Integrating C- and L-Band SAR Imagery for Detailed Flood Monitoring of Remote Vegetated Areas

Abstract

Flood detection and monitoring is increasingly important, especially on remote areas such as African tropical river basins, where ground investigations are difficult. We present an experiment aimed at integrating multi-temporal and multi-source data from the Sentinel-1 and ALOS 2 synthetic aperture radar (SAR) sensors, operating in C band, VV polarization, and L band, HH and HV polarizations, respectively. Information from the globally available CORINE land cover dataset, derived over Africa from the Proba V satellite, and available publicly at the resolution of 100 m, is also exploited. Integrated multi-frequency, multi-temporal, and multi-polarizations analysis allows highlighting different drying dynamics for floodwater over various land cover classes, such as herbaceous vegetation, wetlands, and forests. They also enable detection of different scattering mechanisms, such as double bounce interaction of vegetation stems and trunks with underlying floodwater, giving precious information about the distribution of flooded areas among the different ground cover types present on the site. The approach is validated through visual analysis from Google EarthTM imagery. This kind of integrated analysis, exploiting multi-source remote sensing to partially make up for the unavailability of reliable ground truth, is expected to assume increasing importance as constellations of satellites, observing the Earth in different electromagnetic radiation bands, will be available.

Flood Mapping in Vegetated Areas Using an Unsupervised Clustering Approach on Sentinel-1 and -2 Imagery

“…In general, the SARincF and SARwC FSs complemented by three or ten optical bands perform well and the optimal k lies around 10. This k value is in line with the findings of Refice et al [40], who initially opted for a higher number of clusters but obtained 12 distinct clusters after merging.…”

“…They furthermore observed an increasing backscatter difference with decreasing incidence angles, in accordance with previous research, except for very sharp incidence angles (23.5 • ) at which the detectability dropped sharply [37][38][39]. Long et al [14] successfully mapped flooded marshland using a threshold of 7.7 dB on a C-HH difference image, while Tsyganskaya et al [19] observed a backscatter increase of about 2 dB for an example FV segment with C-VV and Refice et al [40] reported a backscatter increase of almost 5 dB in C-VV for a cluster comprising mainly flooded herbaceous vegetation. An overview of relevant findings for C-as well as X-and L-band is given by Martinis and Rieke [41].…”

“…Thus, the FV class is characterized by an increase in both VV inc and R inc . The thresholds are both set to a value of 3, based on a calibration as well as reported values in literature [19,36,40]. The FV class is defined as…”

“…Depending on the case, the normalized VV intensity or VV/VH ratio were found to be the most important features [19,48]. In a recent proof-of-concept, Refice et al [40] have successfully combined multi-temporal C-and L-band imagery to monitor flooding in a remote vegetated area, by using a clustering-based approach. Olthof and Tolszczuk-Leclerc [49] developed a supervised machine learning approach based on dual-pol RADARSAT-2 imagery, using automatically extracted training data based on a Landsat inundation frequency product to map open water and using region growing to include flooded vegetation.…”

A Tool for Pre-Operational Daily Mapping of Floods and Permanent Water Using Sentinel-1 Data

“…However, several unknowns (e.g., structure and geometry for vegetation, or height and orientation of buildings for urban areas) influence the radar response from these targets [8]. Consequently, although new insights were provided on detecting inundations in agricultural areas [9][10][11][12][13] and urban settlements [8,[14][15][16][17], this task is still very challenging. Not only missed detections (i.e., omission errors), but also commission errors may occur when mapping surface water.…”