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Bangladesh is one of the most flood-affected countries in the world. In the last few decades, flood frequency, intensity, duration, and devastation have increased in Bangladesh. Identifying flood-damaged areas is highly essential for an effective flood response. This study aimed at developing an operational methodology for rapid flood inundation and potential flood damaged area mapping to support a quick and effective event response. Sentinel-1 images from March, April, June, and August 2017 were used to generate inundation extents of the corresponding months. The 2017 pre-flood land cover maps were prepared using Landsat-8 images to identify major land cover on the ground before flooding. The overall accuracy of flood inundation mapping was 96.44% and the accuracy of the land cover map was 87.51%. The total flood inundated area corresponded to 2.01%, 4.53%, and 7.01% for the months April, June, and August 2017, respectively. Based on the Landsat-8 derived land cover information, the study determined that cropland damaged by floods was 1.51% in April, 3.46% in June, 5.30% in August, located mostly in the Sylhet and Rangpur divisions. Finally, flood inundation maps were distributed to the broader user community to aid in hazard response. The data and methodology of the study can be replicated for every year to map flooding in Bangladesh.

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Volcanoes are hazardous to local and global populations, but only a fraction are continuously monitored by ground-based sensors. For example, in Latin America, more than 60% of Holocene volcanoes are unmonitored, meaning long-term multiparameter data sets of volcanic activity are rare and sparse. We use satellite observations of degassing, thermal anomalies, and surface deformation spanning 17 years at 47 of the most active volcanoes in Latin America and compare these data sets to ground-based observations archived by the Global Volcanism Program. This first comparison of multisatellite time series on a regional scale provides information regarding volcanic behavior during, noneruptive, pre-eruptive, syneruptive, and posteruptive periods. For example, at Copahue volcano, deviations from background activity in all three types of satellite measurements were manifested months to years in advance of renewed eruptive activity in 2012. By quantifying the amount of degassing, thermal output, and deformation measured at each of these volcanoes, we test the classification of these volcanoes as open or closed volcanic systems. We find that~28% of the volcanoes do not fall into either classification, and the rest show elements of both, demonstrating a dynamic range of behavior that can change over time. Finally, we recommend how volcano monitoring could be improved through better coordination of available satellite-based capabilities and new instruments.

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Radio frequency interference (RFI) is a known issuein low-frequency radar remote sensing. In synthetic aperture radar (SAR) image processing, RFI can cause severe degradation of image quality, distortion of polarimetric signatures, and an increase of the SAR phase noise level. To address this issue, a processing system was developed that is capable of reliably detecting, characterizing, and mitigating RFI signatures in SAR observations. In addition to being the basis for image correction, the robust RFI-detection algorithms developed in this paper are used to retrieve a wealth of RFI-related information that allows for mapping, characterizing, and classifying RFI signatures across large spatial scales. The extracted RFI information is expected to be valuable input for SAR-system design, sensor operations, and the development of effective RFI-mitigation strategies. The concepts of RFI detection, analysis, and mapping are outlined. Large-scale RFI mapping results are shown. In case studies, the benefit of detailed RFI information for customized RFI filtering and sensor operations is exemplified.Index Terms-Calibration, error correction, L-band, radar remote sensing, radio frequency interference (RFI), synthetic aperture radar (SAR). I. INTRODUCTIONR ADIO frequency interference (RFI) has long been identified as a problem in L-band synthetic aperture radar (SAR), limiting the performance, and applicable signal bandwidth of SAR in many areas around the globe [1]. Most RFI are incoherent electromagnetic interference signals emitted by communication systems, television networks, air-traffic surveillance radars, and other mainly military-based radiation sources whose operating frequency overlaps with the frequency band of a SAR system. RFI usually appear as various kinds of bright linear features in the azimuth-time rangefrequency diagram of a SAR dataset and the interference-to-SAR signal ratio is often high. Incoherent RFI will be partly mitigated by the matched filter process that is applied in the SAR image focusing due to Manuscript

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