## 3 Materials and Methods
### Data
#### 3.1.2 Ground Measurements  
We obtained half-hourly eddy covariance (EC) measurements of net ecosystem exchange (NEE) to predict half-hourly GPP and standardized meteorological variables for the spruce forest stand during the 2016 growing season. The EC technique (40) was applied to measure turbulent fluxes of CO\({}_{2}\) at the Blly Kriz forest site. The EC system consisted of Gill ultrasonic anemometer (HS-50, Gill Instruments, Hampshire, UK) and a LI-COR infrared gas analyzer (LI-7200, LI-COR, Lincoln, NE, USA), and was installed on a meteorological tower at 20.5 m as of 1 October 2013 and moved to 25 m as of 7 June 2016 above ground level. The EC raw data measured at 20 Hz were processed using spike detection and removal (41), time lag compensation, sonic temperature correction (42), and high- (43,44) and low (45)-frequency spectral corrections. Coordinates were rotated using the planar-fit method (46). Fluxes were computed at half-hourly time intervals using the block averaging method. Fetch filteringwas applied to exclude half hours for which more than 30% of the signal originated from outside of the target area. This was achieved by comparing fetch distance at a given upwind direction with estimated distance providing 70% contribution to the EC fluxes using footprint modeling [47; 48]. Fetch distance for all directions is represented as an ecosystem boundary, i.e., the target area (Figure 1), that delimits the area that is sufficiently homogeneous considering the canopy height and the species represented (Norway spruce at the Bily Kriz site). All EC processing and calculations were performed using EddyPro software (v6.2.0, LI-COR, Lincoln, NE, USA). A thorough data quality checking procedure was applied to EC measurements in this study using R package openeddy (R Core Team, 2020; [[https://github.com/Isigut/openeddy](https://github.com/Isigut/openeddy)]([https://github.com/Isigut/openeddy](https://github.com/Isigut/openeddy)); see [PERSON] et al. [32] for details). Measured CO\({}_{2}\) fluxes were filtered based on the friction velocity threshold computed using the moving point method [49]. After this filtering, half-hourly CO\({}_{2}\) fluxes were assumed to represent the NEE (\(\mu\)mol CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\)). Caps were filled using marginal distribution sampling [50] and NEE was partitioned into GPP (hereafter, GPP\({}_{\rm EC}\)) and ecosystem respiration according to [PERSON] et al. [6] using the R package REddyProc [51]. Half-hourly GPP\({}_{\rm EC}\) were then aggregated to daily sums of carbon, expressed as g C m\({}^{-2}\) s\({}^{-1}\). Meteorological variables included half-hourly data of air temperature (T\({}_{\rm a}\), in \({}^{\circ}\)C), TOC incoming shortwave radiation (R\({}_{\rm in}\), in W m\({}^{-2}\)), TOC incoming longwave radiation (R\({}_{\rm li}\), in W m\({}^{-2}\)), vapor pressure deficit (e\({}_{\rm a}\), in hPa), air pressure (p, in hPa), and wind speed (u, in m s\({}^{-1}\)) for each day in the vegetation growing season of 2016. We additionally obtained half-hourly soil water content (SWC, in %) from the nine sensors installed at a 5 cm depth at different locations within the target area. For each half-hour, SWC values from all nine sensors were averaged to obtain the representative value of soil moisture of the target area. The half-hourly meteorological variables, mean SWC, along with HLS-, CASI-, and SASI-derived information served as the inputs to the SCOPE model to estimate (or simulate) half-hourly GPP, which was aggregated to daily sums of carbon (Section 3.3). We refer to simulated GPP as GPP\({}_{\rm SIM}\) hereafter. We validated daily GPP\({}_{\rm SIM}\) against daily GPP\({}_{\rm EC}\). We also compared daily GPP\({}_{\rm SIM}\) with daily means of half-hourly measurements of meteorological variables to show the relationship between them. Table 1 lists the summary statistics of daily meteorological variables during the 2016 vegetation growing season.