# Applications of Satellite Radar Imagery for Hazard Monitoring: Insights from Australia
## 3 Hydrological Hazards
### Flooding
#### 3.1.1 Example Case Study: Wildman Coastal Floodplains (Northern Territory)  
The wetlands of the Wildman coastal floodplains, Northern Territory (for location, see Figure 2A), are an example where SAR provides a method of mapping inundation amongst and beneath vegetation. While a simple amplitude thresholding might be used as the first approach or for simple cases (Section 2), it is important to keep in mind that complex and/or mixed land covers (water, aquatic and semi-aquatic vegetation, and partly inundated forests) might challenge the interpretation of the SAR backscattering signal. The amplitude of the returned signal is impacted by plant density and physical structure, which modifies the partitioning between direct, volumetric, and double-bounce scattering. Open-water and low-aquatic vegetation is typically well-retrieved by such an amplitude thresholding approach, whereas inundated forests induce high values of SAR intensity due to a particular water-trunk double-bounce scattering (e.g., used in [23; 24]). Therefore, a more complex scattering mechanism retrieval is required (e.g., see [27] for information on SAR-scattering mechanisms over wetlands). In this example, flooding patterns and surface-water bodies are mapped using time-series analysis of 49 Sentinel-1 Interferometric Wide swath (IW) SAR backscatter intensity images (co-polarised vertical transmit, vertical receive (VV)) to extract the minimum and maximum, temporal gradients, and Coefficient of Variation (COV; the standard deviation divided by the mean) (Figure 3). Seasonally flooded areas are associated with high COV (red in Figure 3A), whereas flat and rapidly flooded areas are identified as having a high temporal gradient (maximum absolute variation between consecutive acquisition dates; blue in Figure 3A), and vegetated, flood-free areas have a high-temporal minimum with low-temporal variability (green in Figure 3A). Advancing and receding seasonal flooding can be mapped between consecutive image acquisitions (12-day intervals for Sentinel-1) by identifying pixels with intensitybelow a threshold (\(-15\) dB selected based upon histogram analysis). The floodplain dynamics (red on Figure 3) do not permit ligneous vegetation to grow, and hence no major influence of double-bounce scattering, increasing SAR intensity during flood events, is expected. An example for a precipitation event in February 2018 is shown in Figure 3B,C, respectively, with a time-series of the SAR-derived percentage of flooded areas throughout the observation period (from 20160805 to 20180409) shown in Figure 3D. This example illustrates SAR's potential for mapping the extent of floodwaters in vegetated areas, which is significant for coastal floodplains in Australia, where inundation impacts infrastructure development and the long-term evolution of sensitive ecosystems that are particularly susceptible to the impacts of sea-level rise (e.g., [24]). It also highlights the challenges of applying simple intensity thresholding in environments where multiple concomitant factors influence the strength of the returned signal. For example, terrestrial (or semi-aquatic) plant growth during dry seasons decreases the VV intensity returns over dry floodplains (Figure 3D--areas below \(-15\)dB increasing in proportion from July to October), whereas aquatic plant growth during flooding season (Figure 3D--January--April) might increase the VV intensity returns over flooded areas through double-bounce scattering. These factors should be considered when developing operational SAR map products for similar environments, as simple intensity thresholding may result in inaccuracies. To some extent, the use of multiple polarizations reinforces such analysis by protecting from potential false-positives, e.g., HV and VH bands being less sensitive to water surface waves and perturbations than HH and VV bands [28]. Figure 3: Assessment of the flooding of the Wildman coastal plain, Northern Territory, derived from Sentinel-1 intensity imagery between 20170108 and 20181222.