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Synthetic Aperture Radar (SAR) observations are widely used in emergency response for flood mapping and monitoring. However, the current operational services are mainly focused on flood in rural areas and flooded urban areas are less considered. In practice, urban flood mapping is challenging due to the complicated backscattering mechanisms in urban environments and in addition to SAR intensity other information is required. This paper introduces an unsupervised method for flood detection in urban areas by synergistically using SAR intensity and interferometric coherence under the Bayesian network fusion framework. It leverages multi-temporal intensity and coherence conjunctively to extract flood information of varying flooded landscapes. The proposed method is tested on the Houston (US) 2017 flood event with Sentinel-1 data and Joso (Japan) 2015 flood event with ALOS-2/PALSAR-2 data. The flood maps produced by the fusion of intensity and coherence and intensity alone are validated by comparison against high-resolution aerial photographs. The results show an overall accuracy of 94.5% (93.7%) and a kappa coefficient of 0.68 (0.60) for the Houston case, and an overall accuracy of 89.6% (86.0%) and a kappa coefficient of 0.72 (0.61) for the Joso case with the fusion of intensity and coherence (only intensity). The experiments demonstrate that coherence provides valuable information in addition to intensity in urban flood mapping and the proposed method could be a useful tool for urban flood mapping tasks.

Rainbands are essential to tropical cyclones (TCs), significantly affecting TC structure and intensity change. High‐resolution synthetic aperture radar (SAR) imagery can capture the footprints of rainbands caused by rain‐induced sea surface roughness modification. Using 464 SAR TC images, we investigated the rainband‐occurrence probability of TCs under different hemispheres, local times (LTs), intensities, and ocean basins. Results show that the rainband‐occurrence probability is highest in the downshear‐left quadrant for Northern Hemisphere TCs (downshear‐right quadrant for Southern Hemisphere TCs). For Northern Hemisphere TCs, the rainband‐occurrence probability is overall higher in the early morning (LT), and the peak region of rainband‐occurrence probability appears farther from the TC center in the evening (LT). Compared with weak TCs, the rainband‐occurrence probability becomes higher for strong TCs in the Northern Hemisphere. Furthermore, TCs have a higher rainband‐occurrence probability in the Northwest Pacific than in the North Atlantic and Northeast Pacific. Plain Language Summary Rainbands are a salient feature of tropical cyclones (TCs) and are closely related to TC structure and intensity change. Synthetic aperture radar (SAR) can capture the sea surface imprint of rainbands beneath clouds caused by rain‐induced sea surface roughness modification. Using 464 SAR TC images, we made 464 rainband‐annotated data. The data were mapped to grid nodes spaced at 0.027 times the radius of max winds in a coordinate system with the origin at the TC center and the y‐axis in the vertical wind shear direction. Then, the data were composited to estimate and further investigate the rainband‐occurrence probability of TCs under different hemispheres, local times (LTs), intensities, and ocean basins. Results show that the rainband‐occurrence probability is highest in the downshear‐left quadrant for Northern Hemisphere TCs (downshear‐right quadrant for Southern Hemisphere TCs). For Northern Hemisphere TCs, the rainband‐occurrence probability is overall higher in the early morning (LT), and the peak region of rainband‐occurrence probability appears farther from the TC center in the evening (LT). Compared with weak TCs, the rainband‐occurrence probability becomes higher for strong TCs in the Northern Hemisphere. Furthermore, TCs have a higher rainband‐occurrence probability in the Northwestern Pacific than in the North Atlantic and Northeast Pacific. Key Points The sea surface imprint of tropical cyclone (TC) rainbands in many synthetic aperture radar images reveals their occurrence probability The rainband‐occurrence probability is overall higher in the early morning than in the evening. The feature is more obvious in strong TCs The peak region of the probability appears farther from the TC center in the evening than in the early morning

Field‐based studies are limited in Himalaya‐Karakoram (HK); therefore, remote sensing and glaciohydrological modeling provide alternative solutions to investigate runoff evolution under changing climate conditions. Due to limited in situ runoff data in HK, glaciohydrological models are often calibrated using high‐resolution remote sensing data. This study introduces the calibration of the glaciohydrological model Spatial Processes in Hydrology (SPHY), at glacier catchment‐scale over 2000–2023 using satellite‐based Sentinel‐1 Synthetic Aperture Radar (SAR) wet snow maps, along with available geodetic mass balance estimates in the HK region. The selected calibrated model parameters are validated against in situ runoff data to test the robustness of satellite‐based calibration for Chhota Shigri Glacier (CSG), Dokriani Bamak Glacier (DBG), and Gangotri Glacier System (GGS) catchments in HK. The SPHY modeled and in situ catchment‐wide runoff estimates show good agreement. The Sentinel‐1 SAR‐derived wet snow percentage area shows strong spatial and temporal variability from 2015 to 2023. The mean annual runoff is 1.79 ± 0.15 m3s−1, 1.63 ± 0.09 m3s−1 and 39.40 ± 3.15 m3s−1 over 2000–2023 for CSG, DBG and GGS catchments, respectively. Maximum annual runoff occurred in 2021/2022, mainly due to heatwaves in early spring/summer 2022. Snowmelt runoff is highest in CSG (61%) and GGS (49%), while rainfall‐runoff dominates in DBG (42%). Satellite‐based glaciohydrological model calibration offers a unique opportunity to improve runoff estimates for glacierized catchments in data‐sparse regions. Applying present study to glacierized catchments lacking in situ runoff data will strengthen our past, present, and future glaciohydrological understanding of regions such as HK and Andes.

Synthetic aperture radar (SAR) imaging is used to identify ships, which is a vital task in the maritime industry for managing maritime fisheries, marine transit, and rescue operations. However, some problems, like complex background interferences, various size ship feature variations, and indistinct tiny ship characteristics, continue to be challenges that tend to defy accuracy improvements in SAR ship detection. This research study for multiscale SAR ships detection has developed an upgraded YOLOv5s technique to address these issues. Using the C3 and FPN + PAN structures and attention mechanism, the generic YOLOv5 model has been enhanced in the backbone and neck section to achieve high identification rates. The SAR ship detection datasets and AirSARship datasets, along with two SAR large scene images acquired from the Chinese GF-3 satellite, are utilized to determine the experimental results. This model’s applicability is assessed using a variety of validation metrics, including accuracy, different training and test sets, and TF values, as well as comparisons with other cutting-edge classification models (ARPN, DAPN, Quad-FPN, HR-SDNet, Grid R-CNN, Cascade R-CNN, Multi-Stage YOLOv4-LITE, EfficientDet, Free-Anchor, Lite-Yolov5). The performance values demonstrate that the suggested model performed superior to the benchmark model used in this study, with higher identification rates. Additionally, these excellent identification rates demonstrate the recommended model’s applicability for maritime surveillance.

The goal of this study was to investigate the performance of a spectral-transformation wave retrieval algorithm and confirm the accuracy of wave retrieval from C-band Chinese Gaofen-3 (GF-3) Synthetic Aperture Radar (SAR) images. More than 200 GF-3 SAR images of the coastal China Sea and the Japan Sea for dates from January to July 2020 were acquired in the Quad-Polarization Strip (QPS) mode. The images had a swath of 30 km and a spatial resolution of 8 m pixel size. They were processed to retrieve Significant Wave Height (SWH), which is simulated from a numerical wave model called Simulating WAves Nearshore (SWAN). The first-guess spectrum is essential to the accuracy of Synthetic Aperture Radar (SAR) wave spectrum retrieval. Therefore, we proposed a wave retrieval scheme combining the theocratic-based Max Planck Institute Algorithm (MPI), a Semi-Parametric Retrieval Algorithm (SPRA), and the Parameterized First-guess Spectrum Method (PFSM), in which a full wave-number spectrum and a non-empirical ocean spectrum proposed by Elfouhaily are applied. The PFSM can be driven using the wind speed without calculating the dominant wave phase speed. Wind speeds were retrieved using a Vertical-Vertical (VV) polarized geophysical model function C-SARMOD2. The proposed algorithm was implemented for all collected SAR images. A comparison of SAR-derived wind speeds with European Center for Medium-Range Weather Forecasts (ECMWF) ERA-5 data showed a 1.95 m/s Root-Mean-Squared Error (RMSE). The comparison of retrieved SWH with SWAN-simulated results demonstrated a 0.47 m RMSE, which is less than the 0.68 m RMSE of SWH when using the PFSM algorithm.

Synthetic aperture radar (SAR) tomography (TomoSAR) is an emerging technology to image the 3D structure of the illuminated media. TomoSAR exploits the key feature of microwaves to penetrate into vegetation, snow, and ice, hence providing the possibility to see features that are hidden to optical and hyper-spectral systems. The research on the use of P-band waves, in particular, has been largely propelled since 2007 in experimental studies supporting the future spaceborne Mission BIOMASS, to be launched in 2022 with the aim of mapping forest aboveground biomass (AGB) accurately and globally. The results obtained in the frame of these studies demonstrated that TomoSAR can be used for accurate retrieval of geophysical variables such as forest height and terrain topography and, especially in the case of dense tropical forests, to provide a more direct link to AGB. This paper aims at providing the reader with a comprehensive understanding of TomoSAR and its application for remote sensing of forested areas, with special attention to the case of tropical forests. We will introduce the basic physical principles behind TomoSAR, present the most relevant experimental results of the last decade, and discuss the potentials of BIOMASS tomography.

Synthetic Aperture Radar (SAR) can image the ground with a wide area and high resolution at all times and in all weather and has become an important means of remote sensing. Differential interferometry SAR technology can obtain high-precision surface deformation information by processing more than two SAR images before and after deformation. In recent years, it has attracted widespread attention and research. However, due to the increasing number of ground electronic devices, ground radio frequency interference (RFI) has become one of the main problems in differential interferometry SAR processing, seriously affecting the performance of differential interferometry SAR imaging and differential interferometry surface deformation monitoring applications. In this paper, a clutter cancellation interference enhancement detection algorithm is proposed. By clutter suppression in the primary and secondary images, the interference-to-signal ratio is increased, which effectively improves the interference detection capabilities. The effectiveness of the algorithm in this paper is verified by the on-orbit measured data of the Lutan-1 satellites.

This paper presents the first comprehensive review on the scientific utilization of earth observation data provided by the German TerraSAR-X mission. It considers the different application fields and technical capabilities to identify the key applications and the preferred technical capabilities of this high-resolution SAR satellite system from a scientific point of view. The TerraSAR-X mission is conducted in a close cooperation with industry. Over the past decade, scientists have gained access to data through a proposal submission and evaluation process. For this review, we have considered 1636 data utilization proposals and analyzed 2850 publications. In general, TerraSAR-X data is used in a wide range of geoscientific research areas comprising anthroposphere, biosphere, cryosphere, geosphere, and hydrosphere. Methodological and technical research is a cross-cutting issue that supports all geoscientific fields. Most of the proposals address research questions concerning the geosphere, whereas the majority of the publications focused on research regarding “methods and techniques”. All geoscientific fields involve systematic observations for the establishment of time series in support of monitoring activities. High-resolution SAR data are mainly used for the determination and investigation of surface movements, where SAR interferometry in its different variants is the predominant technology. However, feature tracking techniques also benefit from the high spatial resolution. Researchers make use of polarimetric SAR capabilities, although they are not a key feature of the TerraSAR-X system. The StripMap mode with three meter spatial resolution is the preferred SAR imaging mode, accounting for 60 percent of all scientific data acquisitions. The Spotlight modes with the highest spatial resolution of less than one meter are requested by only approximately 30 percent of the newly acquired TerraSAR-X data.

The present study attempts to identify the flood footprint of river Keleghai, a tributary of river Haldi, in West Bengal, India. Keleghai basin is known for recurrent flooding that causes severe damage to the socioeconomic infrastructure. So far, no attempt has been made for the identification of flood inundation footprints and flood risk zones in Keleghai river basin through remote sensing and multi-criteria decision-making process. Initially, a flood susceptibility or vulnerability map has been prepared, and secondly, flood footprints have been identified in the said river basin. For the preparation of flood vulnerability map with the help of the analytical hierarchy process (AHP), the elevation, slope, rainfall, normalised difference vegetation index (NDVI), land use and land cover (LULC) and distance from river and topographical wetness index (TWI) of the concerned river basin have been used. To prepare the flood footprints synthetic aperture radar (SAR), data have been processed on Google Earth Engine (GEE) platform. The result shows that more than 50% of the basin area belongs to high risk zone, and the other 40% comes under the moderate risk category. The central, northern and eastern parts of the basin present the highest susceptibility to flood hazard. This area is characterised by moderate-to-low elevation, gentle slope, moderate rainfall and less vegetative cover. This outcome can effectively be utilised in hazard management purpose for Keleghai as well as other river basins. This study will help in identifying the most vulnerable zones of the basin in terms of flood hazard assessment. On the other hand, correlating the empirical model with the real world data will provide excellent opportunity to testify the applicability of the model in decision-making purpose that could lead to a way of resilience.

The estimation and monitoring of 3D forest structure at large scales strongly rely on the use of remote sensing techniques. Today, two of them are able to provide 3D forest structure estimates: lidar and synthetic aperture radar (SAR) configurations. The differences in wavelength, imaging geometry, and technical implementation make the measurements provided by the two configurations different and, when it comes to the sensitivity to individual 3D forest structure components, complementary. Accordingly, the potential of combining lidar and SAR measurements toward an improved 3D forest structure estimation has been recognised from the very beginning. However, until today there is no established framework for this combination. This paper attempts to review differences, commonalities, and complementarities of lidar and SAR measurements. First, vertical lidar reflectance and SAR reflectivity profiles at different wavelengths are compared in different forest types. Then, current perspectives on their combination for the generation of enhanced structure products are discussed. Two promising frameworks for combining lidar and SAR measurements are reviewed. The first one is a model-based framework where lidar-derived parameters are used to initialize SAR scattering models, and relies on both the validity of the models and on the physical equivalence of the used lidar and SAR parameters. The second one is a structure-based framework based on the ability of lidar and SAR measurements to express physical forest structure by means of appropriate indices. These indices can then be used to establish a link between the two kind of measurements. The review is supported by experimental results achieved using space- and airborne data acquired in recent relevant mission and campaigns.