# Generation of High Resolution Vegetation Productivity from a Downscaling Method
## 4 Discussion
### Uncertainties Analysis  
The errors of the input data of the downscaling process would have influence on the accuracy of the downscaling results. Although both GLASS LAI/FPAR products (RMSE = 0.7848 and R\({}^{2}\) = 0.8095 for the LAI product, R\({}^{2}\) = 0.9292 and RMSE =0.0716 for the FPAR product) [6,7] and MODIS LAI/FPAR products (RMSE = 1.1173 and R\({}^{2}\) = 0.6705 for the LAI product, R\({}^{2}\) = 0.8048 and RMSE = 0.1276 for the FPAR product) [3,4] have high accuracy, the errors of the GLASS and MODIS product certainly have some impact on the accuracy of downscaling FPAR and LAI, and then have some influence in the GPP and NPP estimates. Second, there are stripes in the original Landsat ETM+ images, the destripe process may introduce errors in the high spatial resolution surface reflectance data, which will bring additional errors to the final high spatial and temporal LAI/FPAR products. The process of FPAR and LAI downscaling would also bring some uncertainties in estimating high resolution GPP and NPP. In our study, the high spatial resolution of FPAR and LAI were retrieved by Cubist regression tree approach from Landsat ETM+ data using MODIS/GLASS FPAR and LAI products as a reference. Although the Cubist regression tree have the ability to predict outside the range of values in the training dataset, the accuracy of the regression tree approach still relied on the data quality and distribution of training dataset [37]. If high quality data are limited, the regression tree approach may not be stable for FPAR and LAI retrievals. Because of large heterogeneity of land surface in the study area, most of GLASS/MODIS 1 km pixels are mixed pixels, the low fraction of vegetation cover land cover types (such as bare land, grassland and artificial land) existed in a mixed GLASS/MODIS 1 km pixel will lead to the low values of the reference data, i.e., GLASS/MODIS 1 km FPAR and LAI, in the growing season [60,61]. Therefore, the FPAR and LAI estimates from the regression tree approach may not be able to describe the high values of FPAR and LAI, as shown in Figures 6 and 7. The precision of reference high spatial resolution data and the spatial representativeness of ground observation data are also related to the performance assessment of the downscaling method. In this paper, high resolution (30 m) FPAR/LAI retrievals based on the law of energy conservation and the concept of recollision probability were used to validate the downscaled FPAR and LAI. Some errors exist in geometric correction when retrieving the FPAR and LAI from the Chinese HJ-1B satellite data, which could \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Biome** & **Validation Data** & **R\({}^{2}\)** & **RMSE (g C\(\cdot\)m\({}^{-2}\cdot\)d\({}^{-1}\))** & **References** \\ \hline Medicago & & 0.66 & 1.48 & \\ barley & & 0.76 & 1.51 & \\ Maize & & 0.14 & 3.92 & \\ Durum wheat & MOD17 A2 GPP & 0.79 & 1.47 & [PERSON] et al., 2018 [28]. \\ Pea & & 0.55 & 1.44 & \\ Spring wheat & & 0.67 & 1.39 & \\ Winter wheat & & 0.81 & 1.66 & \\ \hline Cropland & & 0.80 & & \\ Grass & & 0.75 & & [PERSON] et al., 2018 [27]. \\ EBF & & 0.66 & & \\ DBF & & 0.63 & & \\ Wheat & Landsat derived & 0.85 & & [PERSON], 2011 [26]. \\ \hline Cropland (mainly maize) & & 0.89(D\_GPP\_GLASS), & 3.14 (D\_GPP\_GLASS), & \\  & & 0.87 (D\_GPP\_MODIS) & 2.52 (D\_GPP\_MODIS) & \\ Orchard & & 0.68(D\_GPP\_GLASS), & 0.87 (D\_GPP\_GLASS), & This paper \\ Ground observed & & 0.71 (D\_GPP\_MODIS) & 0.89 (D\_GPP\_MODIS) & \\ Wetland & GPP & 0.90 (D\_GPP\_GLASS), & 0.90 (D\_GPP\_GLASS), & \\  & & 0.75(D\_GPP\_MODIS) & 1.02 (D\_GPP\_MODIS) & \\ Vegetable field & & 0.74 (D\_GPP\_GLASS), & 1.30 (D\_GPP\_GLASS), & \\  & & 0.74 (D\_GPP\_MODIS) & 1.47 (D\_GPP\_MODIS) & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the GPP estimates from spatial and temporal adaptive reflectance fusion model (STARFM). lead to some uncertainty in the downscaled FPAR and LAI validation.