## 4 Results
### Retrieval of Vegetation Properties  
Figure 4 illustrates the variations in the retrieved vegetation properties during the 2016 vegetation growing season. For comparison, Figure 4 also includes the retrieval from airborne data on 31 August 2016, and the minimum, maximum, and mean values obtained from the ground measurements. The y-axis represents the non-informative prior distribution range of each vegetation property (Table 2). An optimization process constrained the RTMo input by the TOC reflectance (either HLS or airborne). Each vegetation property was retrieved in a narrow range during the growing season, indicating the reduction in prior uncertainty after the optimization. From HLS data, we were able to retrieve the vegetation properties at least once per month. Only in June was it not possible to find a reliable observation for use in the retrieval. These retrievals provide insight into the temporal patterns of vegetation properties. Except for \(C_{\rm{ab}}\) and \(C_{\rm{dm}}\), we did not observe any apparent temporal variation in any of the studied vegetation properties. We retrieved a low estimate of \(C_{\rm{ab}}\) at the start of the growing season (23.4 \(\upmu\)g cm\({}^{-2}\) on 3 April), which increased to 50 \(\upmu\)g cm\({}^{-2}\) in mid-season (5 July), and then showed an overall decreasing trend toward the end. \(C_{\rm{dm}}\) followed a similar pattern as \(C_{\rm{ab}}\) during the growing season, i.e., both decreased or increased  
Figure 3: Scatterplot between the observed and simulated top-of-canopy (TOC) reflectance values for each of the 14 days during the 2016 vegetation growing season using the SCOPE radiative transfer module (RTMo). The last plot in the lowermost row shows the result for the airborne data; the other plots show the results for the harmonized Landsat-8 and Sentinel-2 (HLS) data. Values are color-coded by wavelength. The observed TOC reflectance represents the mean reflectance within the target area defined by the flux footprint (delineated in Figure 1). The correlation coefficient (COR) and Nash–Sutchlife Efficeince (NSE) between observed and simulated TOC reflectance are shown in each plot. together between any two successive days. The retrieval of \(LAI\) showed consistency during the growing season, and we retrieved the \(LAI\) between 7 and 8 m\({}^{2}\) m\({}^{-2}\), except for 23 May, where it dropped to 6 m\({}^{2}\) m\({}^{-2}\). This drop is, however, not an unrealistic estimate of \(LAI\) for Norway spruce. We obtained the percentage difference between the vegetation properties retrieved from the HLS and airborne data using Equation (5) to show the similarity between them. We observed low percentage differences in most of the vegetation properties. The percent differences for \(C_{\text{ab}}\), \(C_{\text{dm}}\), \(C_{\text{ca}}\), and \(LAI\) were less than 10%, with the lowest being 0.3% for \(C_{\text{ab}}\). \(C_{\text{w}}\) and \(LIDF_{\text{a}}\) showed a difference of 25%. The maximum differences in \(C_{\text{s}}\) and \(N\) were observed at 63% and 42%, respectively. Based on the percentage difference, we compared the vegetation properties retrieved from the HLS data on 31 August with the mean of the available ground measurements (Table 2 and Figure 4). We found that the percentage differences for \(C_{\text{ab}}\), \(C_{\text{w}}\), \(C_{\text{dm}}\), \(C_{\text{ca}}\), and \(LAI\) were less than 30%. We found the lowest difference for \(LAI\), with 7%, whereas \(C_{\text{ab}}\) showed a difference of 17%. These percentage differences appear to be acceptable given the aggregated effect of the target area compared with the  
Figure 4: **(a-i)** Retrieved vegetation properties (\(C_{\text{ab}}\), \(C_{\text{w}}\), \(C_{\text{dm}}\), \(C_{\text{s}}\), \(C_{\text{ca}}\), \(LAI\), \(LIDF\), and \(N\)); **(j)** maximum rate of carboxylation (\(V_{\text{cmax}}\)) from the available time series of the harmonized Landsat-8 and Sentinel-2 data during the 2016 growing season.