# Inundation Extent Mapping by Synthetic Aperture Radar: A Review
## 3 Relative Strengths and Limitations of Existing Techniques
### Selected Studies of Combined Approaches
[59] developed a four-step processor, as shown in Figure 3, the Radar Produced Inundation Diary (RAPID), which makes use not only of time series of SAR data, but also the abundance of available high-resolution ancillary satellite products, including land cover classification (LCC) maps from Landsat [84; 85], water occurrence from 30-year Landsat images [58], global river width from STRM [86] and Landsat images [87], and fine-resolution hydrography [88; 89]. The four steps are as follows:  
First, SAR images are classified into water and non-water masks (WMs), based on statistics from polarimetric radar. This binary classification step was inspired by the auto-optimization method by the m2b method [49] and extend to utilizing the dual-polarized SAR data. It removes the assumption of a bimodal distribution made in of m2b by initializing the water PDF using the water occurrence map. Second, morphological processing runs over the mask, consisting of two operators, water source tracing (WST) and improved CD (ICD), to form inundated water bodies while to remove false positives caused by water-like surfaces. WST and ICD are also designed to detect fluvial and pluvial inundation, respectively. This morphological procedure is different from the post-processing methods in [PERSON] et al. [51], [PERSON] et al. [77], and [PERSON] et al. [90] in three aspects: (1) in RAPID, a pixel needs either to be accepted by WST or ICD while, in the other studies, the change detection is used to measure the pixels accepted by the RGA, which cannot capture pluvial inundated areas; (2) both WST and ICD utilize object-level information instead of pixel-level information to evaluate whether a detected water body is a false positive; and (3) to prevent the disturbance of calibration error among different SAR images, the change is detected from a binary mask instead of backscattering. Third, a multi-threshold compensation step is applied to reduce the under-detection within water areas, and, fourth, a machine learning-based refinement is used to remove false negatives created by strong scatterers and to reduce further the noise level in the result. These final two steps in RAPID reduce the error caused by speckle and strong scatterers by utilizing the PDF and physically integrating multi-source remote sensing products instead of brutally applying a filtering technique. Thus, RAPID reduce the noise level without sacrificing resolution to mapping quality. Among available satellites with SAR sensors, Sentinel-1 is the most popular because it is free to use and has relatively short revisiting intervals (six days in Europe and the Hawaiian islands and twelve days everywhere else in the world), three-day in average revisiting tracks (the revisiting intervals of the satellites (S1A and S1B) without the promise of producing the data), and a multi-year data archive dating back to 2014. During an event, the satellites may add extra revisits. As illustrated in Figure 4, for instance, for Hurricane Harvey six revisits were acquired from 27 August to 10 September 2017 in Seabrook area, and for Hurricane Irma eleven revisits from 12 September to 2 November 2017. Figure 3: The workflow of RAPID. From the left to the right panels are step 1 (binary classification), 2 (morphological processing consisting of water source tracing (WST) and improved change detection (ICD), 3 (multi-threshold compensation) and 4 (machine-learning-based correction), respectively.