Abstract

This manuscript focuses on the need for tailoring flood assessment products to decision making within the humanitarian sector. Decision-makers often struggle to extract all of the information contained in scientific products, either because they come from different fields of expertise or because they have different needs that are not captured in the results or the processing of the data. Here we define the key elements of a flood assessment product designed for the humanitarian sector. From a remote sensing perspective, in order to assess flooding, the measurement sampling properties, i.e., spatial resolution and temporal repeat, are key. We have therefore implemented a methodology through the processing and interpretation of the measurements from the Cyclone Global Navigation Satellite System (CYGNSS) mission. CYGNSS measurements are usually parametrized in various possible observables. Those observables are then linked to the surface characteristics, such as, in this case, the presence of inundation in the CYGNSS footprint. Our methodology includes the variability of the pixels in landscapes with infrastructure, rivers, agricultural fields, rural areas, and other elements characteristic of the agricultural-urban interface. We provide an original methodology that uses CYGNSS mission bistatic radar measurements and an artificial intelligence classification algorithm based on statistical properties of the land pixels through a k-means clustering strategy to detect and monitor flooding events, as well as to characterize the land surface prior to and post flooding events. The novel methodology to derive a flooding product is then evaluated towards the needs of the humanitarian sector by a cognizant link (a translator) between technologists or scientists and decision-makers. The inclusion of humanitarian needs into product development following the advice of a cognizant link is novel to the applications developed employing GNSS bistatic radar data.

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