset. This study used data from 157 eddy covariance (EC) towers located in the six major terrestrial biomes (evergreen broadleaf forest (EBF), deciduous broadleaf forest (DBF), mixed forest (MF), evergreen needle leaf forest (ENF), shrubland (SHR), and grassland (GRA)). None of the seven models matched well with estimated GPP based on overall EC measurements. The models explained 41-57% GPP variations in the study sites. Reference [13] evaluated four LUE models for GPP estimation and compared them with data obtained by EC in 51 sites with diverse vegetation types. The average daily \(R^{2}\) for all models and all sites was about 66%.