## 3 Results  
### Accuracy of the LAI Products Magnitude  
Satellite derived mean LAI estimates were typically lower than the field measurements, which varied between 3.2 and 5.6 m\({}^{2}\) m\({}^{-2}\) (Table 2). GEOV3 300 m estimates were 10% lower than field measurements (mean = 4.50 m\({}^{2}\) m\({}^{-2}\)), MODIS 500m were 30% lower (mean = 3.6 m\({}^{2}\) m\({}^{-2}\) and GEOV2 1 km were 40% lower (mean = 3.07 m\({}^{2}\) m\({}^{-2}\)) (Figure 2; Table 2). LAI mean and relative uncertainty increased with product resolution: GEOV3 300m (0.22, 7%) < MODIS 500 m (0.37, 10%) < GEOV2 1 km (0.56, 19%). Figure 2: Time series of the LAI for the field, GEOV3 300 m, GEOV2 1 km, and the MODIS 500 m for different sites. The timespan of the LAI time series is from 2005 to 2017. Each field data point represented the mean value of the measurements from the sample subplots in each month. “N” denoted the number of total sample measurements for each site, including the repeat measurements in each month at each subplot. Field mean LAI was best captured by GEOV3 300 m (\(R^{2}\) = 0.45; Figure 3), followed by GEOV2 1 km (\(R^{2}\) = 0.36), and MODIS 500 m (\(R^{2}\) = 0.20) for all of these six forests in this region. This performance varied between forests and all of these products showed the highest \(R^{2}\) (Table 2) for the forests at SNF, which showed more LAI seasonal variation (Figure 2). GEOV3 300 m LAI also had the lowest RMSE and bias (RMSE = 1.21, bias = \(-\)0.41) compared with GEOV2 1 km LAI (RMSE = 2.32, bias = \(-\)2.04) and MODIS 500m LAI (RMSE = 2.29, bias = \(-\)1.47) (Figure 3, Table 2). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**LAI Source** & **Metric Statics** & **ALF** & **BNF** & **DHF** & **GGF** & **HSF** & **SNF** & **Overall** \\ \hline \multirow{4}{*}{Field (20–100 m)} & Mean & 4.18 & 5.55 & 4.90 & 3.18 & 3.44 & 4.11 & 5.1 \\  & Measurements error (std) & 0.46 & 0.58 & 0.89 & 0.5 & 0.51 & 0.57 & 0.59 \\  & Relative error (std/mean) & 0.11 & 0.11 & 0.18 & 0.16 & 0.15 & 0.14 & 0.12 \\  & N & 38 & 571 & 57 & 14 & 96 & 17 & 793 \\ \multirow{4}{*}{GEOV3 300 m} & Mean & 3.28 & 5.15 & 3.82 & 2.38 & 2.94 & 3.34 & 4.50 \\  & Product uncertainty & (RMSE) & 0.13 & 0.20 & 0.12 & 0.04 & 0.55 & 0.30 & 0.22 \\  & Relative uncertainty & & & & & & & & \\  & (RMSE/Mean) & 0.04 & 0.04 & 0.03 & 0.02 & 0.19 & 0.09 & 0.07 \\  & Bias (mean) & \(-\)0.58 & \(-\)0.40 & \(-\)0.89 & \(-\)1.18 & \(-\)0.94 & \(-\)0.66 & \(-\)0.54 \\  & \(r\) (median) & 0.17 & 0.24 & 0.09 & 0.65 & 0.45 & 0.36 & 0.26 \\  & \(R^{2}\) & 0.12 & 0.18 & 0.53 & 0.05 & 0.21 & 0.93 & 0.45 \\  & N & 20 & 191 & 23 & 11 & 30 & 11 & 286 \\ \multirow{4}{*}{GEOV2 1 km} & Mean & 3.3 & 3.35 & 2.92 & 0.82 & 1.72 & 2.96 & 3.07 \\  & Product uncertainty & & & & & & & \\  & (RMSE) & 0.54 & 0.60 & 0.60 & 0.27 & 0.33 & 0.77 & 0.56 \\  & Relative uncertainty & & & & & & & \\  & (RMSE/Mean) & 0.16 & 0.18 & 0.21 & 0.33 & 0.19 & 0.26 & 0.19 \\  & Bias(mean) & \(-\)0.88 & \(-\)2.20 & \(-\)1.97 & \(-\)2.36 & \(-\)1.72 & \(-\)1.16 & \(-\)2.04 \\  & \(r\) (median) & 0.56 & 0.28 & 0.37 & 0.16 & 0.22 & 0.58 & 0.28 \\  & \(R^{2}\) & 0.18 & 0.22 & 0.15 & 0.38 & 0.30 & 0.74 & 0.36 \\  & N & 38 & 571 & 57 & 14 & 96 & 17 & 793 \\ \multirow{4}{*}{MODIS 500 m} & Mean & 3.1 & 4.1 & 3.3 & 1.45 & 1.29 & 1.72 & 3.60 \\  & Product uncertainty (std) & 0.45 & 0.38 & 0.48 & 0.66 & 0.16 & 0.40 & 0.37 \\  & Relative uncertainty & & & & & & & \\  & (std/mean) & 0.15 & 0.09 & 0.14 & 0.44 & 0.12 & 0.23 & 0.10 \\  & Bias (mean) & \(-\)0.99 & \(-\)1.35 & \(-\)1.54 & \(-\)1.94 & \(-\)2.09 & \(-\)2.38 & \(-\)1.47 \\  & \(r\) (median) & 0.37 & 0.24 & 0.39 & 0.26 & 0.07 & 0.13 & 0.21 \\  & \(R^{2}\) & 0.03 & 0.12 & 0.10 & 0.47 & 0.16 & 0.25 & 0.20 \\  & N & 33 & 506 & 52 & 7 & 85 & 17 & 700 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Mean site LAI values (m\({}^{2}\) m\({}^{-2}\)) and their statistical metrics calculated using months which have both field and EO LAI values from 2005 to 2017.