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Airborne ash generated by explosive volcanic eruptions presents a significant danger to aviation. Accurate modelling and predictions of the dispersal of hazardous ash into the atmosphere are currently hampered by uncertainties in the ‘source term’ parameters associated with the initial eruption plume, specifically the amount and size of ash particles released into the atmosphere. Ground based radar offers the means to remotely measure ash reflectivity, however estimation of source term parameters from reflectivity measured by single frequency radar is limited by ambiguity between the contribution of particle size distribution (PSD) and ash concentration in the plume. This means that one of these parameters must be assumed rather than measured directly, leading to uncertainties in forecasting eruption hazards. We report on R4AsH, a close range FMCW radar designed to resolve this ambiguity by simultaneous characterization of falling volcanic ash in a laboratory-controlled environment at three different frequencies: 10, 35 and 94 GHz. The R4AsH design uses a single DDS based chirp generator as a common source, multiplied and upconverted to feed three sets of transmit-receive horn antennas directed at a common target volume such that measurements will give spatially and temporally coincident measurements of falling ash. In addition, there will be independent measurement of the PSD using optical imaging and logging of the landing particle mass to calibrate results and inform analysis. The aim of R4AsH is to develop a triplefrequency inversion algorithm to enable simultaneous retrieval of PSD and ash concentration from radar data suitable for future volcano monitoring systems. Publisher PDF

Global climate warming is rapidly reducing Arctic sea ice volume and extent. The associated perennial sea ice loss has economic and global security implications associated with Arctic Ocean navigability, since sea ice cover dictates whether an Arctic route is open to shipping. Thus, understanding changes in sea ice thickness, concentration and drift is essential for operation planning and routing. However, changes in sea ice cover on scales up to a few days and kilometers are challenging to detect and forecast; current sea ice models may not capture quickly-changing conditions on short timescales needed for navigation. Assimilating these predictive models requires frequent, high-resolution morphological information about the pack, which is operationally difficult. We suggest an approach to mitigate this challenge by using machine learning (ML) to interpret satellite-based synthetic aperture radar (SAR) imagery. In this study, we derive ML models for the analysis of SAR data to improve short-term local sea ice monitoring at high spatial resolutions, enabling more accurate analysis of Arctic navigability. We develop an algorithm/classifier that can analyze Sentinel-1 SAR imagery with the potential to inform operational sea ice forecasting models. We focus on detecting two sea ice features of interest to Arctic navigability: ridges and leads (fractures in the ice shelf). These can be considered local extremes in terms of ice thickness, a crucial parameter for navigation. We build models to detect these ice features using machine learning techniques. Both our ridge and lead detection models perform as well as, if not better than, state-of-the- art methods. These models demonstrate Sentinel-1's ability to capture sea ice conditions, suggesting the potential for Sentinel-1 global coverage imagery to inform sea ice forecasting models.

Remote sensing of wind waves propagating in the areas covered by ice at initial stages of its formation in the marginal ice zone using radar is an urging problem for mapping ice boundaries and their dynamics. Another aspect of the problem is to investigate possibilities of discrimination between the marginal ice zone and oil spills in radar imagery. This study is focused on modeling the damping of surface waves due to ice floes in laboratory and field experiment. Laboratory experiments were carried out is a container filled with pure water in order to exclude the effect of surfactant films on wave damping. The container was mounted on a vibration table, so that surface gravity-capillary waves (GCW) could be parametrically generated in the container when the amplitude of the vibrations exceeded some threshold level. The wave damping coefficient could be retrieved when measuring the threshold. The floes in experiment were modeled using thin plastic pieces of two different sizes, the relative square of the “floe” coverage of the water surface was controlled in experiment. The dependences of the damping coefficient at different relations between the surface wavelength and the floe dimensions as functions of the floe coverage area were obtained. It is obtained that the damping of gravity-capillary waves in the presence of floes comparable in size with GCW can be one to two orders of magnitude greater that the wave damping due to inextensible film. Preliminary field experiments have been conducted on the Gorky Water Reservoir using a research catamaran vessel of the Institute of Applied Physics. Plywood pieces with sizes several times smaller that the studied surface wavelengths were used as imitators of ice floes and were deployed in between the catamaran hulls. Surface waves propagating between the halls were generated mechanically by a vertically oscillating motor boat. The amplitude of attenuating surface waves due to the “plywood floes” was measured with wire gauges mounted at the bow and the stern of the catamaran. The damping distance due to ice floes obtained in the field experiment was estimated as about 10 wavelengths thus indicating that that wave suppression due to the floes was essentially stronger than the viscous wave damping for clean or contaminated water surface. Wave damping observed both in the laboratory and field experiments can be comparable with the wave damping due to crude oil/oil emulsion films, so the problem of discrimination between, e.g. grease ice and oil spills in radar imagery can be nontrivial.

Radar sounders mounted on airborne platforms have acquired data of the subsurface of the Earth's icy areas over the last decades. These data, called radargrams, contain information on the dielectric discontinuities in the ice-sheets, and thus on the buried geological structures and the related processes. Conventionally, these structures have been characterized and mapped by visually inspecting the radargrams. However, visual inspection is subjective and time-consuming and can lead to misinterpretations. Recently, state-of-the-art automatic techniques are proposed to map the position of the bedrock, the ice layering, and the noise in the radargram. However, there are no automatic techniques for mapping the basal refreezing, which is an important ice target that controls the rate of sea-ward ow of the ice-sheets. This paper proposes an automatic method to map the refreezing ice in radargrams. We model the refreezing ice considering its geophysical and radiometric properties. Then, we design a set of features considering this model to perform a classification of the radargrams into four classes, i.e., ice layering, echo-free zone (EFZ) and thermal noise, bedrock, and the refreezing ice. We applied the proposed method to radargrams acquired in the north Greenland by Multichannel Coherent Radar Depth Sounder (MCoRDS3), a radar sounder designed by the Center for Remote Sensing of Ice Sheets (CReSIS). The results indicate a good overall accuracy. The accuracy of refreezing ice is high, while that of the other classes is comparable with the state-of-the-art techniques. The results indicate the effectiveness of the proposed features in mapping the refreezing ice.

Ground Based Interferometric Radar (GBInRad) is a class of terrestrial remote sensing imaging system, based on microwave interferometric techniques. The principal application of GBInRad system is deformation monitoring, since respect to other techniques they can provide remote sensing, high sensitivity to small deformations, long range of measurements, imaging capability and fast scan time. The main limitation of standard GBInRad system is their capability of detecting movements only along the Line of Sight (LoS) of the sensor, although actual targets may show deformations in any direction of space; this represents an important limitation with respect to other techniques able to estimate the full 3D deformation vector. If the displacement direction is not known a priori, combining together LoS displacement measured from different spatial positions, it is possible to reconstruct the actual 3D displacement vector of monitored targets. In this paper are introduced and analysed the various aspect of the displacement vector measurement with multiple GBInRad system that work both in a monostatic and in a bistatic configuration. In the monostatic configuration every system transmits and receives the signal independently from the others; this approach requires multiple GBInRad system deployed to monitoring the same scenario and therefore its main limitations lie in the costs, power consumption and maintenance. A possible cost-effective evolution of the monostatic configuration is to exploit GBInRad system in a multiple bistatic configuration; a multiple bistatic Radar is a system in which a transmitter operates together with multiple receivers located in different positions in space. In this paper, the deformation vector measurement by means of bistatic GBInRad is proposed.

The use of ground return signals is investigated for checks on the calibration of power measurements of a polarimetric C-band radar. To this aim, a peculiar permanent single scatterer (PSS) consisting of a big metallic roof with a periodic mesh grid structure and having a hemisphere-like shape is considered. The latter is positioned in the near-field region of the weather radar and its use, as a reference calibrator, shows fairly good results in terms of reflectivity and differential reflectivity monitoring. In addition, the use of PSS indirectly allows to check for the radar antenna de-pointing which is another issue usually underestimated when dealing with weather radars. Because of the periodic structure of the considered PSS, simulations of its electromagnetic behavior were relatively easy to perform. To this goal, we used an electromagnetic Computer-Aided-Design (CAD) with an ad-hoc numerical implementation of a full-wave solution to model our PSS in terms of reflectivity and differential reflectivity factor. Comparison of model results and experimental measurements are then shown in this work. Our preliminary investigation can pave the way for future studies aiming at characterizing ground-clutter returns in a more accurate way for radar calibration purposes.

In the context of rising the frequency of natural disasters and catastrophes humanity has to develop methods and tools to ensure safe living conditions. Effectiveness of preventive measures greatly depends on quality and lead time of the forecast of disastrous natural phenomena, which is based on the amount of knowledge about natural hazards, their causes, manifestations, and impact. To prevent them it is necessary to get complete and comprehensive information about the extent of spread and severity of natural processes that can act within a defined territory. For these purposes the High Mountain Geophysical Institute developed the automated workplace for mining, analysis and archiving of radar, satellite, lightning sensors information and terrestrial (automatic weather station) weather data. The combination and aggregation of data from different sources of meteorological data provides a more informativity of the system. Satellite data shows the global cloud region in visible and infrared ranges, but have an uncertainty in terms of weather events and large time interval between the two periods of measurements, which complicates the use of this information for very short range forecasts of weather phenomena. Radar and lightning sensors data provide the detection of weather phenomena and their localization on the background of the global pattern of cloudiness in the region and have a low period measurement of atmospheric phenomena (hail, thunderstorms, showers, squalls, tornadoes). The authors have developed the improved algorithms for recognition of dangerous weather phenomena, based on the complex analysis of incoming information using the mathematical apparatus of pattern recognition.

Time series analysis of GPS (Global Positioning Systems) and InSAR (Interferometric Synthetic Aperture Radar) data are important tools for Earth’s surface deformation assessment, which can result from a wide range of geological phenomena like as earthquakes, landslides or ground water level changes. The aim of this paper was to identify several types of earthquake precursors that might be observed from geospatial data in Vrancea seismogenic region in Romania. Continuous GPS Romanian network stations and few field campaigns data recorded between 2005-2012 years revealed a displacement of about 5 or 6 millimeters per year in horizontal direction relative motion, and a few millimeters per year in vertical direction. In order to assess possible deformations due to earthquakes and respectively for possible slow deformations, have been used also time series Sentinel 1 satellite data available for Vrancea zone during October 2014 till October 2016 to generate two types of interferograms (short-term and medium- term). During investigated period were not recorded medium or strong earthquakes, so interferograms over test area revealed small displacements on vertical direction (subsidence or uplifts) of 5-10 millimeters per year. Based on GPS continuous network data and satellite Sentinel 1 results, different possible tectonic scenarios were developed. The localization of horizontal and vertical motions, fault slip, and surface deformation of the continental blocks provides new information, in support of different geodynamic models for Vrancea tectonic active region in Romania and Europe.

The amount of radar sounder data, which are used to analyze the subsurface of icy environments (e.g., Poles of Earth and Mars), is dramatically increasing from both airborne campaigns at the ice sheets and satellite missions on other planetary bodies. However, the main approach to the investigation of such data is by visual interpretation, which is subjective and time consuming. Moreover, the few available automatic techniques have been developed for analyzing highly reflective subsurface targets, e.g., ice layers, basal interface. Besides the high reflective targets, glaciologists have also shown great interest in the analysis of non-reflective targets, such as the echo-free zone in ice sheets, and the reflective free zone in the subsurface of the South Pole of Mars. However, in the literature, there is no dedicated automatic technique for the analysis of non-reflective targets. To address this limitation, we propose an automatic classification technique for the identification of non-reflective targets in radar sounder data. The method is made up of two steps, i.e., i) feature extraction, which is the core of the method, and ii) automatic classification of subsurface targets. We initially prove that the commonly employed features for the analysis of the radar signal (e.g., statistical and texture based features) are ineffective for the identification of non-reflective targets. Thus, for feature extraction, we propose to exploit structural information based on the morphological closing profile. We show the effectiveness of such features in discriminating of non-reflective target from the other ice subsurface targets. In the second step, a random forest classifier is used to perform the automatic classification. Our experimental results, conducted using two data sets from Central Antarctica and South Pole of Mars, point out the effectiveness of the proposed technique for the accurate identification of non-reflective targets.

Subglacial lakes decouple the ice sheet from the underlying bedrock, thus facilitating the sliding of the ice masses towards the borders of the continents, consequently raising the sea level. This motivated increasing attention in the detection of subglacial lakes. So far, about 70% of the total number of subglacial lakes in Antarctica have been detected by analysing radargrams acquired by radar sounder (RS) instruments. Although the amount of radargrams is expected to drastically increase, from both airborne and possible future Earth observation RS missions, currently the main approach to the detection of subglacial lakes in radargrams is by visual interpretation. This approach is subjective and extremely time consuming, thus difficult to apply to a large amount of radargrams. In order to address the limitations of the visual interpretation and to assist glaciologists in better understanding the relationship between the subglacial environment and the climate system, in this paper, we propose a technique for the automatic detection of subglacial lakes. The main contribution of the proposed technique is the extraction of features for discriminating between lake and non-lake basal interfaces. In particular, we propose the extraction of features that locally capture the topography of the basal interface, the shape and the correlation of the basal waveforms. Then, the extracted features are given as input to a supervised binary classifier based on Support Vector Machine to perform the automatic subglacial lake detection. The effectiveness of the proposed method is proven both quantitatively and qualitatively by applying it to a large dataset acquired in East Antarctica by the MultiChannel Coherent Radar Depth Sounder.

Most existing radar algorithms are developed under the assumption that the environment, data clutter, is known and stationary. However, in practice, the characteristics of clutter can vary enormously in time depending on the operational scenarios. If unaccounted for, these nonstationary variabilities may drastically hinder the radar performance. It is essential that the radar systems dynamically detect changes in the environment, and adapt to these changes by learning the new statistical characteristics of the environment. In this paper, we employ sparse recovery for clutter identification, specifically we identify the statistical profile the clutter follows. We use Monte Carlo simulations to simulate and test clutter data coming from various distributions.

The paper presents a synthetic information on a UAV-based radar terrain imaging system, its purpose, structure and working principle as well as terrain images obtained from flight experiments. A SAR technology demonstrator has been built as a result of a research project conducted by the Military University of Technology and WB Electronics S.A. under the name WATSAR. The developed system allows to obtain high resolution radar images, both in on-line and off-line modes, independently of the light conditions over the observed area. The software developed for the system allows to determine geographic coordinates of the imaged objects with high accuracy. Four LFM-CW radar sensors were built during the project: two for S band and two for Ku band, working with different signal bandwidths. Acquired signals were processed with the TDC algorithm, which allowed for a number of analyses in order to evaluate the performance of the system. The impact of the navigational corrections on a SAR image quality was assessed as well. The research methodology of the in-flight experiments of the system is presented in the paper. The projects results show that the developed system may be implemented as an aid to tactical C4ISR systems.

The Earth Clouds, Aerosols and Radiation Explorer (EarthCARE) mission is joint mission between Europe and Japan for the launch year of 2018. Mission objective is to improve scientific understanding of cloud-aerosol-radiation interactions that is one of the biggest uncertain factors for numerical climate and weather predictions. The EarthCARE spacecraft equips four instruments such as an ultra violet lidar (ATLID), a cloud profiling radar (CPR), a broadband radiometer (BBR), and a multi-spectral imager (MSI) and perform complete synergy observation to observe aerosols, clouds and their interactions simultaneously from the orbit. Japan Aerospace Exploration Agency (JAXA) is responsible for development of the CPR in this EarthCARE mission and the CPR will be the first space-borne W-band Doppler radar. The CPR is defined with minimum radar sensitivity of -35dBz (6dB better than current space-borne cloud radar, i.e. CloudSat, NASA), radiometric accuracy of 2.7 dB, and Doppler velocity measurement accuracy of less than 1.3 m/s. These specifications require highly accurate pointing technique in orbit and high power source with large antenna dish. JAXA and National Institute of Information and Communications Technology (NICT) have been jointly developed this CPR to meet these strict requirements so far and then achieved the development such as new CFRP flex-core structure, long life extended interaction klystron, low loss quasi optical feed technique, and so on. Through these development successes, CPR development phase has been progressed to critical design phase. In addition, new ground calibration technique is also being progressed for launch of EarthCARE/CPR. The unique feature of EarthCARE CPR is vertical Doppler velocity measurement capability. Vertical Doppler velocity measurement is very attractive function from the science point of view, because vertical motions of cloud particles are related with cloud microphysics and dynamics. However, from engineering point of view, Doppler measurement from satellite is quite challenging Technology. In order to maintain and ensure the CPR performance, several types of calibration data will be obtained by CPR. Overall performance of CPR is checked by Active Radar Calibrator (ARC) equipped on the ground (CPR in External Calibration mode). ARC is used to check the CPR transmitter performance (ARC in receiver mode) and receiver performance (ARC in transmitter mode) as well as overall performance (ARC in transponder mode with delay to avoid the contamination with ground echo). In Japan, the instrument industrial Critical Design Review of the CPR was completed in 2013 and it was also complemented by an Interface and Mission aspects CPR CDR, involving ESA and the EarthCARE Prime, that was completed successfully in 2015. The CPR Proto-Flight Model is currently being tested with almost completion of Proto-Flight Model integration. After handed-over to ESA planned for the beginning of 2017, the CPR will be installed onto the EarthCARE satellite with the other instruments. After that the CPR will be tested, transported to Guiana Space Center in Kourou, French Guiana and launched by a Soyuz launcher in 2018. This presentation will show the summary of the latest CPR design and CPR PFM testing status.

The millimeter-wave cloud radar can provide a large number of fine and reliable information for the inversion of cloud macro and micro parameters. A key link of using the millimeter-wave cloud radar to detect the cloud is that the radar must be calibrated. Due to the precision components and severe environment of millimeter-wave cloud radar, subtle changes may take place in the operation process of cloud radar, unless the cloud radar is calibrated regularly. Although the calibration system inside the cloud radar can track and monitor the main working parameters and correct the detection results, it fails to consider the characteristics of the antenna and the mutual influence among different components of cloud radar. Therefore, the external calibration for cloud radar system is very important. Combined with the actual situation of cloud radar under domestic onboard platform, this paper builds a complete external calibration technique process of cloud radar based on the calm sea, providing the theoretical support for the external calibration experiments of the airborne and even satellite-borne millimeter-wave cloud radar developed by our country.

Most surveillance systems combine camera sensors with other detection sensors that trigger an alert to a human operator when an object is detected. The detection sensors typically require careful installation and configuration for each application and there is a significant burden on the operator to react to each alert by viewing camera video feeds. A demonstration system known as Sensing for Asset Protection with Integrated Electronic Networked Technology (SAPIENT) has been developed to address these issues using Autonomous Sensor Modules (ASM) and a central High Level Decision Making Module (HLDMM) that can fuse the detections from multiple sensors. This paper describes the 24 GHz radar based ASM, which provides an all-weather, low power and license exempt solution to the problem of wide area surveillance. The radar module autonomously configures itself in response to tasks provided by the HLDMM, steering the transmit beam and setting range resolution and power levels for optimum performance. The results show the detection and classification performance for pedestrians and vehicles in an area of interest, which can be modified by the HLDMM without physical adjustment. The module uses range-Doppler processing for reliable detection of moving objects and combines Radar Cross Section and micro-Doppler characteristics for object classification. Objects are classified as pedestrian or vehicle, with vehicle sub classes based on size. Detections are reported only if the object is detected in a task coverage area and it is classified as an object of interest. The system was shown in a perimeter protection scenario using multiple radar ASMs, laser scanners, thermal cameras and visible band cameras. This combination of sensors enabled the HLDMM to generate reliable alerts with improved discrimination of objects and behaviours of interest.

The multibeam monopulse radar for Airborne Based Sense and Avoid (ABSAA) system concept is the next step in the development of passive monopulse direction finder proposed by Stephen E. Lipsky in the 80s. In the proposed system the multibeam monopulse radar with an array of directional antennas is positioned on a small aircaraft or Unmanned Aircraft System (UAS). Radar signals are simultaneously transmitted and received by multiple angle shifted directional antennas with overlapping antenna patterns and the entire sky, 360° for both horizontal and vertical coverage. Digitizing of amplitude and phase of signals in separate directional antennas relative to reference signals provides high-accuracy high-resolution range and azimuth measurement and allows to record real time amplitude and phase of reflected from non-cooperative aircraft signals. High resolution range and azimuth measurement provides minimal tracking errors in both position and velocity of non-cooperative aircraft and determined by sampling frequency of the digitizer. High speed sampling with high-accuracy processor clock provides high resolution phase/time domain measurement even for directional antennas with wide Field of View (FOV). Fourier transform (frequency domain processing) of received radar signals provides signatures and dramatically increases probability of detection for non-cooperative aircraft. Steering of transmitting power and integration, correlation period of received reflected signals for separate antennas (directions) allows dramatically decreased ground clutter for low altitude flights. An open architecture, modular construction allows the combination of a radar sensor with Automatic Dependent Surveillance – Broadcast (ADS-B), electro-optic, acoustic sensors.

This paper evaluates the rain effects on Ku-band radar backscatter at low incidence angles. The data used consisted of the sea surface backscatter and averaged rain rates from Tropical Rainfall Mapping Mission precipitation radar (TRMM PR) measurements and the collocated 10-m height numerical prediction wind speeds from the European Centre for Medium-Range Weather Forecasts (ECMWF). The wind-induced backscatter was estimated by the Ku-band low incidence backscatter model (KuLMOD) and possible bias due to different wind speed inputs was considered. The rain effect was analysed by comparing the TRMM PR-measured surface backscatter for the rain-affected sea surface with the collocated wind-induced backscatter. We found that the surface backscatter decreases with increases in the averaged rain rate. The rain-induced backscatter was clearly dependent of the wind speed and was slightly dependent of the incidence angle. Results show that it is necessary to develop a wind and rain backscatter model instead of single wind backscatter model.

Investigation of the Doppler shift of radar return from the sea surface is very important for better understanding of capabilities of exploitation of microwave radar for measuring velocities of marine currents. Here new field experiments carried out from a Platform on the Black Sea with a coherent X-band scatterometer, and a Doppler multifrequency (X- /C-/S-band) dual-polarized radar recently designed at IAP RAS are discussed. It is shown that the radar return contains both Bragg (polarized) and non polarized scattering components, presumably giving different contributions to radar Doppler shifts. Radar Doppler shifts were estimated using two different definitions as a) a frequency of the “centre of gravity” of an instantaneous radar return spectrum (ASIS) averaged over periods of dominant wind waves and b) the “centre of gravity” of the averaged over dominant wave periods spectrum (SAS). The ASIS and SAS values for both VV and HH-polarizations are shown to be different due to effects of radar backscatter modulation by dominant (long) wind waves. The radar Modulation Transfer Function (MTF) has been analyzed from experimental data and difference between SAS- and ASIS-values has been satisfactory explained using the measured MTF-values. It is obtained that experimental values of ASIS can be satisfactory described by the Bragg model despite the significant contribution of NP component to the radar backscatter. A physical explanation of the effect is given.

The new European Observatory radar data of polar orbiting satellite system Sentinel-1 provide a continuous and systematic data acquisition, enabling flood events monitoring and mapping. The study area is the basin of Sperchios River in Fthiotida Prefecture, Central Greece, having an increased ecological, environmental and socio-economic interest. The catchment area and especially the river delta, faces several problems and threats caused by anthropogenic activities and natural processes. The geomorphology of Sperchios catchment area and the drainage network formation provoke the creation of floods. A large flash flood event took place in late January early February 2015 following an intense and heavy rainfall that occurred in the area. Two space born radar images, obtained from Sentinel-1 covering the same area, one before and another one during the flood event, were processed. Two different methods were utilized so as to produce flood hazard maps, which demonstrate the inundated areas. The results of the two methods were similar and the flooded area was detected and delineated ideally.

Radar Sounders are valuable instruments for subsurface investigation. They are widely employed for the study of planetary bodies around the solar system. Due to their wide antenna beam pattern, off-nadir surface reflections (i.e. clutter) of the transmitted signal can compete with echoes coming from the subsurface thus masking them. Different strategies have been adopted for clutter mitigation. However, none of them proved to be the final solution for this specific problem. Bats are very well known for their ability in discriminating between a prey and unwanted clutter (e.g. foliage) by effectively employing their sonar. According to recent studies, big brown bats can discriminate clutter by transmitting two different carrier frequencies. Most interestingly, there are many striking analogies between the characteristics of the bat sonar and the one of a radar sounder. Among the most important ones, they share the same nadir acquisition geometry and transmitted signal type (i.e. linear frequency modulation). In this paper, we explore the feasibility of exploiting frequency diversity for the purpose of clutter discrimination in radar sounding by mimicking unique bats signal processing strategies. Accordingly, we propose a frequency diversity clutter reduction method based on specific mathematical conditions that, if verified, allow the disambiguation between the clutter and the subsurface signal to be performed. These analytic conditions depend on factors such as difference in central carrier frequencies, surface roughness and subsurface material properties. The method performance has been evaluated by different simulations of meaningful acquisition scenarios which confirm its clutter reduction effectiveness.

The Dual-frequency Precipitation Radar (DPR) on the Global Precipitation Measurement (GPM) core satellite was developed by Japan Aerospace Exploration Agency (JAXA) and National Institute of Information and Communications Technology (NICT). The objective of the GPM mission is to observe global precipitation more frequently and accurately. The GPM core satellite is a joint product of National Aeronautics and Space Administration (NASA), JAXA and NICT. NASA developed the satellite bus and the GPM Microwave Imager (GMI), and JAXA and NICT developed the DPR. The inclination of the GPM core satellite is 65 degrees, and the nominal flight altitude is 407 km. The non-sunsynchronous circular orbit is necessary for measuring the diurnal change of rainfall. The DPR consists of two radars, which are Ku-band precipitation radar (KuPR) and Ka-band precipitation radar (KaPR). GPM core observatory was successfully launched by H2A launch vehicle on Feb. 28, 2014. DPR orbital check out was completed in May 2014. DPR products were released to the public on Sep. 2, 2014 and Normal Observation Operation period was started. JAXA is continuing DPR trend monitoring, calibration and validation operations to confirm that DPR keeps its function and performance on orbit. The results of DPR trend monitoring, calibration and validation show that DPR kept its function and performance on orbit during the 3 years and 2 months prime mission period. The DPR Prime mission period was completed in May 2017. The version 5 GPM products were released to the public in 2017. JAXA confirmed that GPM/DPR total system performance and the GPM version 5 products achieved the success criteria and the performance indicators that were defined for the JAXA GPM/DPR mission.

This work has been focused on the integration of microwave data coming from different sensors (SMAP, Sentinel-1, AMSR2), in order to obtain an improved estimation of hydrological parameters and in particular of the Soil Moisture (SMC). The failure of radar sensor in SMAP satellite induced to look for other available microwave frequencies, both from active (e.g. Sentinel-1, C band) and passive sensors (e.g. AMSR2, from C to Ka bands). The contribution of higher frequencies, which are more suitable for monitoring vegetation and other surface features, has been evaluated for compensating the vegetation and roughness effects on the SM retrieval accuracy. In particular, the contribution of C band SAR from Sentinel-1 and of the multi-frequency AMSR2 radiometer (C to Ka bands) have been considered. Moreover, a disaggregation technique, based on the Smoothing filter based intensity modulation (SFIM), enabled achieving a more suitable ground resolution for comparing SAR and radiometric data. Disaggregated microwave data were used as inputs of a retrieval algorithm based on Artificial Neural Network (ANN), able to exploit the synergy between active and passive acquisitions. The algorithm was defined basing on the data available from the SMEX02 and SMAPVEX12 campaigns, and on data simulated by electromagnetic forward models. The algorithm validation on these datasets returned encouraging results, while the disaggregation technique enabled adapting the algorithm to work with Sentinel, SMAP and AMSR2 synergic acquisitions

With the increasingly frequent human activities in space, number of dead satellites and space debris has increased dramatically, bring greater risks to the available spacecraft, however, the current widespread use of measuring equipment between space target has a lot of problems, such as high development costs or the limited conditions of use. To solve this problem, use radar multi-target measure error information to the space, and combining the relationship between target and the radar station point of view, building horizontal distance decoding model. By adopting improved signal quantization digit, timing synchronization and outliers processing method, improve the measurement precision, satisfies the requirement of multi-objective near distance measurements, and the using efficiency is analyzed. By conducting the validation test, test the feasibility and effectiveness of the proposed methods.

We present a general method for retrieving time-series of three component surface velocity field vector given a set of continuous synthetic aperture radar (SAR) acquisitions collected from multiple geometries. Our algorithm extends the single-line-of-sight mathematical framework developed for time-series analysis using interferometric SAR (InSAR) to three spatial dimensions. The inversion is driven by a design matrix corresponding to a dictionary of displacement functions parameterized in time. The resulting model minimizes a cost function using a non-regularized least-squares method. We applied our method to Rutford ice stream (RIS), West Antarctica, using a set of 101 multi-track multi-angle COSMO-SkyMed displacement maps generating azimuth and range pixel offsets.

The Dual-frequency Precipitation Radar (DPR) on the Global Precipitation Measurement (GPM) core satellite was developed by Japan Aerospace Exploration Agency (JAXA) and National Institute of Information and Communications Technology (NICT). The objective of the GPM mission is to observe global precipitation more frequently and accurately. The GPM core satellite is a joint product of National Aeronautics and Space Administration (NASA), JAXA and NICT. NASA developed the satellite bus and the GPM Microwave Imager (GMI), and JAXA and NICT developed the DPR. The inclination of the GPM core satellite is 65 degrees, and the nominal flight altitude is 407 km. The non-sunsynchronous circular orbit is necessary for measuring the diurnal change of rainfall. The DPR consists of two radars, which are Ku-band precipitation radar (KuPR) and Ka-band precipitation radar (KaPR). GPM core observatory was successfully launched by H2A launch vehicle on Feb. 28, 2014. DPR keeps its performances on orbit after launch. DPR products were released to the public on Sep. 2, 2014. JAXA is continuing DPR trend monitoring, calibration and validation operations to confirm that DPR keeps its function and performance on orbit. JAXA have started to provide new version (Version 4) of GPM standard products on March 3, 2016. Various improvements of the DPR algorithm were implemented in the Version 4 product. Moreover, the latent heat product based on the Spectral Latent Heating (SLH) algorithm is available since Version 4 product. Current orbital operation status of the GPM/DPR and highlights of the Version 4 product are reported.

Link to publishers version: 10.1117/12.2241266 Evolution of the damping ratio for Bragg wavenumbers in the range 32-43 rad/m is evaluated for oil slicks of different composition released in the open ocean and allowed to develop naturally. The study uses quad-polarimetric L-band airborne synthetic aperture radar data acquired over three mineral oil emulsion releases of different, known oil-to-water ratio, and a near-coincident release of 2-ethylhexyl oleate that served as a biogenic look-alike. The experiment occurred during the 2015 Norwegian oil-on-water exercise in the North Sea during a period of relatively high winds (~12 m/s). NASA’s Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) was used to repeatedly image the slicks over a period of eight hours, capturing the slicks’ early development and providing a time series from which to track the evolution of the slicks’ size, position, and radiometric characteristics. Particular emphasis is given in this analysis to identification of zones of higher damping ratio within the slicks (zoning) as potential indicators of thicker oil, and to comparison of the evolution of emulsion and plant oil damping ratios. It was found that all mineral oil slicks initially exhibited zoning apparent in VV, HH, and HV intensities, and that the areas of higher damping ratio persisted the longest for the highest oil content emulsion (80% oil by volume). In contrast, zoning was not unambiguously evident for plant oil at any time from 44 minutes to 8.5 hours after release. © (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

This paper describes the impact of ground target motion in Synthetic Aperture Radar (SAR) and video SAR mode imagery. The observations provide an approach for optimizing algorithms that detect moving targets by using only the magnitude of a SAR image. A slowly moving target at a constant velocity in the along-track direction or accelerating in the cross-track direction often generates a ridge of intensity that is distinguishable from the background clutter. The direction and location of a detected ridge provide information about the motion of the associated target. The ridge can be approximated as a linear feature and detected using the Hough transform. This approach acts as a complement to detecting the radar shadow of a moving target, improving detection probability. The method is robust enough to discriminate between a ridge associated with a moving target and false alarms due to vegetation, boulders, or stationary manmade objects. Simulated and flight test data collected by General Atomics Aeronautical Systems, Inc. (GA-ASI) validate the method.

Within the framework of the project, substances are indicators. It is these substances that are the main constituents of a watery suspension found on the surface of Mars. According to the conducted researches, the spectral region for the study of indicator substances was chosen. The method of remote sensing of the surface and the lidar construction scheme are chosen. The results of the preliminary calculation of the system are presented.

In the problem of light scattering by ice crystals of cirrus clouds, a new quantity characterizing microphysics of the clouds is determined. This quantity appears at simultaneous use of lidar and radar signals reflected from the same cloud. The quantity is the ratio of the backscattering cross section averaged over a statistical crystal ensemble to the averaged crystal volume. It is shown that this new characteristics can be effective if it is used together with the color ratio for retrieving microphysics of quasi-horizontally oriented crystals

Due to the unique imaging approach for ground-based radar, identification and classification in observation area is very difficult. In order to improve the accuracy of the calculation and application combine with other data resource. it is necessary to implement data matching of radar images and 3D laser point cloud. First, the 3D cloud should to be transformed to orthographic maps, and then the horizontal rotation and orbit attitude angle parameters would be estimated for similarity transformation according to the characteristics such as common points and lines. Finally, the same reference point of the ground-based SAR data and cloud data is employed to accomplished in a two-dimensional coordinate system (called local common coordinate system).

A tool for the entire Indian weather radar network using the static composite QI (Quality Index) map is generated. Various customized modules are used for this generation of the radar mosaic. The characterization of quality of DWR (Doppler weather Radar) data in terms of their QI is essential for assimilating the data into NWP (Numerical Weather Prediction) models. The static QI maps give a quick overview about the inherent errors in the DWR data. Quality control algorithms are applied for the generation of composite QI. The near real time access to the DWR data at NCMRWF enables the generation of an accumulated gridded radar rainfall product. This gridded rainfall map is useful for generating products like high resolution rainfall product, QPE (quantitative precipitation estimate) and for other applications. Results of some case studies shall be presented.

Every vessel moving in the sea, imprints a perturbation on the wave structure of the sea and forms a so-called wake. These wakes can be used in the detection of a target and can also help in identifying its characteristics. Several studies concentrated on detection of a target wake by making use of either radar or infrared sensors. We model the infrared and radar signature of the wake and sea surface background and investigate the synergy between the two bands. The primary goal of this work is to make a comparative study between the two bands in order to be able to discriminate which sensor gives a more reliable detection in which scenario.

WWe propose to apply the compressive sensing technique to the design of satellite radar altimeter for increasing the sampling time window (STW) while keeping the same data rate so as to enhance the tracking robustness of an altimeter. A satellite radar altimeter can measure the range between the satellite platform where it is aboard and the averaged sea surface with centimeter level accuracy. The rising slope of the received waveform by altimeter contains important information about the sea surface, e.g. the larger the slope of the waveform, means the smoother the sea surface. Besides, the half-power point of the slope refers to the range information. For satellite altimeter, due to the rising slope just occupies fewer range bins compared with the whole range bins illuminated by the long pulse signal, i.e. the signal is sparse in this sense, thus compressive sensing technique is applicable. Altimeter echoes are simulated and the waveforms are constructed by using the traditional method as well as by compressive sensing (CS) method, they are very well agreed with each other. The advantage of using CS is that we can increase the sampling time window without increasing the data, thus the tracking capability can be enhanced without sacrificing the resolution.

Slicks on the sea surface are characterized by attenuation of short wind waves and appeаr in radar imagery at moderate incidence angles as areas of reduced intensity. In the proximity of oil platforms, ship routes, fish farms, etc. marine slicks are often identified as oil spills or biogenic films. However, probability of false alarm when detecting film slicks is very high because of the occurrence of structures in radar images looking similar but not related to surface films (“lookalikes”). One of the most frequent "look-alikes" is wind depression areas (WDAs) where the wind excitation of short surface waves is reduced compared to the ambient background. Results of field observations of films slicks and WDA are described and differences in character of wind wave attenuation in different parts of the wind wave spectrum are revealed. Model calculations of wave damping degree (contrast) in film slick and in WDA are carried out and are shown to be in general agreement with experiment. Capabilities of dual-polarization and multi-band microwave radar for discrimination between film slicks and “look-alikes” are analyzed based on experiment and model results.

There is a need for small Sense and Avoid (SAA) systems for small and micro Unmanned Aerial Systems (UAS) to avoid collisions with obstacles and other aircraft. The proposed SAA systems will give drones the ability to “see” close up and give them the agility to maneuver through tight areas. Doppler radar is proposed for use in this sense and avoid system because in contrast to optical or infrared (IR) systems Doppler can work in more harsh conditions such as at dusk, and in rain and snow. And in contrast to ultrasound based systems, Doppler can better sense small sized obstacles such as wires and it can provide a sensing range from a few inches to several miles. An SAA systems comprised of Doppler radar modules and an array of directional antennas that are distributed around the perimeter of the drone can cover the entire sky. These modules are designed so that they can provide the direction to the obstacle and simultaneously generate an alarm signal if the obstacle enters within the SAA system’s adjustable “Protection Border”. The alarm signal alerts the drone’s autopilot to automatically initiate an avoidance maneuver. A series of Doppler radar modules with different ranges, angles of view and transmitting power have been designed for drones of different sizes and applications. The proposed Doppler radar micro SAA system has simple circuitry, works from a 5 volt source and has low power consumption. It is light weight, inexpensive and it can be used for a variety of small unmanned aircraft.

Interferometry constitutes a technique of acquisition height information with a range of applications, such as Digital Surface Model (DSM) generation in order to monitoring the Earth's surface. This work is focused on interferometric DSM creation utilizing radar data of Sentinel-1 and TerraSAR-X missions, covering the wider area of Northern Peloponnese. This area is characterized of loose geological formations and intense active tectonics resulting in continuous and intense relief changes. In this context, the accuracy and the update of the DSMs is essential in order to detect and map any terrain change. The selection of Sentinel-1 and TerraSAR-X images was based on the fact that both missions provide timely, with short revisiting period and satisfactory spatial resolution data. In particular, two ranges of radar data from both missions were submitted in interferometric process aimed at DSM creation. The produced DSMs were compared both visually and statistically to a very accurate reference DSM produced from airphotos by the Greek Cadastral. Furthermore, in order to estimate the accuracy of the DSMs and detect variations of terrain’s surface, points of known elevation have been used. 2D RMSE, correlation and the percentile value were computed and the results are presented.

Even though both the rain measuring instruments, radar and radiometer onboard the TRMM observe the same rain scenes, they both are fundamentally different instruments. Radar is an active instrument and measures backscatter component from vertical rain structure; whereas radiometer is a passive instrument that obtains integrated observation of full depth of the cloud and rain structure. Further, their spatial resolutions on ground are different. Nevertheless, both the instruments are observing the same rain scene and retrieve three dimensional rainfall products. Hence it is only natural to seek answer to the question, what type of information about radiometric observations can be directly retrieved from radar observations. While there are several ways to answer this question, an informational theoretic approach using neural networks has been described in the present work to find if radiometer observations can be predicted from radar observations. A database of TMI brightness temperature and collocated TRMM vertical attenuation corrected reflectivity factor from the year 2012 was considered. The entire database is further classified according to surface type. Separate neural networks were trained for land and ocean and the results are presented.

Due to their great impact on the environment and society, the study of the ice sheets has become a major concern of the scientific community. In particular, the estimation of the ice attenuation is crucial since it enables a more precise characterization of the ice and basal conditions. Although such problem has been often addressed in the literature, the assessment of the ice attenuation is subject to several hypotheses and uncertainties, resulting in a wide range of possible interpretations of the properties of the ice. In this paper, we propose a method for constraining the ice attenuation profiles in the vicinity of an ice core by jointly using coincident radar sounder (RS) data (radargrams) and dielectric profile (DEP) data. Radargrams contain measurements of radar reflected power from ice subsurface dielectric discontinuities (layers) on wide areas. DEP data contain ice dielectric permittivity measurements collected at an ice core. The method relies on the detection of ice layers in the radargrams, the estimation of their depth and reflectivity from the DEP data, and the use of the radar equation for the estimation of ice attenuation through the whole ice column and locally at each layer position. The method has been applied to RS and DEP data acquired at the NEEM core site in Greenland. Experimental results confirm the effectiveness of the proposed method.

Speckle filtering seems to be a never-ending topic for polarimetric synthetic aperture radar imagery processing. Constantly emerging literatures demonstrate that this issue deserves further research effort, especially in the context of much more high spatial resolution. A comparative study will be performed in this paper for recently proposed method such as non-local SAR speckle filtering, Extended Sigma filter proposed by Lee, non-local means filter, Bilateral filter, and so on. Their performance on spatial details preserving and polarimetric properties preserving should be measured thoroughly. Further more the computing performance on large-scale dataset should also be measured.

In this work, a multitemporal algorithm (MLTA), originally conceived for the C-band radar aboard the Sentinel-1 satellite, has been updated in order to retrieve soil moisture from L-Band radar data, such as those provided by the NASA Soil Moisture Active Passive (SMAP) mission. Such type of algorithm may deliver frequent and more accurate soil moisture maps mitigating the effect of roughness and vegetation changes, which are assumed to occur at longer temporal scales with respect to the soil moisture changes. Within the multitemporal inversion scheme based on the Bayesian Maximum A Priori (MAP) criterion, a dense time series of radar measurements is integrated to invert a forward backscattering model which includes the contribution from vegetation. The calibration and validation tasks have been accomplished by using the data collected during the SMAP Validation Experiment 12.The SMAPVEX12 campaign consists of L-Band images collected by the UAVSAR sensor, in situ soil moisture data and measurements of vegetation parameters, collected during the growing season of several crops (pasture, wheat, soybean, corn, etc.). They have been used to update the forward model for bare soil scattering at L-band with respect to the Oh and Sarabandi model previously used at C band. Moreover, the SMAPVEX12 data have been also used to tune a simple vegetation scattering model which considers two different classes of vegetation: those producing mainly single scattering effects, and those characterized by a significant multiple scattering involving terrain surface and vegetation elements interaction.

Understanding the dynamics and processes of the ice sheets is crucial for predicting the behavior of climate change. A potential approach to achieve this is by using high resolution (HR) digital elevation models (DEMs) of the ice surface derived from remote sensing radar or laser altimeters. Unfortunately, at present HR DEMs of large portions of the ice sheets are not available. To address this issue, in this paper we propose a multisensor data fusion technique for the generation of a HR DEM of the ice sheets, which fuses two types of data, i.e., radargrams acquired by radar sounder (RS) instruments and ice surface elevation data measured by altimeter (ALT) instruments. The aim of the technique is to generate a DEM of the ice surface at the best possible horizontal resolution by exploiting the complementary characteristics of the RS and ALT data. This is done by defining a novel processing scheme that involves image processing techniques based on data rescaling, geostatistical interpolation and multiresolution analysis (MRA). The method has been applied to a subset of RS and ALT data acquired over a portion of the Byrd Glacier in Antarctica. Experimental results confirm the effectiveness of the proposed method.

Marine slicks are one of the most common features on the sea surface and a significant part of the slicks is a result of accidental or deliberate oil spills. The shape of oil slicks is their important characteristic that can be used to identify the nature of slick signatures in radar or optical images of the sea surface and possibly to describe them quantitatively. Nowadays, however, there is a lack of systematic experiments with slicks, and the very physical mechanisms of slick spreading are still not well understood. This paper presents results of controlled experiments with spills of surfactants, and a possible physical mechanism of slick asymmetry is discussed. Experiments with artificial film slicks were carried out in different environmental conditions: from an Oceanographic Platform on the Black Sea, and from a vessel on the Gorky Water Reservoir. Slick shape and its evolution were studied using photographic methods, and satellite radar imagery. In the satellite experiments surfactants were poured on the surface at certain time intervals before the satellite overpass. It is obtained that film spreading is not axial symmetric, and the spills are stretched along the wind, a long-to-short slick axis ratio weakly depends on spreading time and grows with wind speed. A physical mechanism of slick deformation due to mean surface currents induced by wind waves is proposed. Namely, drift currents induced by oblique propagating surface waves increase in film slicks due to enhanced wave damping and these currents result in reduced spreading rate in the cross wind direction. Theoretical analysis of slick spreading accounting for the effect of surface waves is presented, and theoretical estimates are shown to be consistent with experiment.

The Dual-frequency Precipitation Radar (DPR) on the Global Precipitation Measurement (GPM) core satellite was developed by Japan Aerospace Exploration Agency (JAXA) and National Institute of Information and Communications Technology (NICT). The GPM is a follow-on mission of the Tropical Rainfall Measuring Mission (TRMM). The objectives of the GPM mission are to observe global precipitation more frequently and accurately than TRMM. The frequent precipitation measurement about every three hours will be achieved by some constellation satellites with microwave radiometers (MWRs) or microwave sounders (MWSs), which will be developed by various countries. The accurate measurement of precipitation in mid-high latitudes will be achieved by the DPR. The GPM core satellite is a joint product of National Aeronautics and Space Administration (NASA), JAXA and NICT. NASA developed the satellite bus and the GPM Microwave Imager (GMI), and JAXA and NICT developed the DPR. JAXA and NICT developed the DPR through procurement. The configuration of precipitation measurement using active radar and a passive radiometer is similar to TRMM. The major difference is that DPR is used in GPM instead of the precipitation radar (PR) in TRMM. The inclination of the core satellite is 65 degrees, and the nominal flight altitude is 407 km. The non-sun-synchronous circular orbit is necessary for measuring the diurnal change of rainfall similarly to TRMM. The DPR consists of two radars, which are Ku-band (13.6 GHz) precipitation radar (KuPR) and Ka-band (35.5 GHz) precipitation radar (KaPR). Both KuPR and KaPR have almost the same design as TRMM PR. The DPR system design and performance were verified through the ground test. GPM core observatory was launched at 18:37:00 (UT) on February 27, 2014 successfully. DPR orbital check out was completed in May 2014. The results of orbital checkout show that DPR meets its specification on orbit. After completion of initial checkout, DPR started Normal Operations and Initial Calibration and Validation period was started. JAXA conducted internal calibrations, external calibrations and phase code changes to mitigate KuPR sidelobe clutter effect. JAXA evaluated these operations results and concluded that DPR data could go public. DPR products were released to the public on Sep. 2, 2014 and Normal Observation Operation period was started. JAXA is continuing DPR trend monitoring, calibration operations to confirm that DPR keeps its function and performance on orbit.

During the Northeast monsoon season, India receives about 11% of its annual rainfall. Many districts in South Peninsula receive 30-60% of their annual rainfall. Coastal Tamil Nadu receives 60% of its annual rainfall and interior districts about 40-50 %. During the month of November, 2015, three synoptic scale weather systems affected Tamil Nadu and Pondicherry causing extensive rainfall activity over the region. Extremely heavy rains occurred over districts of Chennai, Thiruvallur and Kancheepuram, due to which these 3 districts were fully inundated. 122 people in Tamil Nadu were reported to have died due to the flooding, while over 70,000 people had been rescued. State government reported flood damage of the order of around Rs 8481 Crores. The rainfall received in Chennai district during 1.11.2015 to 5.12.2015 was 1416.8 mm against the normal of 408.4 mm. The extremely heavy rains were found to be associated with strong wind surges at lower tropospheric levels, which brought in lot of moisture flux over Chennai and adjoining area. The subtropical westerly trough at mid-tropospheric levels extended much southwards than its normal latitude, producing favorable environment for sustained rising motions ahead of approaching trough over coastal Tamil Nadu. Generated strong upward velocities in the clouds lifted the cloud tops to very high levels forming deep convective clouds. These clouds provided very heavy rainfall of the order of 150-200 mm/hour. In this paper we have used radar data to examine and substantiate the cloud burst that led to these torrential rains over Chennai and adjoining areas during the Northeast Monsoon period, 2015.

The application of sparse representation to SAR/ISAR imaging has attracted much attention over the past few years. This new class of sparse representation based imaging methods present a number of unique advantages over conventional range-Doppler methods, the basic idea behind these works is to formulate SAR/ISAR imaging as a sparse signal recovery problem. In this paper, we propose a new two-dimensional pattern-coupled sparse Bayesian learning(SBL) method to capture the underlying cluster patterns of the ISAR target images. Based on this model, an expectation-maximization (EM) algorithm is developed to infer the maximum a posterior (MAP) estimate of the hyperparameters, along with the posterior distribution of the sparse signal. Experimental results demonstrate that the proposed method is able to achieve a substantial performance improvement over existing algorithms, including the conventional SBL method.

NASA-ISRO Synthetic Aperture Radar (NISAR), a novel SAR concept will be utilized to image wide swath at high resolution of stripmap SAR. It will have observations in L- and S-bands to understand highly spatial and temporally complex processes such as ecosystem disturbances, ice sheet changes, and natural hazards including earthquakes, tsunamis, volcanoes, and landslides. NISAR with several advanced features such as 12 days interferometric orbit, achievement of high resolution and wide swath images through SweepSAR technology and simultaneous data acquisition in dual frequency would support a host of applications. The primary objectives of NISAR are to monitor ecosystems including monitoring changes in ecosystem structure and biomass estimation, carbon flux monitoring; mangroves and wetlands characterization; alpine forest characterization and delineation of tree-line ecotone, land surface deformation including measurement of deformation due to co-seismic and inter-seismic activities; landslides; land subsidence and volcanic deformation, cryosphere studies including measurements of dynamics of polar ice sheet, ice discharge to the ocean, Himalayan snow and glacier dynamics, deep and coastal ocean studies including retrieval of ocean parameters, mapping of coastal erosion and shore-line change; demarcation of high tide line (HTL) and low tide line (LTL) for coastal regulation zones (CRZ) mapping, geological studies including mapping of structural and lithological features; lineaments and paleo-channels; geo-morphological mapping, natural disaster response including mapping and monitoring of floods, forest fires, oil spills, earthquake damage and monitoring of extreme weather events such as cyclones. In addition to the above, NISAR would support various other applications such as enhanced crop monitoring, soil moisture estimation, urban area development, weather and hydrological forecasting.

In this paper we propose to simulate SAR radar images that can be acquired by aircraft or satellite. This corresponds to a real problematic, in fact, an airborne radar data acquisition campaign, was conducted in the south east of France. We want to estimate the geometric deformations that a digital terrain model can be subjected. By extrapolation, this construction should also allow to understand the image distortion if a plane is replaced by a satellite. This manipulation allow to judge the relevance of a space mission to quantify geological and geomorphological data. The radar wave is an electromagnetic wave, they have the advantage of overcoming atmospheric conditions since more wavelength is large is better crossing the cloud layer. Therefore imaging radar provides continuous monitoring.

A new Ku-band low incidence model (KuLMOD) is proposed for retrieving wind speeds from Tropical Rainfall Mapping Mission (TRMM) precipitation radar (PR) data. The data set consisted of TRMM PR observations and collocated National Data Buoy Center (NDBC) buoy-measured wind and wave data. The TRMM PR data properties were analyzed regarding their dependence on the wind speed. The KuLMOD model was developed using incidence angles (0.5–6.5°) and wind speeds (1.5–16.5 m/s) as inputs. The model coefficients were derived by fitting the collocated data. With the KuLMOD, the wind speeds were retrieved from the TRMM PR data using the least squares method and validated with the NDBC buoy measurements, yielding a root mean square error of 1.57 m/s. The retrieval accuracies for the different incidence angles and wind speeds are presented.

The precipitation radar (PR) on the Tropical Rainfall Measuring Mission (TRMM) satellite has high vertical resolution, and ground radar (GR) has relatively good capability to detect weak precipitation and relatively good horizontal resolution. The joint utilization of PR and GR will be an important factor in maximizing the benefit to be reaped from both instruments. In this paper, we will blend the PR data and GR reflectivity in Nanjing, China based on image fusion algorithm. Integrating PR and GR mainly includes following steps: spatial-temporal matchup of PR and GR data, image fusion algorithms selection and quality evaluation of the fused image.