## 3 Results
### Differences in Model Accuracy Between Bione Types
The underestimation of FAPAR\({}_{\text{FVC}}\) was more severe in both boreal forest and tropical broadleaf evergreen forest, with an MPE of \(-19.1\%\) and \(-29.5\%\), respectively, which were \(894.8\%\) and \(326.9\%\) lower compared to FAPAR\({}_{\text{MOD}}\) (Figure 4 and Table 2). To visually evaluate the comprehensive accuracy of the three FAPAR models in terms of SD (Standard deviation), R (Correlation coefficient), and CRMSE (Centered root mean squared error) across various biome types [52], Taylor diagrams were generated using the SM module from the sklearn.metrics library in Python [53]. FAPAR\({}_{\text{FVC}}\) exhibited superior comprehensive accuracy in grass savanna, arctic tundra, cropland, temperate mixed forest, tallgrass prairie, and desert ecosystems. In contrast, FAPAR\({}_{\text{LAI}}\) showed better integrated performance in tropical broadleaf evergreen forest and temperate needleleaf forest environments and FAPAR\({}_{\text{MOD}}\) achieved optimal accuracy in boreal forest regions (Figure 5). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Vegetation Type** & **Site Name** & **Model** & \(\mathbf{R^{2}}\) & **RMSE** & **Bias** & **RPIQ** & **MAPE (\%)** & **MPE (\%)** \\ \hline \multirow{3}{*}{grass savanna} & Dahra + & FAPAR\({}_{\text{MOD}}\) & 0.90 & 0.20 & 0.18 & 1.92 & 333.75 & 333.75 \\  & Tseseke & FAPAR\({}_{\text{LAI}}\) & 0.91 & 0.13 & 0.11 & 2.88 & 195.71 & 195.38 \\  & Tseseke & FAPAR\({}_{\text{FVC}}\) & 0.94 & 0.10 & 0.08 & 3.85 & 142.72 & 141.36 \\  & FAPAR\({}_{\text{MOD}}\) & 0.23 & 0.30 & 0.28 & 0.68 & 168.30 & 168.30 \\ \multirow{3}{*}{arctic tundra} & TUND & FAPAR\({}_{\text{LAI}}\) & 0.02 & 0.19 & 0.14 & 1.07 & 109.16 & 107.56 \\  & FAPAR\({}_{\text{FVC}}\) & 0.61 & 0.10 & 0.05 & 2.10 & 50.52 & 45.24 \\  & FAPAR\({}_{\text{MOD}}\) & 0.87 & 0.25 & 0.20 & 1.86 & 350.12 & 350.12 \\ cropland (corn and & & FAPAR\({}_{\text{LAI}}\) & 0.89 & 0.16 & 0.04 & 3.01 & 217.02 & 203.62 \\ soybean) & & FAPAR\({}_{\text{FVC}}\) & 0.93 & 0.13 & \(-0.01\) & 3.64 & 154.70 & 134.37 \\  & FAPAR\({}_{\text{MOD}}\) & 0.75 & 0.22 & 0.19 & 1.97 & 43.99 & 41.30 \\  & CHEQ & FAPAR\({}_{\text{LAI}}\) & 0.83 & 0.21 & 0.19 & 2.08 & 39.97 & 36.32 \\ temperate mixed & & FAPAR\({}_{\text{FVC}}\) & 0.84 & 0.10 & 0.03 & 4.55 & 19.19 & 4.36 \\ forest & & FAPAR\({}_{\text{MOD}}\) & 0.76 & 0.31 & 0.23 & 1.96 & 73.19 & 72.91 \\  & HARY & FAPAR\({}_{\text{LAI}}\) & 0.79 & 0.27 & 0.21 & 2.22 & 65.05 & 64.50 \\  & & FAPAR\({}_{\text{FVC}}\) & 0.78 & 0.16 & \(-0.06\) & 3.90 & 30.16 & \(-10.31\) \\  & & FAPAR\({}_{\text{MOD}}\) & 0.78 & 0.16 & 0.09 & 2.12 & 52.61 & 50.64 \\ tallgrass prairie & KONZ & FAPAR\({}_{\text{LAI}}\) & 0.83 & 0.15 & \(-0.07\) & 2.27 & 38.69 & 9.07 \\  & & FAPAR\({}_{\text{FVC}}\) & 0.79 & 0.19 & \(-0.14\) & 1.80 & 37.43 & \(-9.67\) \\  & & FAPAR\({}_{\text{MOD}}\) & 0.71 & 0.10 & \(-0.01\) & 0.20 & 8.53 & \(-1.92\) \\ boreal forest & NOBS & FAPAR\({}_{\text{LAI}}\) & 0.36 & 0.10 & \(-0.04\) & 0.20 & 8.88 & \(-4.65\) \\  & & FAPAR\({}_{\text{FVC}}\) & 0.47 & 0.21 & \(-0.15\) & 0.10 & 19.31 & \(-19.10\) \\  & & FAPAR\({}_{\text{MOD}}\) & 0.48 & 0.16 & 0.16 & 0.21 & 111.18 & 111.18 \\ desert & SEVI & FAPAR\({}_{\text{LAI}}\) & 0.42 & 0.05 & 0.04 & 0.66 & 34.01 & 31.16 \\  & FAPAR\({}_{\text{FVC}}\) & 0.59 & 0.04 & 0.03 & 0.84 & 24.16 & 19.66 \\ tropical broadleaf & & FAPAR\({}_{\text{MOD}}\) & - & 0.07 & \(-0.07\) & - & 6.91 & \(-6.91\) \\ evergreen forest & TAPA & FAPAR\({}_{\text{FVC}}\) & - & 0.02 & \(-0.02\) & - & 1.86 & \(-1.85\) \\ temperate needleleaf & & FAPAR\({}_{\text{MOD}}\) & - & 0.23 & 0.15 & - & 37.75 & 25.10 \\ forest & METL & FAPAR\({}_{\text{LAI}}\) & - & 0.18 & 0.15 & - & 27.28 & 25.43 \\  & FAPAR\({}_{\text{FVC}}\) & - & 0.18 & \(-0.09\) & - & 22.17 & \(-14.99\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The accuracies for FAPAR estimation in different biome types.