# Improving Flood Detection and Monitoring through Remote Sensing
###### Abstract
A potentially innovative data source for flood monitoring is investigated in [9]. GNSS reflectometry is a technique that uses microwave signals emitted by global positioning system constellations and collected by suitable receivers to gain information about (bistatic) reflectivity of the Earth's surface. NASA's CYGNSS mission is a constellation of micro-satellites carrying receivers to exploit GNSS signals, which has proven to be useful in many land and ocean studies [10]. In this case, CYGNSS-collected reflectivity signals were used to map inundated areas during an exceptional precipitation event that occurred in January 2020 over a large basin in Southeastern Iran. Despite its relatively low resolution (\(25\times 25\) km\({}^{2}\)), the high temporal acquisition frequency (average of about 7 h, reaching about 2 h as a maximum) makes it a very promising tool to improve the timeliness of flood survey maps over large areas. In [11], an in-depth discussion is presented about the application of a methodology [12] to detect flood water both on open areas and under vegetation (indicated, respectively, as \"temporary open water\" and \"temporary flooded vegetation\" in the article) from time series of SAR images. Sentinel-1 data are analyzed over three test sites in Greece and Turkey that were affected by floods in March and April 2015 and June 2017, respectively. Identifying flooded vegetation, typically through the detection of the \"double bounce\" scattering behavior that causes significant backscatter increase with respect to non-flooded conditions, is considered one of the most difficult tasks in flood monitoring by SAR data. In the article, various combinations of the two available Sentinel-1 polarization channels, namely VV and VH, are considered as possible features to detect different terrain classes. The relative importance of single polarization channels and the difference, sum, and ratio of the two were evaluated by using statistical means, using information extracted from optical images as reference data, to identify the most relevant one(s) for the detection of each class. Results point to single VV polarization as the most efficient for open water flood identification, while the sum of VV + VH polarizations is recommended for detecting flooded vegetation, although small variations in performance for the different features appear from one test site to another, which seems to suggest that choosing one feature over the others may actually be a site-dependent task. The task of discriminating terrain cover during floods is also considered in [13], where both C-band (Sentinel-1) and L-band (ALOS 2) data are exploited in order to extract information about a prolonged, large inundation, which affected a part of the Zambesi river basin in Mozambique, Africa, from December 2014 to April 2015. No useful optical data are available for this event, as is normally the case over equatorial sites that are mostly clouded for long periods of time. Nevertheless, the analysis of multi-temporal series, aided by the use of the CORINE land cover database, available at a resolution of 100 m over Africa and the synergy between the different wave penetration and backscattering characteristics of the two sensors allowed the derivation of an informative set of multitemporal maps of flood evolution, as well as an integrated, multi-sensor map discriminating various types of ground features and situations, such as flooded crops, grassland, and forest in addition to open water areas.