## 4 Discussion  
The performance of three EO LAI products was evaluated at six forests across southern China. GEOV3 300 m LAI showed the best fit (\(R^{2}\) = 0.45) and the smallest bias (bias = \(-\)0.54) (Figure 3) and the most similar LAI time series seasonal dynamic variation (phase and amplitude) compared with in situ LAI measurements (Figure 5). For the GEOV2 1 km LAI product, its performance had larger bias (Figure 3) and seasonality but similar phases of the LAI time series with the in situ observations compared with GEOV3 300 m LAI products (Figures 3 and 5). The different performance of the two Copernicus products is likely due to the mismatch of the scaling between the EO pixel size and the field site. LAI is strongly non-linearly related to reflectance, making its estimation from remote sensing observations scale-dependent [76; 77]. In contrast, the core operational algorithm (neural network techniques), data filtering and smoothing processes are similar for these two products. There are differences in the method used for temporal compositing, where temporal smoothing and gap filling using a climatology are used for GEOV2 1 km and  
Figure 5: Periodic characteristics of the LAI time series from 2005 to 2017 for different sites. (**a**): Baseline values of the LAI time series; (**b**): Phase of the LAI time series; (**c**): Amplitude of the LAI time series; (**d**): Relative amplitude of the LAI time series. Point colors and shapes represent different sites. A detailed description of the calculation can be found in the Methods section of this paper. interpolation applied in GEOV3 300 m) [78]. Differences in the applied gap-filling approach between these two products do not impact our conclusion that resolution is the primary driver of performance improvement at 300 m relative to 1 km, as all gap-filled and interpolated retrievals were removed in our study. The in situ measurements were usually conducted in protected mature forest areas with high plant densities, complex canopy stratums, and rich species diversities (Figure S1, Table S1). However, at coarser spatial resolutions, pixels integrate heterogeneity over a greater diversity of landscapes, habitats, and species, distributed across a variety of stages of growth and succession [79]. Thus, fine resolution GEOV3 300 m LAI product showed lower bias and similar seasonal dynamic variation to the in-situ measurements compared to the higher bias for the GEOV2 1 km LAI product. For Collection 6 500 m MODIS LAI, our results indicate that this product does not capture the dynamics observed in situ, irrespective of the accuracy of estimated in situ LAI (Figure 3). Furthermore, it does not adequately capture the LAI seasonal dynamics (Figure 5). This result is consistent with two validation studies which both showed MODIS LAI had the poorest performance for the evergreen forests in the south of China [80,81]. The MODIS LAI values for tropical evergreen forests are severely impacted by atmospheric conditions, especially clouds during the growing season (around 42% data are influenced by the cloud in this study, Figure S8), which lead to strong noise in the input reflectance data and affect the retrieval [78]. Additionally, the reflectance saturation usually happened in dense canopies and the main algorithm is sensitive to uncertainties in atmospheric correction, particularly when red and NIR BRFs are saturated [82,83]. This means the reflectance does not contain sufficient information to estimate the LAI value [84] and leads to the instability of the LAI retrievals [14]. All of these may result in the poor performance of representation of evergreen forests in southern China for the MODIS 500 m LAI products. Furthermore, the product does not adequately capture LAI periodic characteristics in comparison to the field data and showed temporal inconsistency with GEOV2 and V3 products (Figure 5). [PERSON] (2017) also found the large temporal inconsistency between existing global LAI products at a longer time scale [85].