## 2 Materials and Methods
### Application and Testing of Virtual Constellation in Apalachicola Bay
#### 2.4.2 Input Preparation  
Images in the L8 time series were clipped to study area boundary using ArcGIS, excluding the major bays, lakes, and river channels from the analysis. In developing and training the virtual constellation, data availability was determined at the pixel scale. At 30 m resolution, there are approximately 1.17 million pixels in the image. The L8 cloud mask [73] distributed with each image was used to identify cloud pixels and calculate cloud cover percentage. An additional filter for negative NDVI values was implemented since NDVI values approaching \(-1\) generally correspond to open water or other non-vegetated areas [74]. Figure 6 shows the temporal distribution of usable data in the study area over the analysis period. L8 NDVI imagery is released as a 16-day composite, therefore some months will contain two images. Due to the fact that the month, encoded as an integer from 1 to 12, is a predictor variable in the RF, in the event that two separate images were available for a particular month we selected the one with a lower CC.  
S2A and ASTER NDVI (floating point values from 0.0 to 1.0), location (floating point Northing and Easting coordinates of the pixels in meters referenced to UTM Zone 16N, WGS84), and the image acquisition month (encoded as an integer from 1 to 12) were the predictor variables for the virtual constellation model. Including the location as an input feature enables the RF to estimate a value correlated with that of nearby pixels as well as a historically plausible value [54].  
The pre-processed S2A and ASTER images were clipped to study area and resampled using a nearest neighbor assignment method to 30 m ground resolution using ArcGIS to maintain the consistency of each pixel location throughout the time series. The SEN2 COR cloud mask [75] was used for S2A cloud identification. The negative NDVI filter mentioned previously was also used for S2A to mask out pixels correspo  
Figure 6: Percentage of scene visibility in Landsat 8 during random forest training time period.  
the virtual constellation model organization, S2A data are selected as secondary. The primary reason for prioritizing S2A over ASTER was that S2A was previously shown to provide adequate continuity for current LANDSAT missions [76]. Secondly, the ASTER NIR bandwidth is wider than the other two sensors and the difference between it and L8 is larger than the difference between S2A and L8 (Table 1). The L8-S2A fused imagery was then ready for tertiary fusion with ASTER if necessary. The virtual constellation model was robust in the sense that in cases of scene unavailability or obscurity for any single sensors, the others could be fused into viable NDVI imagery. In cases where only one sensor was available in addition to L8 (target sensor), then the peer sensors NDVI values were adjusted to L8 compatible NDVI and the L8 values remained unchanged. On the other hand, if the baseline sensor L8 was not available, then S2A was fused with ASTER where both transformed to L8 compatible values using the RF and fused into a virtual L8 image. In the event that the only available imagery is a cloudy image from one sensor, then a technique such as Optical Cloud Pixel Recovery (OCPR) would be necessary to repair the image [54].