# Semi-Automatic Fractional Snow Cover Monitoring from Near-Surface Remote Sensing in Grassland
## 3 Area of Study
### Validation of Optimal Band Selection Scenario for FSC Estimation  
Previous work used the Blue band to calculate FSC. For example, [46] applied a threshold in the first local minimum above 127 in the Blue band histogram to map snow cover. In addition, they selected the Blue band after processing the three bands separately and got similar values. We also explored the utility of the MoNIR band for FSC calculation.  
To select images that contained some snow cover, we calculated the average brightness of each band for all the images we downloaded. The oblique images from Oakville usually have a panel, a pole, information (name, date, and hour), and the sky (Figure 5), all of which we excluded from the band brightness calculations. Calculating average brightness for each band for all images allowed us to understand which PhenoCam bands were most affected by the presence of snow cover.  
Figure 4: (**a**) Research site at the Oakville Prairie Biology Field Station, including the PhenoCam; (**b**) example RGB images; and (**c**) example MoNIR images through the year acquired by the PhenoCam.  
Guided by our band brightness calculations, 38 images were selected from the entire dataset, representing 11 categories of FSC (0%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100%) for four periods (the winter seasons of 2015-2016, 2016-2017, 2017-2018, and 2018-2019). Three images with sastrugi present were also selected. We tested the images for calculating the FSC in four different scenarios:  
Scenario 1: Blue band threshold  
In the first scenario, pixels with Digital Numbers (DNs) greater than or equal to 127 in the Blue band were classified as \"snow\", and pixels with lower Blue DNs were classified as \"no-Snow\" [46].  
Scenario 2: Monochromatic RGB+NIR (MoNIR) threshold  
The second scenario selected as snow all pixels with DNs greater than or equal to 127 in the MoNIR band. Depending on the land cover and the sunlight, the reflectance value may show differences for each band histogram (Figure 6).  
Figure 5: Example PhenoCam image showing the area used to calculate FSC in each band.  
Figure 6: (**a**) Senesced grassland, (**b**) Green grassland and their corresponding Blue and MoNIR band histograms.  
Because shadowing on snow cover has been shown to confound efforts to calculate FSC from PhenoCCam data, we developed two additional scenarios. The third and fourth scenarios, then, exploited the relationship between MoNIR and the Blue band calculated by:  
\[\text{Shadow }=\frac{\text{MoNIR }-\text{ B}}{\text{MoNIR}+\text{B}}\times 100 \tag{1}\]  
The resulting DNs considered shadow on snow cover fell between \(-80\) and \(20\). These were selected and used in the next two scenarios.  
Scenario 3: Blue (B) band threshold + Shadow  
The selected values from Shadow were added to the Blue band selected pixels from the first scenario.  
Scenario 4: MoNIR + Shadow  
The fourth scenario added the pixels from shadow to the pixels from scenario two.  
To validate which scenario worked best for each FSC, we used the Accuracy Assessment tool in ArcGIS 10.6, which generates points for each class. For this study, we generated 50 points per image, 25 for each class (snow or no snow). Then, we visually compared the selected points to the original image to generate a confusion matrix between both classes.