# Capability Analysis of Earth Observation Data for Integrated Emergency Management
## 4 R2-D2 Case Study Results
### Pilot Studies Investigating Satellite Imagery in Support of Emergency Management  
#### 4.3.1 Snow and Extreme Cold Incidents  
This case study utilised archival Sentinel-1 imagery to identify snow cover arising from Storm Darcy, which brought strong winds, cold weather and persistent snow showers to parts of the UK between 7 February 2021 and 13 February 2021 [70]. Fifteen Sentinel-1 images were utilised, captured between 4 December 2020 and 28 February 2021 and evenly spaced six days apart. To create a set of seasonal snow-free reference SAR images, we determined snow-free image dates using NDSI and cloud cover percentages in the MODIS snow data product, where the average NDSI was <10% and cloud-free pixels were >20%. Through this filtering,  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Dataset** & **Case Study** & **Description and Justification** \\ \hline Satellite imagery & & \\ \hline MODIS NDVI snow cover [59] & Snow/extreme cold & Interferometric Wide Swath (IW) mode in ascending orbit and relative orbit pass 30 from Google Earth Engine (GEE) using the Earth Engine Python API Version 0.1.341.  
and accounting for missing MODIS reference data on five dates, the SAR reference stack contained three images (22 December 2020, 27 January 2021 and 26 February 2021), which were reduced to a single image by computing the average pixel values in each of the VV and VH channels. In order to validate the wet snow pixel detection against the MODIS snow product, the Sentinel-1 data were resampled to match the MODIS pixel resolution of 500 m \(\times\) 500 m by mean averaging across all pixels within the sample window. This process also applied speckle reduction to the Sentinel-1 images, to overcome problems that affect SAR backscattering snow cover detection algorithms [71], and improve performance in classification tasks [72]. The backscatter coefficients were converted to decibels (dB) using the \(10 log_{10}\big{(}\sigma^{0}\big{)}\) transformation and, similar to work by [PERSON] et al. [40] and [PERSON] et al. [44], we set a threshold of \(-2\) dB to determine wet snow pixels with respect to \(\sigma^{sv}_{snow}-\sigma^{sv}_{ref}\)_and_\(\sigma^{vh}_{snow}-\sigma^{vh}_{ref}\)_, where \(\sigma^{vv/vh}_{snow}\)_and_\(\sigma^{vv/vh}_{ref}\) are the backscatter coefficients for the snow and reference images, respectively. Finally, cloud masks from MODIS and water masks from Copernicus Global Land Cover [68] were applied to the scene to consider only cloud-free land pixels and compare them against the NDSI values in the MODIS imagery. Figure 4 visualises the individual steps of the methodology used to transform the snow/extreme cold datasets described in Table 1.  
The SAR change detection identified 8878 snow pixels in VV polarisation and 16,330 in VH, representing 28.8% and 52.9% of the total area, respectively. Figure 5 shows the extent of these pixels across the study area. VV and VH identified significant changes in the west and northwest of the region, located across the North Pennines and Northumberland National Park, particularly in the high-altitude areas surrounding Kielder Water and Kielder Forest. The analysis detected fewer change pixels in the metropolitan areas surrounding Newcastle upon Tyne. Comparing the number of changed pixels in MODIS NDSI indicates the proportion of total snow pixels identified using the SAR snow detection analysis. Unfortunately, on the snow image date of 8 February 2021, cloud cover accounted for 67% of the total MODIS image, meaning less than a third of the data could be used to validate the snow detection. Figure 6 displays the VV and VH snow detection classification and accompanying cloud mask. For MODIS NDSI, all values greater than 0 were classified as snow pixels, which essentially included all areas in the unmasked data.