# Capability Analysis of Earth Observation Data for Integrated Emergency Management
## 4 R2-D2 Case Study Results
### Previous Use of Satellite Imagery for Managing Selected Emergency Management Situations  
Snow and extreme cold: Snow data products are generated from various multi-spectral instruments and satellite missions, including MODIS, Visible Infrared Imaging Radiometer Suite (VIIRS), and Sentinel-1. The characteristic reflectance of snow cover is in the 0.3-1.0 \(\upmu\)m visible and 1.6 \(\upmu\)m infrared wavelengths [38]. Snow cover pixels are typically derived from the Normalised Difference Snow Index (NDSI), where snow is detected if the pixel value exceeds a threshold of 0.4 [39]. For example, [PERSON] et al. [40] combined optical data from MODIS fractional snow cover and radar data from Sentinel-1 wet snow masks to produce monthly dry and wet Snow Cover Extent (SCE) maps over a 55,000 km\({}^{2}\) area in the Indian Himalayas. [PERSON] et al. [41] used Sentinel-1 data to measure snow depth in Northern Hemisphere mountains. [PERSON] et al. [42] used an empirical approach to identify the start of run-off, end of snow cover, and SCE for dual-polarised Sentinel-1 SAR imagery of Greenland. Space-borne passive microwave radiometers have been used to measure snow depth, snow water equivalent and snow cover area, but are limited due to the coarse spatial resolutions [43]. However, active SAR satellites have much finer spatial resolutions than passive radiometers, with, e.g., Sentinel-1 providing data at a 20 m resolution. While researchers have used SAR to measure SCE, depth and melt rate [44], these studies have primarily focused on high mountain regions where snow cover is prevalent for several months, and there is an opportunity to acquire multiple cloud-free snow cover images. Dry and wet snow cover have different effects on radar surface and volume backscatter as a function of snow water equivalent at varying snow depths [45]. The change in backscatter intensity can distinguish different stages in the snowpack cycle, including the moistening and melting of dry snow into liquid snow run-off and snow-free ground [42]. The change in backscatter intensity has been used in multiple studies to measure different characteristics of SCE and wet snow extent. There has been little research into using SAR data to measure sudden snow events in low-lying rural and urbanised areas, which could help extend snow cover products from optical satellites where extensive cloud cover is often an issue. IWS: [PERSON] et al. [46] and [PERSON] et al. [47] used thermal and multi-spectral imagery for the detection of waste dumping sites; however, as the methods did not provide information on vertical height, they could only be used as detection rather than quantification methods. Sentinel-1 supports multiple SAR polarisation modes (VV, VH, HH, HV), offering more detailed information about surface properties, which may potentially provide additional insight into the extent and severity of a fire incident. SAR processing can also detect changes in the Earth's surface characteristics, such as changes in the height of waste sites, both illegal and legal. Comparing pre-fire and post-fire SAR images could help identify burn scars, as the backscatter coefficient decreases significantly in the burned area. For example, [PERSON] and [PERSON] [48] used SAR data from Sentinel-1 after the 2019-2020 Australian bushfires to identify burned areas. SAR data can complement the analysis of areas where optical imagery may be obscured by smoke and/or cloud cover. Applying machine learning algorithms to satellite imagery presents another possible method for detecting IWSs. For example, [PERSON] et al. [49] used semantic segmentation models on a new custom dataset of high-resolution multi-spectral satellite images from WorldView and GeoEye with accurate semantic segmentation masks to detect landfill sites. They found that using pre-trained models based on the ImageNet and SpaceNet datasetswith transfer learning meant a small dataset (240 images) could achieve an accuracy of 0.83 and a mean intersection over union (mIoU) of 0.401.