## 4 Data Processing
### Pre-Processing  
#### 4.2.1 Screening Outliers  
The reliability of an analysis based on measurements clearly depends on the accuracy of the inputs, so outliers and doubtful items should be culled out of the data. As explained above and in Appendix A.2, FAPAR values are themselves obtained through a model inversion procedure, and their accuracy and reliability can be quantitatively assessed by inspecting two pieces of information: the variance associated with the reported values, and the value of the cost function at the end of the inversion process. Whenever the latter exceeds a given threshold (in this case, set to the 90 th percentile of its distribution), the mismatch between the model and the empirical evidence is deemed excessive and the corresponding data items are discarded, following the approach described in [PERSON] et al. [76]. This is preferable to traditional statistical methods (such as box-and-whisker statistics) because it eliminates not only obvious outliers but also data values that may look reasonable but are probably questionable: a high cost function value betrays a mismatch between the JRC-TIP model and the input data and often results in one or more of the model parameters taking on unusual or obviously unbelievable values. This process, thus, tends to discard more data points than would be eliminated on the basis of extreme values only but yields a time series of high quality. The threshold of acceptability can be fine-tuned by inspecting the histogram of cost function values, which typically exhibits a long tail of occasional large values, so the selection of a particular value within that tail is not very crucial, as it screens out a small number of points. Figure 3 shows that keeping data points associated with slightly higher cost function values (for instance 0.12 instead of 0.10) would add a few more data points to the time series, though most of them cluster in late 2016 and early 2017, at a time when they would not provide much additional constraint on the model inversion. Selecting much smaller values does have a significant impact on the number of remaining data points, as documented in Table 1. In practice, the choice should be driven by the requirements of the user of the information: is it better to have very reliable data on a smaller number of cases, or more approximate and potentially less reliable information more frequently? The interesting feature of this approach is that there is no need to impose a single solution for all possible problems: the process can be customized to meet the specific requirements of the user. Figure 3 shows the outcome of this process in a particular case: The top panel exhibits the time series of FAPAR values for the Skukuza flux tower for Paths 168 and 169, while the bottom panel shows the associated JRC-TIP cost function values. In this case, the threshold of acceptable cost function values is set to 0.1. All cost values larger than this threshold and the corresponding FAPAR values in the top panel, are marked in red. This process eliminates the occasional slightly negative FAPAR values, but also other values that might otherwise appear to be reasonable at face value. Of course, a lower threshold would prune even more data points and jeopardize the feasibility of characterizing the vegetative season, while a larger threshold would retain more data but possibly lead to an incorrect interpretation because of the presence of dubious values. Table 1 shows how selecting progressively stricter threshold values for the cost function increases the number of data points considered outliers or potentially suspicious and decreases the number of data points remaining available for further analysis. Figure 4 exhibits the histogram of the FAPAR values in the five-by-five pixel area around Skukuza for Paths 168 and 169 (a) before screening outliers (indicated in red), and (b) after discarding values associated with an excessive cost function value for those pixels (depicted in blue). The average cost