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Soil moisture estimates at high spatial and temporal resolution are of great value for optimizing water and agricultural management. To fill the gap between local ground observations and coarse spatial resolution remote sensing products, we use Soil Moisture Active Passive (SMAP) and Sentinel-1 data together with a unique data set of ground-based soil moisture estimates by cosmic ray neutron sensors (CRNS) and capacitance probes to test the possibility of downscaling soil moisture to the sub-kilometre resolution. For a high-latitude study area within a highly heterogeneous landscape and diverse land use in Denmark, we first show that SMAP soil moisture and Sentinel-1 backscatter time series correlate well with in situ CRNS observations. Sentinel-1 backscatter in both VV and VH polarizations shows a strong correlation with CRNS soil moisture at higher spatial resolutions (20–400 m) and exhibits distinct and meaningful signals at different land cover types. Satisfactory statistical correlations with CRNS soil moisture time series and capacitance probes are obtained using the SMAP Sentinel-1 downscaling algorithm. Accounting for different land use in the downscaling algorithm additionally improved the spatial distribution. However, the downscaling algorithm investigated here does not fully account for the vegetation dependency at sub-kilometre resolution. The study suggests that future research focussing on further modifying the downscaling algorithm could improve representative soil moisture patterns at a fine scale since backscatter signals are clearly informative. Highlights. Backscatter produces informative signals even at high resolutions. At the 100 m scale, the Sentinel-1 VV and VH polarizations are soil moisture dependent. The downscaling algorithm is improved by introducing land-cover-dependent clusters. The downscaled satellite and CRNS soil moisture agree best at the agricultural site.

Resumen La polarimetría con radar de apertura sintética (SAR) es una técnica conocida en teledetección por sus aplicaciones en el monitoreo de cambios sobre la superficie terrestre y en cuerpos de agua. El objetivo de la polarimetría radar es caracterizar la superficie terrestre analizando las propiedades de la señal que se dispersa cuando se utilizan diferentes combinaciones de polarización de las antenas transmisoras y receptoras, definidas como canales polarimétricos. La utilización de la polarización cruzada (vertical/horizontal) se mostró eficiente para estimar la extensión de las inundaciones producidas entre el 22 y el 25 de julio de 2021 en la cuenca del río Frío (Costa Rica). Para ello se utilizaron imágenes captadas por el sensor Sentinel 1A, de la Agencia Espacial Europea, mediante un flujo de procesamiento y una composición RGB posterior que permitió establecer una clara distinción entre las zonas inundadas de los cuerpos de agua permanentes. La utilización de imágenes ópticas para el mapeo de inundaciones está limitada por las condiciones climáticas, especialmente por la presencia de nubes. Las mediciones con SAR son independientes de la hora del día y de las condiciones climáticas, proporcionando valiosa información para el monitoreo de inundaciones (Horritt, 2006, p 187). Además, la extensión de las inundaciones es un factor crítico para la calibración y validación de modelos hidráulicos. La extensión de las inundaciones puede usarse también para evaluación y gestión del riesgo, y pueden beneficiarse de ello rescatistas durante las inundaciones (Corbley, 1999, p 1). Abstract Synthetic Aperture Radar (SAR) polarimetry is a remote sensing technique known for being applied to monitoring of land surface and water bodies. Polarimetry aims at charactering the land surface through the analysis of its response under different combinations of transmitting and receiving antennas polarization, called polarimetric channels. The use of cross-polarization (vertical/horizontal) proved to be efficient for estimating the extent of the floods between July 22nd and July 30th in the Frío river basin (Costa Rica). To that end, Sentinel-1 images from the European Space Agency have been used, with a workflow process and RGB composition that allowed to draw a clear distinction between flooded and permanent water bodies. The use of optical imagery for flood mapping is limited by weather conditions, specially by the presence of clouds. SAR measurements are independent of daytime and weather conditions, providing valuable information for monitoring flood events. Furthermore, flood extent is a critical factor for the calibration and validation of hydraulic models (Horritt, 2006). Flood extent can be also used for damage assessment and risk management and can be of benefit to rescuers during flooding (Corbley, 1999). Resumo A polarimetria por radar de abertura sintética (SAR) é uma técnica conhecida em sensoriamento remoto por suas aplicações no monitoramento de mudanças na superfície terrestre e em corpos d'água. O objetivo da polarimetria de radar é caracterizar a superfície terrestre analisando as propriedades do sinal que é espalhado quando são utilizadas diferentes combinações de polarização das antenas transmissoras e receptoras, definidas como canais polarimétricos. O uso da polarização cruzada (vertical/horizontal) mostrou-se eficiente para estimar a extensão das inundações produzidas entre 22 e 25 de julho de 2021 na bacia do rio Frío (Costa Rica). Para isso, foram utilizadas imagens captadas pelo sensor Sentinel 1A da Agência Espacial Europeia, através de um fluxo de processamento e posterior composição RGB que permitiu distinguir claramente as áreas inundadas dos corpos d'água permanentes. O uso de imagens ópticas para mapeamento de inundações é limitado pelas condições climáticas, especialmente pela presença de nuvens. As medições de SAR são independentes da hora do dia e das condições meteorológicas, fornecendo informações valiosas para o monitoramento de enchentes (Horritt, 2006, p.187). Além disso, a extensão da inundação é um fator crítico para a calibração e validação de modelos hidráulicos. A extensão das inundações também pode ser usada para avaliação e gestão de risco e pode beneficiar os socorristas durante as inundações (Corbley, 1999, p. 1).

Upcoming Synthetic Aperture Radar (SAR) missions with larger swath like NASA-ISRO Synthetic Aperture Radar (NISAR) (~250 km) requires a large, flat and homogeneous low-background site, free from human-made structures for point targets deployment for calibration. Finding out such a large, flat, homogeneous area devoid of perceived sources of radar clutter is a challenging task. In this regard, Antarctica is a potential site for calibration as it fulfills many of the criteria required for the ideal calibration site and suitable for setting up a corner reflector (CR) network for SAR calibration. This network will help in the calibration study and aid the studies related to Interferometric SAR (InSAR) viz. ice velocity estimation. As a part of this activity, in-house designed and developed CRs were installed near Indian research base stations Maitri and Bharati at Antarctica during the 38th Indian Scientific Expedition to Antarctica (ISEA) during 2018–2019. Corner reflectors were designed keeping in mind the harsh environment of Antarctica, such as high katabatic winds, blizzards, snowfall and low temperature. Temporal and seasonal analysis of radar backscatter data using available Indian Radar Imaging Satellite (RISAT-1) and European SENTINEL-1 SAR satellite over Maitri and Bharati was carried out to find out the suitable CR deployment site. Super-hydrophobic microwave transparent cover was designed and installed over CRs to protect snow accumulation. Hydrophobic radar absorbing materials were installed over central-mount to decrease the background noise due to it. Various tests such as structural analysis of total CR system, thermal analysis of materials and Radar Cross Section (RCS) characterization of CR were done before sending it to Antarctica. Integrated Wide Swath (IW) mode data of Sentinel-1 satellite was used for analyzing time-series response of CR in SAR image.

Marine X-band radar has been suggested to be capable of monitoring significant wave heights in both offshore and open sea areas. In contrast to studies on offshore radar, significant wave height estimations from coastal radar images, which exhibit complicated radar backscattering features, have received little attention. This study proposes a method for retrieving the significant wave height from coastal areas that are often influenced by nononshore winds. The square root of the signal-to-noise ratio in radar images has been widely applied to estimate the significant wave height. However, nononshore wind cases show a poor correlation between the square root of the signal-to-noise ratio and the in situ significant wave height. In addition, the spectral shapes from radar images in nononshore wind cases are very different from those in onshore wind cases. To improve the significant wave height estimations from coastal radar images, we implement an artificial neural network algorithm. After training and testing the algorithm, we confirm that the estimated significant wave heights are more reliable for both onshore and nononshore wind cases if the square root of the signal-to-noise ratio, power from nearshore radar subimages, and in situ wind components are included in the input layer of the neural network.

Multiple-elevation-beam synthetic aperture radar (SAR) is a concept based on digital beamforming (DBF) in elevation and simultaneous recording of the echoes of multiple transmitted pulses. It enables high-resolution imaging of wide areas and is therefore ideal for the systematic observation of dynamic processes on the Earth's surface. Furthermore, if the pulse repetition interval (PRI) is continuously varied (staggered SAR), it is possible to map a wide continuous swath rather than multiple subswaths separated by ``blind'' ranges. Within the design of multiple-elevation-beam SAR, however, it is fundamental to consider how nadir echoes affect the mapping capabilities of systems with constant PRI and the image quality of staggered SAR systems, where nadir echoes are intrinsically smeared due to the PRI variation. This article addresses the characterization of nadir echoes in multiple-elevation-beam SAR with constant and variable PRI by presenting a parametric model for the nadir echo profile based on real radar measurements, a formulation of the nadir echo location and smearing in staggered SAR, and realistic simulations based on TerraSAR-X data, which show that nadir echo are likely to be barely visible in staggered SAR images. The results of this work are relevant to both the design of future SAR systems and the interpretation of the acquired data.

Snow is an important environmental variable and a primary water resource in many areas of the world. Monitoring seasonal snowpack properties is also crucial for properly managing snow-related hazards such as snow avalanches and snowmelt floods. Recently, an innovative radar architecture, based on the use of two receivers, has been proposed for snowpack monitoring for the case of dry snow, where the snowpack depth and bulk density can be calculated with one single radar measurement, without any kind of external aid. This article presents the extension of this innovative radar architecture for the case of wet snow. The approach to determine, not only the snowpack depth and bulk density but also the liquid water content, is outlined and discussed in detail, along with the experimental validation of the operating principle for two cases., This work was supported in part by the Italian Ministry of Education, University and Scientific Research (MIUR) under Project SIR2014 "SNOWAVE" RBSI148WE5. © 2022, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work.

One of the scientific payloads of Chang'E-5 (CE-5), i.e., the lunar penetrating array radar (LRPR), will carry out the in situ exploration of the regolith structure and guide the drilling sampling ...

The dual-frequency precipitation radar (DPR) onboard the global precipitation measurement (GPM) satellite provides valuable measurements of precipitation. In this study, the GPM DPR products (version 6) are validated against a ground-based S-band polarimetric radar in South China based on a volume-matching method. Good consistency is found for the reflectivity factor (Z) calibration of the two instruments. From the perspective of microphysics, the mass-weighted mean diameter ( $D_{m})$ estimates correspond well with those of the ground-based radar in the inner swath of the normal scan (NS); however, underestimation is found for the raindrop number concentration, indicated by the generalized intercept parameter ( $N_{w})$ , especially for the intense echoes. Thus, the GPM DPR product may fail to depict the microphysical characteristics of small-to-medium raindrops in high concentration for heavy rainfall in South China. This is attributed to the negative Z bias of the DPR caused probably by insufficient correction of attenuation, which also leads to clear underestimation in the liquid water content (W) and the rainfall rate (R) products for intense echoes. In the outer swath where only single-frequency retrieval is available, overestimation in $D_{m}$ exists regardless of echo intensity level, and more underestimation can be found in $N_{w}$ , W, and R especially for intense echoes. In the selected typhoon and squall line cases, better capability in revealing microphysical properties is also found for the inner swath of the NS. After adjusting the scan mode, the performance of the precipitation products in the outer swath can be improved by dual-frequency retrievals in the future.

In this article, an efficient radar fusion-before-imaging (RFBI) method for heterogeneous borehole radar systems is proposed. Different from conventional fusion-after-imaging processes, the sample sets collected from heterogeneous borehole radar systems (monostatic, bistatic, or multiple-input multiple-output) are first merged into one data set before the imaging process in RFBI, and a single imaging operation is demanded to obtain the target space image with high precision. Specifically, the diversity in heterogeneous borehole radar sample sets is taken into consideration, and the radar sample sets are inserted and fused into a high-dimensional sample set before imaging. The target space spectrum is generated according to the echo space-frequency constraint relationship, and the target space is extracted from one imaging process. The influence of clutters in RFBI results is reduced, and the imaging accuracy is satisfactory. Meanwhile, due to the fusion process ahead, the computational time of RFBI hardly increases with the number of radar sample sets. The synthetic and field experiment results show that RFBI demonstrates comparable accuracy as Kirchhoff migration and higher efficiency in processing large amounts of data sets at the cost of large memory requirement, which is suitable for the joint imaging of heterogeneous radar systems.

Undesired echo from a flying object (aerial clutter) significantly contaminates the received signal of a wind profiler radar (WPR) because it has high intensity and spreads over a wide Doppler velocity range. In this study, results of aerial clutter mitigation obtained by applying adaptive clutter suppression (ACS) to a 1.3-GHz WPR are shown. The 1.3-GHz WPR used in this study has a main antenna comprising 13 antenna subarrays (MSAs). five-element Yagi-Uda antennas were also used as antenna subarrays for detecting clutters from low elevation angles (CSAs). The CSAs were used only in reception and installed so that they covered most of the horizontal directions and the horizontal and vertical polarizations. The directionally constrained minimization of power (DCMP) method was used as the adaptive signal processing to mitigate clutter. By the DCMP method, the weighted sum of the signals collected by 13 MSAs and 11 CSAs was computed so that the power of output signals was minimized under the constraint of constant gain in the antenna beam direction. Results of a case study for an aerial clutter from a low elevation angle at 17:04:37 on October 1 2020 showed that an overlap of the aerial clutter over a desired echo (i.e., clear-air echo) was solved by decreasing the aerial clutter whose peak intensity was ~24 dB greater than that of the clear-air echo. In a case study at 09:30:27 on September 18 2020, effects of the DCMP method on the processed results were discussed.

形態: カラー図版あり, Physical characteristics: Original contains color illustrations, Accepted: 2020-11-06, 資料番号: PA2110067000

For radar modeling and simulation of sea clutter, there are a number of key characteristics used to describe the observed features. These include the mean backscatter power, amplitude statistics, spatial and long-time temporal correlation, and the Doppler spectrum. Regarding the latter, a common modeling approach is to use the mean Doppler characteristics (width and center point) to relate to the sea conditions. However, this does not capture the time- and range-varying fluctuations of the Doppler spectra, which are important for assessing the behavior of coherent detection schemes. The evolving Doppler spectrum model offers a way to model these variations and can be described with a moderate number of parameters. Relating these to the observed geometry, environment and radar characteristics are important to make the models accessible. Previous work has characterized X-band radar sea clutter over a range of sea conditions and azimuth and grazing angles. In this article, new models are presented that capture variations with both the radar resolved area and integration time. This enables the evolved Doppler spectra model to be used over a much wider range of applications.

Multiple radar systems represent an attractive option for target tracking because they can significantly enlarge the area coverage and improve both the probability of trajectory detection and the localization accuracy. The presence of multiple extended targets or weak targets is a challenge for multiple radar systems. Moreover, their performance may be severely deteriorated by regions characterized by a high clutter density. In this article, an algorithm for detection and tracking of multiple targets, extended or weak, based on measurements provided by multiple radars in an environment with heavily cluttered regions, is proposed. The proposed method features three stages. In the first stage, past measurements are exploited to build a spatiotemporal clutter map in each radar; a weight is then assigned to each measurement to assess its significance. In the second stage, a track-before-detect algorithm, based on a weighted 3-D Hough transform, is applied to obtain target tracklets. In the third stage, a low-complexity tracklet association method, exploiting a lion reproduction model, is applied to associate tracklets of the same target. Three experiments are presented to illustrate the effectiveness of the proposed approach. The first experiment is based on synthetic data, the second one is based on actual data from a radar network with two homogeneous air surveillance radars, and the third one is based on actual data from a radar network with four different marine surveillance radars. The results reveal that the proposed method can outperform competing approaches.

Convective precipitation nowcasting remains challenging due to the fast change in convective weather. Radar images are the most important data source in nowcasting research area. This study proposes a radar data-based U-Net model for precipitation nowcasting. The nowcasting problem is first transformed into an image-to-image translation problem in deep learning under the U-Net architecture, which is based on convolutional neural networks (CNNs). The input of the model is five consecutive radar images; the output is the predicted radar reflectivity image. The model consists of three operations: upsampling, downsampling, and skip connection. Three methods, U-Net, TREC, and TrajGRU, are used for comparison in the experiments. The experimental results show that both deep learning methods outperform the TREC method, and the CNN-based U-Net can achieve almost the same performance as TrajGRU which is a recurrent neural network (RNN)-based model. With the advantages that U-Net is simple, efficient, easy to understand, and customize, this result shows the great potential of CNN-based models in addressing time-series applications.

The accumulation rate is a key parameter in computing the mass balance of glaciers and ice sheets to estimate sea level rise. A shallow-layers-detection ice sounding radar (SLDISR) is developed to measure the accumulation rate and shape of near-surface internal layers with high resolution. With a transmitting frequency from 500 to 2000 MHz, this frequency-modulated continuous wave (FMCW) radar provides a range resolution of about 16 cm in free space by using a Hanning window and a penetrating depth about 150 m under polar ice. The spectral analysis and coherent integration techniques are used to obtain a high processing gain and to improve the signal-to-noise ratio of the system. A phase-locked loop with wideband yttrium iron garnet (YIG) oscillator is applied to generate a sweeping chirp signal as an input source for the transmitter. A stable, low-frequency reference chirp signal is generated with a direct digital synthesizer (DDS) integrated in field-programmable gate array (FPGA). To reduce the high-speed requirement to the analog-to-digital converter (ADC), dechirp technology is adopted at the RF section of the receiver. The implementation of the digital unit is based on an FPGA chip. The designed radar has been successfully deployed in Antarctica during the 31st Chinese Antarctic Research Expedition (CHINARE 31) and CHINARE 33, mainly over the East Antarctic Ice Sheet (EAIS). The echograms indicate the effectiveness of the radar system on detecting clear internal reflecting horizons (IRHs) over ice sheets.

The sea clutter, referred to as the time-varying radar backscatter from the ocean surface, plays a significant role in marine radar development. The ocean's complex hydrodynamics cause it to exhibit non-Gaussian and nonstationary characteristics, which brings challenges in the sea clutter modeling, especially for establishing its spatial-temporal correlated and coherent model. In this article, a data-driven method based on the Koopman mode decomposition (KMD) is proposed for modeling spatial-temporal correlated complex sea clutter. The method decomposes the coherent sea clutter dynamic behavior in terms of Koopman modes and corresponding temporal patterns. Then, these spatiotemporal patterns are used to construct the sea clutter state over time according to the approximate solution. Furthermore, this proposed state-of-the-art data-driven approach is benchmarked by the measured sea clutter data from intelligent PIXel processing radar (IPIX). It is demonstrated that the proposed approach accurately models the complex sea clutter with actual statistic characteristics, phase information, and spatial-temporal correlations. The mean absolute error (MAE) and root mean square error (RMSE) between the obtained and actual sea clutter are only 0.1817 and 0.2349, respectively. This work offers a practical approach for modeling sea clutter, especially when the spatial-temporal correlation and coherence information is needed.

Cross-polarized temporal coherence observations of a boreal forest, acquired using a tower-based radar, are presented in this article. Temporal coherence is analyzed with respect to frequency, temporal baseline, time of day of observation, season, meteorological variables, and biophysical variables. During the summer, P- and L-band temporal coherence exhibited diurnal cycles, which appeared to be due to high rates of transpiration and convective winds during the day. During the winter, freeze-thaw cycles and precipitation resulted in decorrelation. At temporal baselines of seconds to hours, a high temporal coherence was observed even at C-band. The best observation times of the day were midnight and dawn. Temporal coherence is the main limitation of accuracy in interferometric and tomographic forest applications. The observations from this experiment will allow for better spaceborne SAR mission designs for forest applications, better temporal decorrelation modeling, and more accurate forest parameter estimation algorithms using interferometric and tomographic SAR data.

Research based on Mars Advanced Radar for Subsurface and Ionosphere Sounding (MARSIS) data detected unusual radar bright basal reflections located at about 1.5 km depth in a Mars region denoted as Ultimi Scopuli. These reflections were interpreted as a signature of subglacial liquid water even though this interpretation is still being debated in the literature. In this article, we propose a novel approach to the detection and imaging of candidate subglacial liquid water from radar sounding data. The approach combines the radar echo power traces with a suitable digital elevation model to provide a bidimensional representation of the surface. Even if the imaging method reconstructs a representation of the surface, we prove that it can be used to identify subsurface bright reflections in icy regions. Imaging is feasible even if the basal interface is not directly included in the processed data for image generation. To support this experimental evidence, we show that a relationship exists between the value of the reflected echo power originating from the englacial layers and the basal-to-surface-echo-power ratio. The observed relationship holds on both Ultimi Scopuli radar sounding data acquired on Mars and Lake Vostok data acquired on Earth. Our results show that the 2-D imaging provides an alternative way for locating candidate subglacial liquid water bodies on Mars over large areas also where the basal interface is not directly measured. The proposed approach complements previous research for further evaluation of the actual presence of liquid water on Mars.

This article studies changes in microwave signals of oceanic snowfall in response to the formation of snow-covered sea ice using active and passive coincident data from the radar and radiometer onboard the CloudSat and the global precipitation measurement satellites. Using reanalysis data of liquid and ice water path as well as satellite retrievals of sea ice snow-cover depth, spectral regions are determined over which the snowfall signatures are likely to be obscured or falsely detected. Relying on an a priori database populated with the active-passive coincidences, a Bayesian snowfall retrieval algorithm is presented that links a k-nearest neighbor matching with the inverse Gaussian estimator used in the Goddard profiling algorithm. Without relying on any ancillary data of air temperature, the results demonstrate that over open oceans (sea ice), we can passively retrieve the CloudSat active snowfalls with a true positive rate of 92 (85%) and the root mean squared error of 0.24 (0.15) mm h⁻¹.

Radio frequency interference (RFI) has become a growing concern for weather radar, distorting radar variable estimation. By simultaneously or alternately transmitting the horizontal and vertical polarized waves, polarimetric weather radar can be referred to as SHV radar or AHV radar. The SHV radar can mimic the AHV radar by discarding either H- or V-channel measurements, which leads to an alternating scheme. In this research, the real RFI measurements from an operational C-band SHV radar are used to characterize the RFI temporal, spectral, and polarimetric features. Then, the RFI is simulated to quantify the performance of the object-orientated spectral polarimetric (OBSPol) filter in RFI mitigation. The OBSPol filter has been previously proposed by the authors to mitigate the narrowband clutter (both stationary and moving) and noise. This work extends the application of the filter to remove the RFI for SHV radar. Specifically, by taking advantage of the low copolar correlation of the RFI signal measured in AHV radar, the RFI mitigation method is designed, and its effectiveness is proven by qualitative and quantitative analyses. In particular, in the case of RFI overlapped to weather echoes in the time domain, the RFI can be mitigated, also when the duty cycle of the RFI is high. However, this work does not provide a full evaluation of the RFI mitigation performance on all radar data outputs but a proof of concept to show the effectiveness of the proposed filter for RFI mitigation.

A satellite mission onboard a radar sounder for the observation of the earth's polar regions can greatly support the monitoring of the cryosphere and climate change analyses. Several studies are in progress proposing the design and demonstrating the performance of such an earth-orbiting radar sounder (EORS). However, one critical aspect of the cryospheric targets that are often ignored and simplified in these studies is the complex geoelectrical nature of the polar ice. In this article, we present a performance assessment of the polar ice target detectability by focusing on their realistic representation. This is obtained by simulating the orbital radargrams corresponding to different regions of the polar cryosphere by leveraging the data available from airborne campaigns in Antarctica and Greenland. We propose novel performance metrics to analyze the detectability of the internal reflecting horizons (IRHs), the basal interface, and to analyze the nature of the basal interface. This performance assessment strategy can be applied to guide the design of the signal-to-noise ratio (SNR) budget at the surface, which can further support the selection of the main orbital instrument parameters, such as the transmitted power, the two-way antenna gain, and the processing gains.

The optical and radar satellite data based Global Digital Elevation Models (DEMs) serve as a main data source in hydrological studies today. Open access Remote Sensing (RS) products have increasing accuracy and improved spatial resolution. The main research purposes are to study the use of AW3D30, ASTER GDEM V003 and SRTMGL1 DEMs in modeling mountainous lake basin and comparative analysis of their spatial accuracy. Topographic maps and very high resolution satellite imagery of the study area have been applied for the spatial accuracy verification and surface referencing. The relative accuracies of three different DEMs were analyzed using 1284 elevation points on the terrain surfaces. Comparison of these DEMs with 30 m spatial resolution confirms that an AW3D30 DEM has a higher vertical accuracy compared to other models and can be used for modeling river and lake basins over high mountainous terrain. The detected unreliable sections of the modeled lake basin boundary were analyzed and corrected based on the basin delineation by different DEMs. Analysis of spatial accuracy of the delineated Issyk-Kul Lake basin provide further incentive for DEM modification based on higher resolution remote sensing data, considering the applied geoid height differences as well by using satellite positioning data.

Synthetic aperture radar (SAR) is a valuable tool for lake ice monitoring. The recently proposed SAR configuration for Earth observation called compact polarimetric (CP) SAR could be a good compromised choice between conventional (single or dual) and fully polarimetric (FP) SAR for operational ice applications, including lake ice. Given its enhanced radar target information compared with conventional SAR systems over wider swath coverage compared with FP SAR, CP systems could play important role in the new generation of Earth observation systems. Herein, we study the evolution of CP SAR parameters from simulated CP SAR data in relation to early ice growth. Focus of the study is on four lakes located in Cornwallis Island, Canadian Central Arctic. We adopt parameters extracted from dual circular polarimetric and right circular transmit, linear (horizontal and vertical) receive configurations. In this study, we consider the ice thickness calculated from an established empirical model. Meteorological and ice climatological data were used to support the analysis. Results demonstrated a potential connection between a number of CP parameters and lake ice growth. Furthermore, we were able to highlight the relationship between the density of air bubbles in ice layer and the intensity of volume scattering mechanism, leading to the identification of lakes with increased gas production activities. Thus, differences between lakes in terms of density of air bubbles were detected and statistically evaluated.

A measurement setup based on a high-precision, low-cost FMCW radar sensor has been developed for radar-based 1D deformation monitoring of engineering structures. The method enables contactless and ...

The NOAA Wide Swath Radar Altimeter (WSRA) and its processing are described. The WSRA provides real-time measurements of sea surface significant wave height and directional wave spectra during flights in hurricanes and other environments. The characteristics of near nadir scattering from the sea surface and the resulting distortion of the wave topography measured by the WSRA are discussed, as well as the simulation which generated a matrix to correct the directional wave spectra produced from the WSRA wave topography.

Recent work has demonstrated a passive radio sounding approach that uses the Sun as a source for echo detection and ranging. As the Sun is a moving source with a position that is known a priori , we evaluate this technique’s capabilities to measure the echo’s phase history, map topography, and perform synthetic aperture radar (SAR) focusing. Here, we present our approach to implementing passive SAR using a compact, temporally incoherent radio-astronomical source as a signal of opportunity. We first evaluate the passive system’s capabilities to obtain an echo from a rough surface by determining the critical signal-to-noise ratio (SNR) for reliably observing the Sun’s echo reflection with our passive instrument. We then demonstrate that our technique can detect the necessary changes in range, phase, and reflectivity of an echo from the Sun. We next present the experimental results of our passive radar testing using the Sun at Dante’s View, Death Valley, to highlight this technique’s ability to perform 2-D imaging. Finally, with synthetic data, we demonstrate that we can use time-domain backprojection to focus a planar white noise signal, perform passive SAR imaging, and improve the measurement’s SNR and azimuth resolution. The results of passive SAR focusing on white noise highlight the potential for the Sun and Jupiter’s radio emissions to perform surface and subsurface imaging for planetary and terrestrial observations.

Resumen La interferometría diferencial con radar de apertura sintética (SAR) es una técnica conocida en teledetección por sus aplicaciones en la generación de modelos digitales del terreno y el monitoreo de deformaciones de la corteza terrestre. Esta técnica se basa en la fase, un parámetro presente en las imágenes SAR y sensible a la topografía del terreno. Las diferencias de fase se relacionan con cambios de elevación ocurridos entre dos barridos del satélite sobre un mismo espacio. Para la obtención de información topográfica se requiere un modelo digital del terreno y la diferencia de fase permite realizar estimaciones de desplazamiento vertical del terreno con precisiones superiores a 1 centímetro. El presente trabajo permitió estimar la deformación superficial experimentada en la ladera oeste del cráter del volcán Turrialba tras la erupción del 1 de agosto de 2020. Para ello se utilizaron varias imágenes captadas por el sensor Sentinel 1A, de la Agencia Espacial Europea. Abstract Differential synthetic aperture radar interferometry (SAR) is a technique known in remote sensing for its applications in the generation of digital terrain models and the monitoring of deformations of the earth’s crust. This technique is based on the phase, a parameter present in SAR images and sensitive to terrain topography. Phase differences are related to elevation changes that occur between two satellite scans over the same space. To obtain topographic information, a digital terrain model is required and thus, phase differences provide information for estimating vertical terrain displacement to an accuracy greater than 1 centimeter. In the present work it was possible to estimate the surface deformation that occurred on the western slope of the crater of the Turrialba volcano after the eruption of August 1, 2020. For this purpose, several images captured by the Sentinel-1A sensor of the European Space Agency were used.

In this article, we propose a multichannel sea clutter model in a spaceborne early warning radar system and analyze the influence of the sea clutter motion characteristics on the space-time adaptive processing (STAP) performance. To establish a multichannel sea clutter model, the 3-D Gerstner wave model is applied to construct the sea surface. Then the Pierson–Moskowitz wave spectrum and the stereo wave observation project (SWOP) directional spectrum are combined to describe the amplitude distribution of waves in different frequencies and directions. At the same time, the two-scale model is applied to obtain the specific backscattering coefficients of sea clutter at different time and positions. In addition, breaking waves are added in sea clutter returns with the form of false targets. Finally, the space-time distribution characteristics of sea clutter in a spaceborne multichannel array system and the influences of sea clutter under different wind speeds and directions on STAP performance are analyzed based on the simulation processing results. Processing results of some real-measured radar data are also exhibited to verify the theoretical analyses.

The soil roughness and two-way vegetation transmissivity parameters affect the radar backscattered signal; therefore, the results of the soil moisture retrieval from Synthetic Aperture Radar (SAR) ...

Multistatic ground-penetrating radar (GPR) signals can be imaged tomographically to produce three-dimensional distributions of image intensities. In the absence of objects of interest, these intensities can be considered to be estimates of clutter. These clutter intensities spatially vary over several orders of magnitude, and vary across different arrays, which makes direct comparison of these raw intensities difficult. However, by gathering statistics on these intensities and their spatial variation, a variety of metrics can be determined. In this study, the clutter distribution is found to fit better to a two-parameter Weibull distribution than Gaussian or lognormal distributions. Based upon the spatial variation of the two Weibull parameters, scale and shape, more information may be gleaned from these data. How well the GPR array is illuminating various parts of the ground, in depth and cross-track, may be determined from the spatial variation of the Weibull scale parameter, which may in turn be used to estimate an effective attenuation coefficient in the soil. The transition in depth from clutter-limited to noise-limited conditions (which is one possible definition of GPR penetration depth) can be estimated from the spatial variation of the Weibull shape parameter. Finally, the underlying clutter distributions also provide an opportunity to standardize image intensities to determine when a statistically significant deviation from background (clutter) has occurred, which is convenient for buried threat detection algorithm development which needs to be robust across multiple different arrays., 12 pages, 11 figures

This study investigates the prediction of lightning flash rates using a ground-based, S-band polarimetric weather radar and storm cell-based lightning prediction. Both short-term cases during multi...

A better understanding of the hydrological functioning of irrigated crops using remote sensing observations is of prime importance in semi-arid areas where water resources are limited. Radar observations, available at high resolution and with a high revisit time since the launch of Sentinel-1 in 2014, have shown great potential for the monitoring of the water content of the upper soil and of the canopy. In this paper, a complete set of data for radar signal analysis is shared with the scientific community for the first time to our knowledge. The data set is composed of Sentinel-1 products and in situ measurements of soil and vegetation variables collected during three agricultural seasons over drip-irrigated winter wheat in the Haouz plain in Morocco. The in situ data gather soil measurements (time series of half-hourly surface soil moisture, surface roughness and agricultural practices) and vegetation measurements collected every week/2 weeks including aboveground fresh and dry biomasses, vegetation water content based on destructive measurements, the cover fraction, the leaf area index, and plant height. Radar data are the backscattering coefficient and the interferometric coherence derived from Sentinel-1 GRDH (Ground Range Detected High Resolution) and SLC (Single Look Complex) products, respectively. The normalized difference vegetation index derived from Sentinel-2 data based on Level-2A (surface reflectance and cloud mask) atmospheric-effects-corrected products is also provided. This database, which is the first of its kind made available open access, is described here comprehensively in order to help the scientific community to evaluate and to develop new or existing remote sensing algorithms for monitoring wheat canopy under semi-arid conditions. The data set is particularly relevant for the development of radar applications including surface soil moisture and vegetation variable retrieval using either physically based or empirical approaches such as machine and deep learning algorithms. The database is archived in the DataSuds repository and is freely accessible via the following DOI: https://doi.org/10.23708/8D6WQC (Ouaadi et al., 2020a).

Radar external calibration is an important measurement in determining the radar cross sections of unknown targets and the reflectance coefficients of the sea surface. This measurement is often made in an anechoic chamber by standard programs. Sometimes, this measurement must be made in outdoor conditions, and the ambient environment severely affects the calibration precision. Thus, the scheme of the measurement must be specially designed. In this article, a calibration conducted at the sea near Lingshan Island is reported, and a full program of an external calibration on the sea surface and relevant analysis are presented for a P-band island-based sea clutter measurement radar. Data analysis shows that the multipath effect from the sea surface is a major factor to degrade the precision of the calibration. Measurement data over 4 days and the multipath reflectance model of the sea surface are combined to estimate the power increment from the multipath effect. Radar system constant is estimated based on the estimated power increments at different sites, where the viewing geometries of the radar are different. The results show that the proposed external calibration method on the sea surface attains a satisfactory precision.

In the past 25 years, radar altimeters have been designed and operated primarily for ocean observation. However, over the past decade there has been a growing interest for altimetry measurements over inland waters. Studying lakes, reservoirs and rivers water level is of prime importance for the hydrology community to assess the Earth’s global resources of fresh water. Satellite altimetry is key to providing such global and continuous datasets of water surface height. Indeed, much progress has been made in altimeters capability to acquire quality measurements over inland waters. In this paper we present an overview of major technical evolutions of the tracking function of altimeters, from Jason-2/POS3 and SARAL/AltiKa to Jason-3/POS3B and Sentinel-3/SRAL and the improvements brought by the Open-Loop Tracking Command (OLTC) to extend the altimeter observation domain to inland waters. The OLTC is an on-board feature used to set the altimeter waveforms reception window, containing a priori elevation information built from a dedicated database of hydrological targets. This paper focuses on the use of OLTC for acquiring reliable water signal rather than discussing the accuracy of the retrieved height, which constitutes a further step in the altimeter data processing chain. We show how Jason-3 and Sentinel-3 altimetry missions are currently able to observe and monitor about 70,000 hydrological targets over the globe and how it is contributing in building a global dataset of inland waters level, in preparation for future missions such as Sentinel-6 and the Surface Water Ocean Topography mission (SWOT).

Summarization: Time is the fundamental measurement in satellite altimetry and the key parameter in building and keeping up a long-term, consistent, and reliable record for monitoring changes in sea level. Over the years, different time scales, although interconnected, have been used in altimetry and also in satellite positioning. This sometimes leads to inexplicit descriptions and ambiguous use of time and orbit coordinates as well as of their transformations between various reference and measuring systems. Altimetry satellites, like Sentinel-3, CryoSat-2, Jason-3, HY-2A/-2B, IceSat-2, etc., observe and practically realize ranges by measuring time differences between the transmission and reception of an electromagnetic wave (either radar or laser at present). Similar principles apply for global navigation satellite systems and for their terrestrial reference systems upon which altimetry is linked and tied together. Yet, the “meter” of any terrestrial reference systems is also defined by time. This work seeks to establish a standard reference system for “time” and “coordinates” in an effort to reach uniform and absolute standardization for satellite altimetry. It describes various errors generated from differences, discontinuities and interactions in time, frequency, range, time tagging, and reference coordinate frames. A new approach, called “fiducial reference measurements for altimetry”, is here given to examine ways to connect errors with metrology standards in order to improve the estimation of uncertainty budgets in ocean and water level observation by altimetry. Finally, the geocentric inertial reference system and the international atomic time are proposed in this paper for satellite altimetry observations and products. Presented on: Advances in Space Research

The ground-based radar quantitative precipitation estimation (QPE) faces various challenges including the overestimation caused by the bright band (BB) in the stratiform region and the underestimation in mountainous areas when the terrain-enhanced precipitation occurs at the levels below ground-based radar measurements. The vertical precipitation structure provided by spaceborne radars, i.e., the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and the Global Precipitation Measurement mission (GPM) Dual-frequency PR (DPR), is valuable for mitigating the above problems. Since the spaceborne radars and ground-based radars usually operate in different frequencies, e.g., the TRMM PR and the KuPR of GPM DPR work in Ku-band (13.8 and 13.6 GHz, respectively) and the ground-based radars in western China work in C-band (5.4 GHz), the reflectivity conversion from Ku-band to C-band is necessary before the vertical profile of reflectivity (VPR) measured by spaceborne radars can be utilized to improve the ground-based radar QPE in western China. This study presents a conversion method using GPM DPR measurements, i.e., the drop size distribution (DSD) for different precipitation types (the stratiform with/without BB and the convective cases) and particle phases (the solid, melting, and liquid). Using the ${T}$ -matrix method, the reflectivity difference between Ku-band and C-band is found and the Ku-band to C-band conversion relations are derived with the linear regression. These conversion relations have been validated by matching and comparing the converted C-band reflectivity with the C-band ground-based radar measurements. The results demonstrate the effectiveness and reliability of the conversion. This method can be extended for the reflectivity conversion in other frequencies and can facilitate the incorporation of reflectivity measurements from various instruments.

A ground-based scatterometer was installed on an alpine meadow over the Tibetan Plateau to study the soil moisture and temperature dynamics of the top soil layer and air–soil interface during the period August 2017–August 2018. The deployed system measured the amplitude and phase of the ground surface radar return at hourly and half-hourly intervals over 1–10 GHz in the four linear polarization combinations (vv, hh, hv, vh). In this paper we describe the developed scatterometer system, gathered datasets, retrieval method for the backscattering coefficient (σ0), and results of σ0. The system was installed on a 5 m high tower and designed using only commercially available components: a vector network analyser (VNA), four coaxial cables, and two dual-polarization broad-band gain horn antennas at a fixed position and orientation. We provide a detailed description on how to retrieve the backscattering coefficients for all four linear polarization combinations σpq0, where p is the received and q the transmitted polarization (v or h), for this specific scatterometer design. To account for the particular effects caused by wide antenna radiation patterns (G) at lower frequencies, σ0 was calculated using the narrow-beam approximation combined with a mapping of the function G2/R4 over the ground surface. (R is the distance between antennas and the infinitesimal patches of ground surface.) This approach allowed for a proper derivation of footprint positions and areas, as well as incidence angle ranges. The frequency averaging technique was used to reduce the effects of fading on the σpq0 uncertainty. Absolute calibration of the scatterometer was achieved with measurements of a rectangular metal plate and rotated dihedral metal reflectors as reference targets. In the retrieved time series of σpq0 for L-band (1.5–1.75 GHz), S-band (2.5–3.0 GHz), C-band (4.5–5.0 GHz), and X-band (9.0–10.0 GHz), we observed characteristic changes or features that can be attributed to seasonal or diurnal changes in the soil: for example a fully frozen top soil, diurnal freeze–thaw changes in the top soil, emerging vegetation in spring, and drying of soil. Our preliminary analysis of the collected σpq0 time-series dataset demonstrates that it contains valuable information on water and energy exchange directly below the air–soil interface – information which is difficult to quantify, at that particular position, with in situ measurement techniques alone. Availability of backscattering data for multiple frequency bands (raw radar return and retrieved σpq0) allows for studying scattering effects at different depths within the soil and vegetation canopy during the spring and summer periods. Hence further investigation of this scatterometer dataset provides an opportunity to gain new insights in hydrometeorological processes, such as freezing and thawing, and how these can be monitored with multi-frequency scatterometer observations. The dataset is available via https://doi.org/10.17026/dans-zfb-qegy (Hofste et al., 2021). Software code for processing the data and retrieving σpq0 via the method presented in this paper can be found under https://doi.org/10.17026/dans-xyf-fmkk (Hofste, 2021).

Pulse compression has enabled a new generation of low-cost and compact spaceborne weather radar systems. To successfully utilize pulse compression techniques for cloud and precipitation applications, the effects of Doppler-range migration must be considered during the design and operation of the radar. Pulse compression for spaceborne weather applications introduces additional interdependence between the radar system and the operations when compared with traditional pulsed radar systems, primarily as a result of the large platform velocities. Pulse compression signals for weather radar can be simulated with high fidelity to predict and optimize the radar’s performance. In this article, we evaluate the pulse compression performance of RainCube, a Ka-band precipitation radar in a CubeSat, through analysis and comparison of observations and radar simulations. Through these comparisons, design and operational considerations for pulse compression weather radar are discussed. This work shows that the optimal pointing angle for RainCube to achieve the finest vertical resolution is not at nadir, but when pointing forward approximately 2.25°, in the direction of the spacecraft’s orbit.

One of the fundamental technical challenges of any new spaceborne vegetation remote sensing mission is the determination of what sensor(s) to place onboard and what, if any, overlapping modes of operation they will employ as each onboard sensor adds significant cost to the overall mission. In this article, the remote sensing of forest parameters using multimodal remote sensing is presented. In particular, polarimetric radar, Light Detection And Ranging (LiDAR), and near-IR passive optical sensing platforms are employed in conjunction with physics-based models. These models are used to accurately estimate forest aboveground biomass as well as canopy height in homogeneous areas. It is shown that this proposed method is capable of achieving high accuracy estimates while using minimal ancillary data in the estimation process. We present a method to combine measured data sets with our geometric and electromagnetic sensor models to develop a forest parameter estimation algorithm that fuses multimodal remote sensing technologies with a minimal amount of ground information and yields an accurate estimate of forest structure including dry biomass and canopy height with rms errors of 1.6 kg/m2 and 1.68 m respectively.

Clouds and precipitation have vital roles in the global hydrological cycle and the radiation budget of the atmosphere–Earth system and are closely related to both the regional and the global climate. Changes in the status of the atmosphere inside clouds and precipitation systems are also important, but the use of multi-source datasets is hampered by their different spatial and temporal resolutions. We merged the precipitation parameters measured by the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) with the multi-channel cloud-top radiance measured by the visible and infrared scanner (VIRS) and atmospheric parameters in the ERA5 reanalysis dataset. The merging of pixels between the precipitation parameters and multi-channel cloud-top radiance was shown to be reasonable. The 1B01-2A25 dataset of pixel-merged data (1B01-2A25-PMD) contains cloud parameters for each PR pixel. The 1B01-2A25 gridded dataset (1B01-2A25-GD) was merged spatially with the ERA5 reanalysis data. The statistical results indicate that gridding has no unacceptable influence on the parameters in 1B01-2A25-PMD. In one orbit, the difference in the mean value of the near-surface rain rate and the signals measured by the VIRS was no more than 0.87 and the standard deviation was no more than 2.38. The 1B01-2A25-GD and ERA5 datasets were spatiotemporally collocated to establish the merged 1B01-2A25 gridded dataset (M-1B01-2A25-GD). Three case studies of typical cloud and precipitation events were analyzed to illustrate the practical use of M-1B01-2A25-GD. This new merged gridded dataset can be used to study clouds and precipitation systems and provides a perfect opportunity for multi-source data analysis and model simulations. The data which were used in this paper are freely available at https://doi.org/10.5281/zenodo.4458868 (Sun and Fu, 2021).

Radar sounder surface imaging is a rather unexplored approach to the analysis of planetary bodies. While a radar sounder is an instrument specifically designed for subsurface investigations, a particular set of power measurements (denoted as off-nadir surface echoes) can be exploited together with an external digital elevation model to produce images of the investigated surface at meters wavelength. The use of the off-nadir data may also reveal the presence of previously undetected subsurface features. In this article, we present a method for producing surface roughness images by high-frequency (HF) radar sounder data. The study of surface roughness in the HF band is particularly useful for both geologic studies and landing-zone reconnaissance as it is evaluated at meters to hundreds of meters horizontal scale. The proposed method combines off-nadir data of the Shallow Radar Sounder (SHARAD) with the Mars Orbiter Laser Altimeter (MOLA) digital elevation model. The produced roughness images at 20 MHz (15-m wavelength) of the Martian surface provide higher coverage and resolution of the surface roughness characterization at a 10–100-m horizontal scale than previous SHARAD work. By comparing the experimental roughness image with the one obtained by radar backscattering simulations, it is possible to identify subsurface features. In our experiments, we were able to produce a bidimensional image of a previously undetected large buried crater (10 km $\times12$ km) located in the Nili Fossae. This finding opens up new possibilities in exploiting radar sounding data for better detecting shallow subsurface features.

Slope correction is important to improve the accuracy of satellite radar elevation measurements by mitigating the slope-induced error (SE), especially over uneven ground surfaces. Although several slope correction methods have been proposed, guidance in the form of stepwise algorithm on how to implement these methods in processing radar altimetric data at the coding level, and the differences among these methods need to be presented and discussed systematically. In this paper, three existing types of slope correction methods—the direct method (DM), intermediate method (IM), and relocation method (RM, further divided into RM1 and RM2)—are described in detail. In addition, their main differences and features for various scientific applications are analyzed. We conduct a systematic experiment with CryoSat-2 Low Resolution Mode (LRM) data in a physically stable area around Dome Argus in East Antarctica, where in-situ measurements were available for comparison. The slope correction is implemented separately using the three methods, with the latest high-accuracy Reference Elevation Model of Antarctica (REMA) as the a-priori topography model. The bias and precision of the slope-corrected CryoSat-2 data results from the RM2 is −0.18 ± 0.86 m based on the comparison with the field Global Navigation Satellite System (GNSS) data. The results from the RM2 indicate higher precision compared to those from the RM1. According to the correlation analysis of the slope-corrected CryoSat-2 data results (RM1 and RM2), the bias enlarges and the precision becomes worse when the surface slope increases from 0 to 0.85°. After a comprehensively comparative analysis, we find that the results from the RM1 and RM2 are superior in precision (0.93 m and 0.86 m) with respect to the GNSS data. The relatively low precision (1.22 m) from the IM is due to the potential error from the a-priori digital elevation model (DEM). The DM has the lowest precision (2.66 m). Another experiment over rough topography in West Antarctica is carried out for comparison, especially between the RM1 (precision of 15.27 m) and RM2 (precision of 16.25 m). In general, the RM is recommended for the SE elimination among the three methods. Moreover, the RM2 is firstly considered over smooth topography due to the superior performance in bias and precision, while the RM1 is more suggested over the rough topography because of the slightly smaller bias and better precision. The IM relies much on the accuracy of the a-prior DEM and is not usually recommended, because of the strict requirement in the sampling time between the radar altimetry data and the a-priori DEM to avoid any surface change over time.

We propose a lightweight radar that autonomously measures snow depth over sea ice from an unmanned aerial vehicle (UAV). Development of this snow radar and its integration with an octocopter UAV is presented. Field trials of the UAV-mounted snow radar, conducted in Antarctica during the summer season of 2017/2018, are also described. The radar allows measurements of snow depths on sea ice between 10 and 100 cm. Additional reflections due to internal layers within the snow are evident at a few measurement points. The snow radar is evaluated for various flight parameters: stationary; flying at speeds between 1 and 3 m/s, and at heights from 5 to 15 m. Evaluation of snow-depth results indicates that a depth accuracy of ±3.2 cm is achieved with stationary measurements, and of ±9.1 cm with measurements at the various flight speeds.

This paper describes an innovative method for processing nadir altimeter data acquired in Synthetic Aperture Radar (SAR) mode, enhancing the system performances over open ocean. Similarly to the current SAR data processing scheme, the so-called LR-RMC (Low Resolution with Range Migration Correction) method, originally designed by Phalippou and Demeester (2011), includes Doppler beam forming, Doppler shift correction and range correction. In LR-RMC, however, an alternative and less complex averaging (stacking) operation is used so that all the Doppler beams produced in a radar cycle (4 bursts of 64 beams for the open-burst Sentinel-3-mode altimeter) are incoherently combined to form a multi-beam echo. In that manner, contrarily to the narrow-band SAR technique, the LR-RMC processing enlarges the effective footprint to average out the effects of surface waves and particularly those from small sub-mesoscale structures (

Assessing landslide activity at large scales has historically been a challenging problem. Here, we present a different approach on radar coherence and normalized difference vegetation index (NDVI) analyses – metrics that are typically used to map landslides post-failure – and leverage a time series analysis to characterize the pre-failure activity of the Mud Creek landslide in California. Our method computes the ratio of mean interferometric coherence or NDVI on the unstable slope relative to that of the surrounding hillslope. This approach has the advantage that it eliminates the negative impacts of long temporal baselines that can interfere with the analysis of interferometric synthetic aperture (InSAR) data, as well as interferences from atmospheric and environmental factors. We show that the coherence ratio of the Mud Creek landslide dropped by 50 % when the slide began to accelerate 5 months prior to its catastrophic failure in 2017. Coincidentally, the NDVI ratio began a near-linear decline. A similar behavior is visible during an earlier acceleration of the landslide in 2016. This suggests that radar coherence and NDVI ratios may be useful for assessing landslide activity. Our study demonstrates that data from the ascending track provide the more reliable coherence ratios, despite being poorly suited to measure the slope's precursory deformation. Combined, these insights suggest that this type of analysis may complement traditional InSAR analysis in useful ways and provide an opportunity to assess landslide activity at regional scales.

This study investigates the impact of the October 2016 flash floods in urban areas located in selected catchments along the Nile Valley and Red Sea regions for the assessment and the proposal of feasible mitigation measures. Optical and radar satellite images acquired before and shortly after flash flood events were analyzed to detect and map the active contributing channels, the interfering urban areas, the response of natural depressions and storage areas, and the conveying channels to the flash floods. The quantitative estimates of October 2016 flash floods parameters including total rainfall depths, the active channel cross-sectional areas, and the spatial extent of storage areas of the accumulated flood water were measured. Various hydrological parameters were extracted using the Digital Elevation Model (DEM) for the estimation of water volumes of largely inundated depressions. It was determined that the October 2016 flash floods were devastating in the Ras Gharib area; however, these floods were properly managed at both the New El Minya and New Qena urban areas. In contrast, the flash floods at the Al-Saaf and Atfih urban area require urgent mitigation measures given that wastewater depressions and sanitary facilities are prevalent in the downstream reaches of the studied drainage basins.