## 5 Discussion  
This study provided insight into the potential of using newly produced HLS data for the retrieval of temporal variations in vegetation properties using the RTMo model, and we investigated if these temporal variations could improve GPPsIM. In addition, we investigated the information contained about vegetation properties in the multispectral HLS data compared to hyperspectral airborne data. The amount of HLS data contributing to the time-series was reduced by cloud coverage condition over the site. The cloud mask is not properly defined in the HLS data, and this was also observed in this study. The non-cloud pixels were defined based on the NDVI range (0.4 to 0.5) and NIR range (0.1 to 0.4). The NDVI range adopted in this study was based on the broad range of NDVI for vegetation, and the NIR range was based on our previous experience on the characteristics of Norway spruce during the growing period in the study area. The variations in these ranges can be observed for other species and the study period [60]. For example, the NIR reflectance can drop to below 0.1 outside the vegetation growing period and can rise to above 0.4 for grey alder tree species. Therefore, NDVI- and NIR-based filtering of cloud-free HLS data may be suitable at the local level and for shorter periods, like in this study. However, this filtering solution might be unsuitable if the study involves several tree species in a broad area and a longer study period. Therefore, the existing cloud mask of the HLS data, i.e., irrespective of tree species/land cover types, should be made more robust. The RTMo model successfully reproduced the observed HLS and airborne TOC reflectance (Figure 3). The RTMo model is essentially a well-established combination of leaf-canopy radiative transfer models (PROSPECT5 leaf model and 4 SAIL canopy reflectance model), which has been successfully used in numerous studies for the retrieval of vegetation properties. Therefore, the good fit between the observed and simulated TOC reflectance found in this study has already been reported in the literature [19; 20; 21; 22; 23; 24; 61; 62; 63; 64; 65; 66; 67]. We retrieved the time series of vegetation properties from the HLS data. We had a limited number of ground measurements for validation of the retrievals. However, the following points should be noted:  
1. A temporal increase in \(C_{\text{dm}}\) is positively correlated with \(C_{\text{ab}}\). A previous study, however, did not focus on the temporal aspects of evergreen as well as deciduous tree species, but also found a positive correlation between \(C_{\text{dm}}\) and \(C_{\text{ab}}\)[68]. 2. We observed a good fit between the retrieved vegetation properties and the ground measurements (Section 4.3) on 31 August. At least for one day in the time series, we could therefore validate the retrievals from the HLS TOC reflectance. The retrieved \(C_{\text{ab}}\), however, did not exhibit substantial temporal variation. Nevertheless, these variations followed the characteristics of the evergreen tree species, which shows young leaf development phenology during the growing season. These findings agree with those of a previous study on the global spatio-temporal distribution of leaf chlorophyll [69], which highlighted the consistency in the temporal leaf chlorophyll profile of evergreen tree species across the year with an increasing concentration within new needles in spring. Considering the points described above, we inferred a successful retrieval of vegetation properties from the HLS data. In a future study, these two points could be considered together for validation of retrievals, at least for evergreen tree species, if limited measurements are available. We compared the retrievals from HLS and airborne data. The low percentage differences in most of the vegetation properties indicated that switching from hyperspectral to multispectral observations did not prominently influence the retrieval of vegetation properties in our case.