## 4 Results
### Accuracy Assessment of the ESTARFM Predictions  
The cross-validation of the predicted ESTARFM time series comprises three accuracy measures (Figure 8): the coefficient of determination (R\({}^{2}\)), the mean absolute error (MAE), and the root mean square error (RMSE). In general, the northeastern tile (194/52) produced the lowest error values with MAE around 0.02 and average RMSE values of 0.03. Also, the correlations between predicted and observed NDVI values were the highest for this tile (average R\({}^{2}\) = 0.83). Comparing the northern tiles (row 52) with the southern tiles (row 53), it becomes evident that ESTARFM predictions for the northern tiles commonly produce higher accuracies and lower error values (ranging between 0.01 and 0.04 MAE in the north and between 0.01 and 0.07 MAE in the south). Figure 7: Landsat false color composite (NIR-R-G, **row 1**), mean annual NDVI (year 2014) derived from MODIS (**row 2**), ESTARFM (**row 3**) and Landsat (**row 4**) for three focus areas: a fragmented landscape area in the south of Burkina Faso (**left column**), the city of Ouagadougou in Burkina Faso (**middle column**) and the Mole National Park in Ghana (**right column**). Focusing on the temporal development of accuracy over the year 2014, distinct differences can be observed. During the first part of the year between January and April (DOY 15-79 for path 195 and DOY 8-88 for path 194) representing the dry season, the errors are relatively low for all four tiles. Then, the errors rise with increasing vegetation growth (also compare Figure 6a-d) and the highest errors are observed in mid-November, DOY 319 on path 195 (mean MAE of 0.04) and DOY 312 on path 194 (mean MAE of 0.07), respectively. These dates correspond to a period of rapid temporal land surface changes when leaves start browning and NDVI decreases. In addition, the first fires of the dry season occur resulting in widespread burned areas during this time. For the last dates of the year in December, the quality of the ESTARFM predictions is significantly increased again. These spatio-temporal differences of the prediction accuracies can also be identified in the scatterplots of observed _versus_ predicted NDVI values (Figure 9). During the dry season at the beginning of 2014 (DOY 40), the correlation is highest (R\({}^{2}\) = 0.88 for row 52 and 0.80 for row 53) and the range of NDVI values is rather low. While the NDVI range increases during the vegetative season in June (DOY 152), the correlation decreases (R\({}^{2}\) = 0.79 for row 52 and 0.54 for row 53). At the end of 2014 (DOY 344), at the beginning of the next dry season, the correlation increases again while the NDVI range is still high (R\({}^{2}\) = 0.87 for row 52 and 0.77 for row 53). The north\(-\)south difference in accuracy is also visible in these scatterplots, with row 52 generally showing lower errors and less scattering of values in the plot than row 53. To estimate the prediction quality of the main rainy season where only severely cloud-affected Landsat data is available, eight additional Landsat scenes of path 195 with cloud cover of up to 80% were used for validation (Table 2). The errors for this period vary between 0.03 and 0.10 for MAE and between 0.05 and 0.12 for RMSE with R\({}^{2}\) values between 0.3 and 0.76. Error values are found to increase towards the peak of rainy season (around DOY 280, beginning of October) and decrease afterwards. In addition, a subset area in the northeast of the study region was selected to show an example of the spatial patterns of absolute differences between observed and predicted NDVI (Figure 10). This area represents the different landscape types of the study region including grasslands, woodlands and heterogeneous parklands, _i.e._, mixtures of cropland and semi-natural vegetation. The general differences for the late dry season (DOY 40) are low with some minor discrepancies for the waterbody in the northern part of the subset.