## 5 Discussion  
### Uncertainties in the Prediction with ESTARFM  
Several modifications have been introduced in the presented ESTARFM framework resolving shortcomings and challenges that have been mentioned as drawbacks in the usage of the existing ESTARFM algorithm [3; 10; 31]. However, certain points affecting the accuracy of the ESTARFM fusion remain. First of all, the availability of Landsat input data has a great influence on the quality of the fusion. Minimizing the temporal distance of the shoulder pairs to the target scene is crucial for a successful prediction [3; 32] and it became evident that using at least one shoulder pair from the same phenological stage as the prediction date has even higher positive influence (Figure 12). A further factor influencing the fusion quality is the land cover type of a pixel (Figure 10). In our study region, land cover types with a lower NDVI range, such as mixed rain-fed agricultural areas and grasslands, show low absolute difference between observed and predicted NDVI over the year. In contrast, the predicted NDVI values of densely vegetated areas such as closed woodlands differ more starkly from the actual NDVI values. In our study region, these areas exhibit stronger phenological dynamics which are usually more difficult to predict with ESTARFM [9; 33]. These dynamics are also a potential reason for the north\(-\)south difference of prediction quality in the study area since the southern regions are generally more densely vegetated than the northern parts. In addition, some of these areas with a high NDVI range are characterized by rapid changes through flooding (especially gallery forests) or burning (semi-arid savannas) which negatively affects the quality of prediction. However, not only the temporal variance plays a role for the reduced prediction quality during the main rainy season, but also the cloud coverage of the MODIS dataset. Although MODIS cloud gaps have been interpolated here, these areas are less accurate in the prediction of synthetic Landsat scenes. This issue should be more emphasized for land cover types with a distinct phenology decreasing the accuracy of interpolation. For areas with high temporal variance, [PERSON] _et al._ [9] and  
Figure 12: Relationship between the mean absolute error (MAE) and the temporal distance between the shoulder scene combinations (SSC). The MAE was calculated by using all possible SSC for predicting the Landsat scene (195/52) acquired on DOY 143, 2014, and the MAE was derived between predicted scenes and observed scene. The different symbols represent different acquisition dates of the right shoulder scene, e.g., the four diamond symbols represent four predictions of DOY 143 with the same right shoulder (DOY 319) but different left shoulders. [PERSON] _et al._[10] achieved better results with STARFM than with ESTARFM. However, these comparison studies showed at the same time that the performance of ESTARFM is superior to STARFM for study regions with a high spatial heterogeneity (as being the case in this study) and the overall accuracies of ESTARFM tend to be higher as well [9; 10]. A comparison of ESTARFM and ESTARFM framework with the newly developed Flexible Spatiotemporal Data Fusion (FSDAF) method by [PERSON] _et al._[34] could be the focus of future research in order to identify potential synergies. In addition, especially in areas with a complex canopy structure, the BRDF correction of input data can have an impact on the fusion quality. [PERSON] _et al._[33; 35] tested the usability of the BRDF-corrected MODIS product MCD43A4, which was also applied in our study, together with Landsat-5 TM (Thematic Mapper) as an input to STARFM. They found generally low NIR correlations between Landsat and STARFM predictions and concluded that this could be due to Landsat-5 TM data not being corrected for BRDF effects in contrast to MCD43A4. They further reasoned that the discrepancy between the input datasets would be highest in areas dominated by tall, multi-layered vegetation causing high scattering of NIR radiation [33].