# Scaling of FAPAR from the Field to the Satellite
## 4 Results and Analysis
### Discussion  
The scaling methodology proposed in the study directly addressed the spatial and temporal mismatches between _in-situ_ and coarse-resolution satellite data. However, there are still many issues associated with the further developments of the scaling methodology or the validation attempts. First, as the measurements of skylight proportions were unavailable here, the skylight proportions were decomposed into the part affected by SZA and the part affected by atmosphere. The effects of cloud were not considered here for simplicity. In further studies, when measurements of skylight proportions are available, it could be used in the model directly. Second, the leaf angle distribution of maize was assumed to be spherical (\(G\) = 0.5) and the effects of SZA on the parameter \(G\) were assumed to be constant in the development of the temporal normalization method. This assumption was verified as feasible in developing FAPAR-P model. However, the leaf angle distribution of maize is also found to be variable with seed orientation [58] and phenological stages [59]. The use of more precise description of leaf angle distribution could thus be considered in other work. Similarly, for other types of vegetation (e.g., grass characterized by eercophile leaf orientation), the assumption for the parameter \(G\) should be modified in applying the temporal normalization method. Figure 13: Scatter plot of aggregated ASTER FAPAR and MODIS FAPAR. Third, the atmospheric visibilities in temporal normalization were simplified into three typical cases of mildly hazy (5 km), moderately clear (15 km) and clear air conditions (30 km). It is practical because the normalization of FAPAR is not sensitive to the VIS and three VIS cases are distinguishable. However, the simplification could be a potential source of error. In further applications where accurate VIS data are available, more precise classification of VIS can be used. Fourth, the estimated reference FAPAR data come with uncertainties from measurement errors, temporal correction, geometric errors and the linear fit. The errors inherent in the measurement were identified as a major source of error. The temporal corrections, including both temporal normalization and interpolation, introduced low systematic errors and were found to reduce the uncertainties in measurement significantly. The systematic error of the fine-resolution reference FAPAR was thus very low with RMSE = 0.077, which could be regarded as acceptable. The errors of the coarse-resolution reference FAPAR would be further decreased as the fit errors and geometric errors were decreased in spatial aggregation [35; 60]. Besides, the NDVI-FAPAR relationship established on maize fields were applied to the entire study area, which would also be a potential source of error. However, the error was limited as the study area was dominated by maize. In heterogeneous areas, site-specific functions of transferring NDVI to FAPAR should be established. From a general perspective, further efforts could be exerted on long lasting _in-situ_ data collection and extending the scaling method to more land cover types. Figure 14: (**a–m**)Time series of MODIS FAPAR compared to the upscaled ASTER FAPAR at 13 _in-situ_ sites (the bars represent one standard deviation (STD) of FAPAR values of ASTER pixels within the 1 km MODIS pixel cell). Last, the suitability of the proposed methods, especially temporal correction methods, should be discussed. On the one hand, the differences in ground measurements of FAPAR leads to the different requirements of temporal normalization. The proposed temporal normalization method is targeted at the measurement by energy balance monitoring. On the other hand, differences in FAPAR definitions between satellite products and ground measurements should be considered. For the satellite products that include both direct and diffuse FAPAR (e.g., MODIS\(\backslash\)MISR), the proposed methods are feasible and valid.