## 4 Discussion
This is a potential topic for future work in enhancing virtual constellations. The results also showed that using LR was insufficient to predict L8 NDVI for either complementary sensor. In terms of the wall clock time required to train the models, LR is significantly faster than RF. However, RF showed better prediction accuracy than LR in this application and its complexity is justified. The virtual constellation technique presented here was limited by the availability of historical data in which all three sensors were active. In particular, the peer sensor data (S2A and ASTER) are currently not available over the same long time period as the baseline Landsat 8 NDVI. Similar to many machine learning approaches, its performance is tightly coupled to the quality and quantity of its training data. It is not known, however, whether or not RF is the best machine learning model for this task. Based on the literature, it is a justifiable choice but perhaps as more data are collected, an alternative model such as convolutional neural networks may be a better choice. The authors also strongly recommend that the area of interest lie well inside the analysis boundary used to clip the satellite imagery to avoid interpolation effects near the edges. This is especially relevant to the technique presented here as the location was second only to peer sensor values in its influence on the final NDVI prediction. The results presented herein also show that while the virtual constellation improves spatial coverage of obscured L8 NDVI imagery, there are still cases where it cannot reconstruct the image due to lack of complementary sensor data from S2A or ASTER over all or parts of the target image. One specific instance where this occurred was in October 2018. Figure 5 shows that no ASTER or S2A imagery was available for this month, and Figure 6 shows that only 45.75% of the L8 image was visible. Hurricane Michael made landfall on 10 October 2018 near the study region which likely caused the vast cloud cover rendering these images unusable. In these cases, a tool such as OCPR [54] that relies on deeper environmental data and no other sensors can be used. However, this also illustrates the potential for fusing images that occur in the same calendar month, but were acquired before and after a major event such as a hurricane. Future researchers should exercise caution and perhaps manually review imagery in months where catastrophic events affecting NDVI occurred. There is also a possible issue when NDVI values from a complementary sensor are faulty and there are no accurate values in the other complementary or target sensors to prevent the faulty data from propagating into the output image. This issue is mitigated by quality control procedures implemented prior to the public release of the imagery. This is also mitigated to some degree by including the encoded location and month in the predictor feature set as NDVI values that are uncharacteristic for a specific location and time of year would likely be adjusted to more plausible values by the random forest. Still, the virtual constellation model is a positive step towards producing spatially and temporally seamless NDVI for long-term CW studies. One crucial application where virtual constellation could be of use is the projection of CW coverage, zonation, and above ground biomass density under sea level rise scenarios [82; 83; 84; 85]. A critical component of \begin{table}
\begin{tabular}{c c c c} \hline \hline
**ASTER to L8 NDVI** & \multicolumn{3}{c}{**S2A NDVI to L8**} \\ \hline
**Features** & **Importance** & **Features** & **Importance** \\ \hline ASTER NDVI & 0.586 & S2A NDVI & 0.479 \\ Northing (m) & 0.222 & Northing (m) & 0.275 \\ Easting (m) & 0.185 & Easting (m) & 0.227 \\ Month & 0.014 & Month & 0.024 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Feature importance for the trained random forests. these studies is the ability to capture the present and past states of CW for validation purposes.