The paper "Flood Detection with SAR: A Review of Techniques and Datasets" by Donato Amitrano, Gerardo Di Martino, Alessio Di Simone, and Pasquale Imperatore provides an extensive review of techniques and datasets used for flood detection using synthetic aperture radar (SAR). The authors highlight the increasing importance of SAR in flood monitoring due to its ability to provide imagery regardless of weather conditions, making it a valuable tool for humanitarian, social, and economic reasons. The paper discusses the advantages of SAR over multi-spectral sensors and the recent advancements in processing paradigms such as fuzzy logic, machine learning, and data fusion. Key aspects covered include the phenomenological aspects of floods in SAR images, such as backscattering coefficient and interferometric coherence, which are crucial for understanding the formation of SAR images during flooding. The paper also reviews various processing techniques for flood detection, including thresholding, fuzzy classifiers, machine learning, and multi-sensor data fusion. Each technique is evaluated based on its strengths and limitations, and the authors provide a critical analysis of the current state of the art. Additionally, the paper addresses the importance of validation strategies and the role of open datasets in supporting thorough and objective validation of flood mapping techniques. It highlights the challenges in detecting flooded areas in vegetated and urban environments, where complex scattering mechanisms can complicate the extraction of water regions. The authors also discuss the potential of interferometric coherence (IC) as an indicator of alterations in electromagnetic scattering properties, which can be used to detect flooding between acquisitions. The paper concludes with a discussion on future perspectives and the need for further research to improve the accuracy and reliability of flood mapping techniques. It emphasizes the importance of multi-dimensional SAR data and the integration of different scattering mechanisms to enhance the detection of flooded areas in challenging scenarios.The paper "Flood Detection with SAR: A Review of Techniques and Datasets" by Donato Amitrano, Gerardo Di Martino, Alessio Di Simone, and Pasquale Imperatore provides an extensive review of techniques and datasets used for flood detection using synthetic aperture radar (SAR). The authors highlight the increasing importance of SAR in flood monitoring due to its ability to provide imagery regardless of weather conditions, making it a valuable tool for humanitarian, social, and economic reasons. The paper discusses the advantages of SAR over multi-spectral sensors and the recent advancements in processing paradigms such as fuzzy logic, machine learning, and data fusion. Key aspects covered include the phenomenological aspects of floods in SAR images, such as backscattering coefficient and interferometric coherence, which are crucial for understanding the formation of SAR images during flooding. The paper also reviews various processing techniques for flood detection, including thresholding, fuzzy classifiers, machine learning, and multi-sensor data fusion. Each technique is evaluated based on its strengths and limitations, and the authors provide a critical analysis of the current state of the art. Additionally, the paper addresses the importance of validation strategies and the role of open datasets in supporting thorough and objective validation of flood mapping techniques. It highlights the challenges in detecting flooded areas in vegetated and urban environments, where complex scattering mechanisms can complicate the extraction of water regions. The authors also discuss the potential of interferometric coherence (IC) as an indicator of alterations in electromagnetic scattering properties, which can be used to detect flooding between acquisitions. The paper concludes with a discussion on future perspectives and the need for further research to improve the accuracy and reliability of flood mapping techniques. It emphasizes the importance of multi-dimensional SAR data and the integration of different scattering mechanisms to enhance the detection of flooded areas in challenging scenarios.