## 4 Discussion
### Implications for Ecosystem Respiration and Upscaling of Flux Measurements  
Although the source area of a nighttime footprint is larger than that of daytime due to the influence of atmospheric stability, the overlap areas between daytime and nighttime still account for a large proportion (Figure 2d). These largely overlapped source areas will be beneficial to the partitioning of the net exchange change (NEE) into ecosystem respiration and gross primary production (GPP). Therefore, the O\({}_{\text{day\_night}}\) can potentially be a quality flag to the GPP derived from EC flux data. The O\({}_{\text{season\_day}}\) and O\({}_{\text{season\_night}}\) represent the degree to which monthly footprint climatologies of daytime or nighttime overlapped in a year. Their low values indicated large differences in the spatial distribution of the source areas (Figure 2d), which is the root cause of the strong seasonal variation of spatial representation with the FTVT or EV\({}_{\text{bias}}\) in the part of the results (Figures 7 and 8). Hence, these indices can be used to detect the seasonal consistency of the source area distribution.  
The upscaling of the EC measurements provides an independent approach to quantifying the carbon and water exchange between terrestrial ecosystems and the atmosphere [66]. A commonly used approach for upscaling is to integrate the flux measurements with a remote sensing model. However, biases would be introduced by the assumption that the EC measurement represents a fixed area near the flux tower site [32]. To better connect the EC measurements with remote sensing grids, the temporal variation and spatial heterogeneity of the source area should be noticed at the same time. Recent studies integrated the footprint weighting with the vegetation or land surface properties to upscale the site-level flux data to a region. [PERSON] et al. used a footprint-weighting method to optimize a regression-based NEE-diagnosed model [67]. By integrating with high spatial-temporal resolution remote sensing data, 16 flux sites in North America were upscaled to a continental scale in their study. [PERSON] et al. related the footprint-weighted surface properties to the sensible heat flux and latent heat flux observations taken from airborne properties [68]. With a  
Figure 8: Sensor location bias of EVI (EVI\({}_{\text{bias}}\)) for different months ranging from 30 to 3000 m (**a**) and kernel density distribution of the multi-year averaged EVI\({}_{\text{bias}}\) for circles in 250 m, 500 m, 1000 m, 1500 m, 2000 m and 3000 m (**b**).  
boosted regression tree technique, these fluxes were then upscaled to high-resolution flux maps over the North Slope of Alaska. [PERSON] et al. evaluated the effect of varying flux footprints on CH4 flux observation [69]. In their study, the normalized difference water index was determined to infer the representativeness of CH4 flux. [PERSON] et al. adopted a regression model to upscale the EC-measured GPP and ER of peatland with remote sensing data [70]. In their work, the daily vegetation and water indices derived from high-resolution Sentinel-2 data were weighted by the footprint model to match them with EC observations. Hence, we can conclude that high spatial and temporal resolution vegetation maps and indices are highly needed to upscale surface flux.