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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.

This study presents a remote sensing application of using time series Landsat satellite images for monitoring the solid waste disposal site (WDS). We propose a method of detecting high-rise buildings landfills, such as municipal dumps and solid waste, according to a radar image (the height of the ground level). For disposal site detection a variety steps of image processing used (calculation image average level of the earth's surface; filtering thresholds spectral brightness coefficients, the size of the connected components, the nature of reducing the level of height with the distance of the maximum level). The spatial geometric features of waste disposal facilities are analytically expressed by linear and radial characteristics from other objects of the earth surface. As a result, the proposed method demonstrates good accuracy in detection the solid waste disposal site on real satellite images.

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.

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.

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.

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.

With the development of space technology, the number of spacecrafts and debris are increasing year by year. The demand for detecting and identification of spacecraft is growing strongly, which provides support to the cataloguing, crash warning and protection of aerospace vehicles. The majority of existing approaches for three-dimensional reconstruction is scattering centres correlation, which is based on the radar high resolution range profile (HRRP). This paper proposes a novel method to reconstruct the threedimensional scattering centre structure of target from a sequence of radar ISAR images, which mainly consists of three steps. First is the azimuth scaling of consecutive ISAR images based on fractional Fourier transform (FrFT). The later is the extraction of scattering centres and matching between adjacent ISAR images using grid method. Finally, according to the coordinate matrix of scattering centres, the three-dimensional scattering centre structure is reconstructed using improved factorization method. The three-dimensional structure is featured with stable and intuitive characteristic, which provides a new way to improve the identification probability and reduce the complexity of the model matching library. A satellite model is reconstructed using the proposed method from four consecutive ISAR images. The simulation results prove that the method has gotten a satisfied consistency and accuracy.

This contribution gives an overview of recent investigations regarding the detection of a consumer market unmanned aerial vehicles (UAV). The steadily increasing number of such drones gives rise to the threat of UAVs interfering civil air traffic. Technologies for monitoring UAVs which are flying in restricted air space, i. e. close to airports or even over airports, are desperately needed. One promising way for tracking drones is to employ radar systems. For the detection and classification of UAVs, the knowledge about their radar cross section (RCS) and micro-Doppler signature is of particular importance. We have carried out numerical and experimental studies of the RCS and the micro-Doppler of an example commercial drone in order to study its detectability with radar systems.

Knowing the exact growth stage of agricultural crops can be valuable information for crop management and monitoring. In Canada, canola fields are particularly vulnerable for crop disease development during their flowering stage, especially when the fields are under persistent wet conditions. Clubroot and sclerotinia are diseases that can occur in canola when these two factors come together. Remote sensing can provide an interesting tool for the monitoring of crop phenological stages over large agriculture landscapes. Reliable and frequent access to data is needed to determine field-specific growth stages. Given their all-weather capability, radar sensors are optimal for monitoring such a dynamic crop parameter. In 2014, Agriculture and Agri-Food Canada collected crop phenology information over multiple canola fields in the area of Carman, Manitoba. Coincidental to ground data collection, fully polarimetric RADARSAT-2 and dual-polarimetric TerraSAR-X satellite data were acquired over the study site. In collaboration with A. U. G. Signals Ltd., a methodology will be developed and validated for the identification of inflorescence emergence and flowering in canola fields. Analysis of the polarimetric datasets from this study determined that several polarimetric parameters were sensitive to the emergence of flower buds and the flowering stage in canola. The alpha angle and entropy in both the C- and X-band were able to identify these growth stages, in addition to any of the reflectivity ratios and differential reflectivity responses that incorporated an HV response. The RADARSAT-2 scatter diversity, degree of purity and depolarization index also demonstrated great potential at identifying canola flower emergence and flowering. These latter polarimetric parameters along with the reflectivity ratios may be advantageous given their ease in implementation within a larger risk assessment satellite-derived methodology for canola crop disease.

Clouds cover approximately 70% of the Earth's surface and therefore play a crucial rule in governing both the climate system and the hydrological cycle. The microphysical properties of clouds such as the cloud particle size distribution, shape distribution and spatial homogeneity contribute significantly to the net radiative effect of clouds and these properties must therefore be measured and understood to determine the exact contribution of clouds to the climate system. Significant discrepancies are observed between meteorological models and observations, particularly in polar regions that are most sensitive to changes in climate, suggesting a lack of understanding of these complex microphysical processes. Remote sensing techniques such as polarimetric LIDAR and radar allow microphysical cloud measurements with high temporal and spatial resolution however these instruments must be calibrated and validated by direct in situ measurements. To this end a low cost, light weight holographic imaging device has been developed and experimentally tested that is suitable for deployment on a weather balloon or tower structure to significantly increase the availability of in situ microphysics retrievals.

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.

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.

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.

International audience; This paper investigates performance characteristics of similarity measures (SM) used in image registration domain to discriminate between aligned and not-aligned reference and template image (RI and TI) fragments. The study emphasizes registration of multimodal remote sensing images including optical-to-radar, optical-to-DEM, and radar-to- DEM scenarios. We compare well-known area-based SMs such as Mutual Information, Normalized Correlation Coefficient, Phase Correlation, and feature-based SM using SIFT and SIFT-OCT descriptors. In addition, a new SM called logLR based on log-likelihood ratio test and parametric modeling of a pair of RI and TI fragments by the Fractional Brownian Motion model is proposed. While this new measure is restricted to linear intensity change between RI and TI (assumption somewhat restrictive for multimodal registration), it takes explicitly into account noise properties of RI and TI and multivariate mutual distribution of RI and TI pixels. Unlike other SMs, distribution of logLR measure for the null hypothesis does not depend on registration scenario or fragments size and follows closely chi-squared distribution according to Wilks's theorem. We demonstrate that a SM utility for image registration purpose can be naturally represented in (True Positive Rate, Positive Likelihood Rate) coordinates. Experiments on real images show that overall the logLR SM outperforms the other SMs in terms of area under the ROC curve, denoted AUC. It also provides the highest Positive Likelihood Rate for True Positive Rate values below 0.4-0.6. But for certain registration problem types, logLR can be second or third best after MI or SIFT SMs. © 2016 SPIE..

High-resolution Synthetic Aperture Radar (SAR) data represent an essential resource for the extraction of Ground Control Points (GCP) with sub-metric accuracy without in situ measurement campaigns. Conceptually, SAR-based GCP extraction consists of the following two steps: (i) identification of the same local feature on more SAR images and determination of their range/azimuth coordinates; (ii) spatial 3D positioning retrieval from the 2D radar coordinates, through spatial triangulation (stereo analysis) and inversion methods. In order to boost the geolocation accuracy, SAR images must be acquired from different line of sights, with intersection angles typically wider than 10 degrees, or even in opposite looking directions. In the present study, we present an algorithm specifically designed for ensuring robustness and accuracy in the fully automatic detection of bright isolated targets (steel light poles or towers) even when dealing with opposite looking data takes. In particular, the popular Harris algorithm has been selected as detector because it is the most stable and robust-to-noise algorithm for corners detection on SAR images. We outline the designed algorithmic solution and discusses the results derived over the urban area of Pisa (Italy), where more than ten COSMO-SkyMed Enhanced Spotlight (ES) stereo images are available, thus resulting an optimal test site for an assessment of the performances of the processing chain. The experimental analysis proofs that, assumed timing has been properly recalibrated, we are capable to automatically extract GCP from CSK ES data takes consisting in a very limited number of images.

Ground Control Points (GCP), automatically extracted from Synthetic Aperture Radar (SAR) images through 3D stereo analysis, can be effectively exploited for an automatic orthorectification of optical imagery if they can be robustly located in the basic optical images. The present study outlines a SAR/Optical cross-matching procedure that allows a robust alignment of radar and optical images, and consequently to derive automatically the corresponding sub-pixel position of the GCPs in the optical image in input, expressed as fractional pixel/line image coordinates. The cross-matching in performed in two subsequent steps, in order to gradually gather a better precision. The first step is based on the Mutual Information (MI) maximization between optical and SAR chips while the last one uses the Normalized Cross-Correlation as similarity metric. This work outlines the designed algorithmic solution and discusses the results derived over the urban area of Pisa (Italy), where more than ten COSMO-SkyMed Enhanced Spotlight stereo images with different beams and passes are available. The experimental analysis involves different satellite images, in order to evaluate the performances of the algorithm w.r.t. the optical spatial resolution. An assessment of the performances of the algorithm has been carried out, and errors are computed by measuring the distance between the GCP pixel/line position in the optical image, automatically estimated by the tool, and the “true” position of the GCP, visually identified by an expert user in the optical images.

This paper presents an approach to remove SLAR (Side-Looking Airborne Radar) image regions with low visual quality to be used for an automatic detection of oil slicks on a board system. This approach is focused on the detection and labelling of SLAR image regions caused by a poor acquisition from two antennas located on both sides of an aircraft. Thereby, the method distinguishes ineligible regions which are not suitable to be used on the steps of an automatic detection process of oil slicks because they have a high probability of causing false positive results in the detection process. To do this, the method uses a hybrid approach based on edge-based segmentation aided by Gabor filters for texture detection combined with a search algorithm of significant grey-level changes for fitting the boundary lines in each of all the bad regions. Afterwards, a statistical analysis is done to label the set of pixels which should be used for recognition of oil slicks. The results show a successful detection of the ineligible regions and consequently how the image is partitioned in sub-regions of interest in terms of detecting the oil slicks, improving the accuracy and reliability of the oil slick detection. This work was supported by the project (RTC-2014-1863-8) of call for collaboration challenges MINECO.

The main lobe and the side lobes of strong scatterers are sometimes clearly visible in SAR images. Sidelobe reduction is of particular importance when imaging scenes contain objects such as ships and buildings having very large radar cross sections. Amplitude weighting is usually used to suppress sidelobes of the images at the expense of broadening of mainlobe, loss of resolution and degradation of SAR images. The Spatial Variant Apodization (SVA) is an Adaptive SideLobe Reduction (ASLR) technique that provides high effective suppression of sidelobes without broadening mainlobe. In this paper, we apply SVA to process COSMO-SkyMed (CSK) StripMap and Spotlight X-band data and compare the images with the standard products obtained via Hamming window processing. Different test sites have been selected in Italy, Argentina, California and Germany where corner reflectors are installed. Experimental results show clearly the resolution improvement (20%) while sidelobe kept to a low level when SVA processing is applied compared with Hamming windowing one. Then SVA technique is applied to Interferometric SAR image processing (INSAR) using a CSK StripMap interferometric tandem-like data pair acquired on East-California. The interferometric coherence of image pair obtained without sidelobe reduction (SCS_U) and with sidelobe reduction performed via Hamming window and via SVA are compared. High resolution interferometric products have been obtained with small variation of mean coherence when using ASLR products with respect to hamming windowed and no windowed one.

This paper focuses on the generation of Digital Elevation Models (DEMs) with COSMO SkyMed Spotlight data in providing DEMs. In particular, the peculiarity of Spotlight data (affected from Doppler centroid drift) is investigated, and the use of the processing chain included in the Delft Object-oriented Radar Interferometric Software (DORIS [1]). The effects of not correctly handled Doppler drift is shown. The standard interferometric processing, without Doppler drift handling, has been applied to Spotlight image pairs, resulting in interferometric coherence loss in interferograms as we move away from scene center. So, the standard processing chain has been modified to take in account the Doppler centroid drift affecting Spotlight data and very high resolution and accuracy DEMs have been obtained. Some Spotlight image pairs have been processed and the obtained DEMs have been shown and analyzed proving the high details and product accuracy.

PSI (Persistent Scatterer Interferometry) analysis of large area is always a challenging task regarding the removal of the atmospheric phase component. This work presents an investigation of ground deformation measurements based on a combination of DInSAR Time-Series (DTS) and PSI techniques, applied in a large area of open pit iron mines located in Carajas (Brazilian Amazon Region), aiming at detect high rates of linear and nonlinear ground deformation. These mines have presented a historical of instability and surface monitoring measurements over sectors of the mines (pit walls) have been carried out based on ground based radar and total station (prisms). By using a priori information regarding the topographic phase error and phase displacement model derived from DTS, temporal phase unwrapping in the PSI processing and the removal of the atmospheric phases can be performed more efficiently. A set of 33 TerraSAR-X-1 images, acquired during the period from March 2012 to April 2013, was used to perform this investigation. The DTS analysis was carried out on a stack of multi-look unwrapped interferogram using an extension of SVD to obtain the Least-Square solution. The height errors and deformation rates provided by the DTS approach were subtracted from the stack of interferogram to perform the PSI analysis. This procedure improved the capability of the PSI analysis to detect high rates of deformation as well as increased the numbers of point density of the final results. The proposed methodology showed good results for monitoring surface displacement in a large mining area, which is located in a rain forest environment, providing very useful information about the ground movement for planning and risks control.

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 ultimate objective of this work is to improve characterization of the ice cover distribution in the polar areas, to improve sea ice mapping and to develop a new automated real-time high spatial resolution multi-sensor ice extent and ice edge product for use in operational applications. Despite a large number of currently available automated satellite-based sea ice extent datasets, analysts at the National Ice Center tend to rely on original satellite imagery (provided by satellite optical, passive microwave and active microwave sensors) mainly because the automated products derived from satellite optical data have gaps in the area coverage due to clouds and darkness, passive microwave products have poor spatial resolution, automated ice identifications based on radar data are not quite reliable due to a considerable difficulty in discriminating between the ice cover and rough ice-free ocean surface due to winds. We have developed a multisensor algorithm that first extracts maximum information on the sea ice cover from imaging instruments VIIRS and MODIS, including regions covered by thin, semitransparent clouds, then supplements the output by the microwave measurements and finally aggregates the results into a cloud gap free daily product. This ability to identify ice cover underneath thin clouds, which is usually masked out by traditional cloud detection algorithms, allows for expansion of the effective coverage of the sea ice maps and thus more accurate and detailed delineation of the ice edge. We have also developed a web-based monitoring system that allows comparison of our daily ice extent product with the several other independent operational daily products.

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).

This paper is based on a proposed unmanned aerial system platform that is to be outfitted with high-resolution sensors. The proposed system is to be tethered to a moveable ground station, which may be a research vessel or some form of ground vehicle (e.g., car, truck, or rover). The sensors include, at a minimum: camera, infrared sensor, thermal, normalized difference vegetation index (NDVI) camera, global positioning system (GPS), and a light-based radar (LIDAR). The purpose of this paper is to provide an overview of existing methods for pollution detection of failing septic systems, and to introduce the proposed system. Future work will look at the high-resolution data from the sensors and integrating the data through a process called information fusion. Typically, this process is done using the popular and well-published Kalman filter (or its nonlinear formulations, such as the extended Kalman filter). However, future work will look at using a new type of strategy based on variable structure estimation for the information fusion portion of the data processing. It is hypothesized that fusing data from the thermal and NDVI sensors will be more accurate and reliable for a multitude of applications, including the detection of pollution entering the Chesapeake Bay area.

The scanning pencil-beam Scatterometer configuration is pretty effective in covering a large ground-swath by rotating a moderately sized paraboloid dish at a moderate speed. For example, Oscat (Oceansat-II Scatterometer) did cover a ground-swath of 1550km using a 1m diameter reflector that was rotated at 20.5 rpm. The decade-long service (1999-2009) provided by the Seawinds instrument onboard the Quikscat mission followed by an almost half-a-decade (2009-2014) service of Oscat has made this configuration tremendously popular with the global user community. A major drawback of conventional pencil-beam systems like Seawinds and Oscat is the relatively poor spatial resolution. The ground-resolution is beamwidth-limited azimuthally while, in elevation, the resolution is improved by engaging pulse-compression and range-binning. Oscat’s Instantaneous Field of View (IFOV) was 25km wide in azimuth (az) and 50km in elevation (el) at 49° incidence angle. The range-compressed resolution bins had dimensions of 6km (el) x 25km (az). Therefore, qualified wind products could be generated upon square grids no finer than 25km x 25km resolution. According to recommendations of International Ocean Vector Wind Science Team (IOVWST) and Oscat user community, high-resolution scatterometry is the requirement of the day with wind-vector cell-size dimension of 5km or better. One way to improve the resolution is to adopt the SAR principle of Range-Doppler discrimination in the scanning pencil-beam configuration. The footprint can be resolved simultaneously in range as well as in azimuth, thus significantly improving the size of the combined Range-Doppler resolution bin (~ 1km). However, the addition of Doppler filtering to conically scanning radar brings with it its own disadvantages e.g. the limitations of dwell time and the constant change in orientation of isodop lines. This paper presents the constraints in system design of high-resolution scanning systems, the design trade-offs, the methods of handling high PRF, the radar pulsing scheme and the achievable resolution.

Water level was estimated, using AltiKa radar altimeter onboard the SARAL satellite, over the Ukai reservoir using modified algorithms specifically for inland water bodies. The methodology was based on waveform classification, waveform retracking, and dedicated inland range corrections algorithms. The 40-Hz waveforms were classified based on linear discriminant analysis and Bayesian classifier. Waveforms were retracked using Brown, Ice-2, threshold, and offset center of gravity methods. Retracking algorithms were implemented on full waveform and subwaveforms (only one leading edge) for estimating the improvement in the retrieved range. European Centre for Medium-Range Weather Forecasts (ECMWF) operational, ECMWF re-analysis pressure fields, and global ionosphere maps were used to exactly estimate the range corrections. The microwave and optical images were used for estimating the extent of the water body and altimeter track location. Four global positioning system (GPS) field trips were conducted on same day as the SARAL pass using two dual frequency GPS. One GPS was mounted close to the dam in static mode and the other was used on a moving vehicle within the reservoir in Kinematic mode. In situ gauge dataset was provided by the Ukai dam authority for the time period January 1972 to March 2015. The altimeter retrieved water level results were then validated with the GPS survey and in situ gauge dataset. With good selection of virtual station (waveform classification, back scattering coefficient), Ice-2 retracker and subwaveform retracker both work better with an overall root-mean-square error

Global warming has caused serious damage to our environment in recent years. Accelerated loss of ice from Greenland and Antarctica has been observed in recent decades. The melting of polar ice sheets and mountain glaciers has a considerable influence on sea level rise and altering ocean currents, potentially leading to the flooding of the coastal regions and putting millions of people around the world at risk. Synthetic aperture radar (SAR) systems are able to provide relevant information about subsurface structure of polar ice sheets. Manual layer identification is prohibitively tedious and expensive and is not practical for regular, longterm ice-sheet monitoring. Automatic layer finding in noisy radar images is quite challenging due to huge amount of noise, limited resolution and variations in ice layers and bedrock. Here we propose an approach which automatically detects ice surface and bedrock boundaries using distance regularized level set evolution. In this approach the complex topology of ice and bedrock boundary layers can be detected simultaneously by evolving an initial curve in radar imagery. Using a distance regularized term, the regularity of the level set function is intrinsically maintained that solves the reinitialization issues arising from conventional level set approaches. The results are evaluated on a large dataset of airborne radar imagery collected during IceBridge mission over Antarctica and Greenland and show promising results in respect to hand-labeled ground truth.

Oil spill on the water bodies has adverse effects on coastal and marine ecology. Oil spill contingency planning is of utmost importance in order to plan for mitigation and remediation of the oceanic oil spill. Remote sensing technologies are used for monitoring the oil spills on the ocean and coastal region. Airborne and satellite sensors such as optical, infrared, ultraviolet, radar and microwave sensors are available for remote surveillance of the ocean. Synthetic Aperture Radar (SAR) is used most extensively for oil-spill monitoring because of its capability to operate during day/night and cloud-cover condition. This study detects the possible oil spill regions on fully polarimetric Uninhabited Aerial Vehicle - Synthetic Aperture Radar (UAVSAR) images. The UAVSAR image is decomposed using Cloude-Pottier polarimetric decomposition technique to obtain entropy and alpha parameters. In addition, other polarimetric features such as co-polar correlation and degree of polarization are obtained for the UAVSAR images. These features are used to with fuzzy logic based classification to detect oil spill on the SAR images. The experimental results show the effectiveness of the proposed method.

Doppler Weather Radar (DWR) can provide tropospheric wind observations with high temporal and spatial resolutions. The Volume Velocity Processing (VVP) technique is one of the processing methods which can provide vertical profiles of mean horizontal winds. The DWR observed VVP winds gives a continuous observation of the wind field at various atmospheric levels. The quality of the VVP winds is studied against the short-range forecast of the NCUM model (model background). The biases of the observation are calculated against model background. This study focuses on the quality of VVP winds and seasonal variation of bias of the observed wind. This results shows that the VVP winds provides reasonably accurate estimates of the vertical wind structure in the troposphere over radar locations which can be effectively used in the numerical weather prediction system.

A experiments were performed in the shelf zone of the Black Sea in 2015 to study variability of the current fields and other characteristics of sea bulk, wind waves, and the near-surface atmospheric layer. Region with the secluded underwater hill streamlined with currents was selected. Measurements were carried out from the onboard of vessels on move and in drift by optical, radar, acoustic equipment, and STD probe. The complex different structure of waters, which was formed under the influence of shelf waters and water of the open sea interaction, was observed during the experiment. The analysis of measurements in the water column showed that that the flow around underwater elevation forms the hydro-physical disturbances of marine environment. Maximum flow observed above the slopes of underwater elevation and reach 50 cm/s. Wind speed varied from 0 to 10 m/s. On radar panoramas in the region of underwater elevation is observed the appearance of the wave structure, different from the background wind waves. This anomaly on the sea surface is connected with non-uniform current in the neighborhood underwater elevation.

The El Nino and La Nina have been found to influence the weather at a remote place. In this paper, the authors investigate the impact of El Nino & La Nina on the surface temperature and rainfall over few selected locations in India and abroad. The study shows that the ENSO affects the surface rainfall; however, the impact is not the same over all the locations. In order to find out whether such influence is latitude sensitive, the study has been performed over locations located at different latitudes and at a fixed longitude. To check if the El Nino/La Nina leaves any impressions on the upper air meteorological elements, the cloud liquid water (CLW), precipitation water (PW), latent heat (LH), freezing level height (HFL) and the bright band height (BBH) over a few locations have been studied from the Earth’s surface up to a height of 18 km above. The CLW, PW and LH values have been obtained from the data product 2A12 of the Tropical Microwave Imager (TMI) onboard the Tropical Rainfall Measuring Satellite (TRMM), while that of the BBH and the HFL are obtained from the data product 2A23 of the precipitation radar (PR) onboard the TRMM.

Polarimetric SAR data has proven its potential to extract scattering information for different features appearing in single resolution cell. Several decomposition modelling approaches have been developed to retrieve scattering information from PolSAR data. During scattering power decomposition based on physical scattering models it becomes very difficult to distinguish volume scattering as a result from randomly oriented vegetation from scattering nature of oblique structures which are responsible for double-bounce and volume scattering , because both are decomposed in same scattering mechanism. The polarization orientation angle (POA) of an electromagnetic wave is one of the most important character which gets changed due to scattering from geometrical structure of topographic slopes, oriented urban area and randomly oriented features like vegetation cover. The shift in POA affects the polarimetric radar signatures. So, for accurate estimation of scattering nature of feature compensation in polarization orientation shift becomes an essential procedure. The prime objective of this work was to investigate the effect of shift in POA in scattering information retrieval and to explore the effect of deorientation on regression between field-estimated aboveground biomass (AGB) and volume scattering. For this study Dudhwa National Park, U.P., India was selected as study area and fully polarimetric ALOS PALSAR data was used to retrieve scattering information from the forest area of Dudhwa National Park. Field data for DBH and tree height was collect for AGB estimation using stratified random sampling. AGB was estimated for 170 plots for different locations of the forest area. Yamaguchi four component decomposition modelling approach was utilized to retrieve surface, double-bounce, helix and volume scattering information. Shift in polarization orientation angle was estimated and deorientation of coherency matrix for compensation of POA shift was performed. Effect of deorientation on RGB color composite for the forest area can be easily seen. Overestimation of volume scattering and under estimation of double bounce scattering was recorded for PolSAR decomposition without deorientation and increase in double bounce scattering and decrease in volume scattering was noticed after deorientation. This study was mainly focused on volume scattering retrieval and its relation with field estimated AGB. Change in volume scattering after POA compensation of PolSAR data was recorded and a comparison was performed on volume scattering values for all the 170 forest plots for which field data were collected. Decrease in volume scattering after deorientation was noted for all the plots. Regression between PolSAR decomposition based volume scattering and AGB was performed. Before deorientation, coefficient determination (R2) between volume scattering and AGB was 0.225. After deorientation an improvement in coefficient of determination was found and the obtained value was 0.613. This study recommends deorientation of PolSAR data for decomposition modelling to retrieve reliable volume scattering information from forest area.

Simulation and prediction of Indian monsoon rainfall at scales from days-to-season is a challenging task for numerical modelling community worldwide. Gridded estimates of daily rainfall data are required for both land and oceanic regions for model validation, process studies and in turn for model development. Due to recent developments in satellite meteorology, it has become possible to produce realistic near real-time gridded rainfall datasets at operational basis by combining satellite estimates with rain gauge values and other available in-situ observations. Microwave and space based radar based estimates of rainfall has revolutionised the preparation of rainfall datasets especially for tropics. However, a variety of multi-satellite products are available over Indian monsoon region from a variety of sources. Popular products like TRMM TMPA3B42 (RT and V7), GsMaP, CPC/RFE, GPCP and GPM are available to end users in various space/time scales for applications and model validation. In this study, we show the representation and skill of monsoon rainfall from a variety of multi-satellite products over the Indian region. The bias and skill of multi-satellite rainfall are evaluated against gauge based observations. It was found that the TRMM based TMPA was one of the best dataset for Indian monsoon region. Attempt is made to compare the latest GPM based data with other products. The GPM based rainfall product is seen to be superior compared to TRMM.

Numerical climate and weather models depend on measurements from space-borne satellites to complete model validation and improvements. Precipitation profiling capabilities are currently limited to a few instruments deployed in Low Earth Orbit (LEO), which cannot provide the temporal resolution necessary to observe the evo- lution of short time-scale weather phenomena and improve numerical weather prediction models. A constellation of cloud- and precipitation-profiling instruments in LEO would provide this essential capability, but the cost and timeframe of typical satellite platforms and instruments constitute a possibly prohibitive challenge. A new radar instrument architecture that is compatible with low-cost satellite platforms, such as CubeSats and SmallSats, has been designed at JPL. Its small size, moderate mass and low power requirement enable constellation missions, which will vastly expand our ability to observe weather systems and their dynamics and thermodynamics at sub-diurnal time scales down to the temporal resolutions required to observe developing convection. In turn, this expanded observational ability can revolutionize weather now-casting and medium-range forecasting, and enable crucial model improvements to improve climate predictions.

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.

Convective downdraft motions and related outflow wind considered as an eventual source of potential damage which can be more severe in the aviation sector. A great variety of atmospheric environments can produce these downdraft motions. These events are not easily detectable using conventional weather radar or wind shear alert systems, while Doppler radars are useful for identifying these Downbursts. In order to identify the situations that can cause these downdraft events different diagnostic tools are designed. Recently launched Indian satellite INSAT-3D, with atmospheric sounder and imager on board, is capable of identifying regions of downburst occurrence and can help in monitoring them in real time. Some Downburst events reported over different parts of India, during January-April period is investigated using Microburst Wind Speed Potential Index (MWPI) and thermodynamic characteristics derived from the NCMRWF GFS (NGFS) model. An attempt is made to make a short range prediction of these events using MWPI computed from NGFS model forecasts. The results are validated with in-situ observations and also by employing INSAT-3D data and it is shown that the method has a reasonable success. All the investigated downdraft events are associated with the hybrid Microburst environment.

Using the observations of both ground based disdrometer and Micro Rain Radar, Drop size distribution (DSD) parameters were derived using gamma function over coastal station Thumba. Stratiform rain and transition rain regime has been considered to study the vertical variability of DSD parameters for different monsoon seasons during 2006- 2008. The analysis clearly reveals a significant variation in DSD parameters for different seasons. Contour Frequency by Altitude Diagram (CFAD) of DSD parameters is carried out to examine salient microphysical characteristics of DSD during these two rain regimes. Results show that the observed variability of gamma parameters and median volume diameter is attributed to microphysical processes like evaporation, break-up and collision-coalescence. The significance of the present results demonstrates the capability of Ka band radar in understanding the microphysics of rain during light to moderate rain regimes

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.

Satellites play a major role in understanding the spatial and vertical distribution of aerosols and cloud microphysical parameters over a large area. However, the inherent limitations in satellite retrievals can be improved through inter-comparisons with airborne platforms. Over the Indian sub-continent, the vertical profiles retrieved from space-borne lidar such as CALIOP (Cloud-Aerosol LIdar with Orthogonal Polarization) on board the satellite CALIPSO and Cloud Profiling Radar (CPR) on board the satellite CloudSat were inter- compared with the aircraft observations conducted during Cloud Aerosol Interactions and Precipitation Enhancement Experiment (CAIPEEX). In the absence of high clouds, both aircraft and CALIOP showed similar features of aerosol layering and water-ice cloud signatures. As CALIOP could not penetrate the thick clouds, the aerosol information below the cloud is missed. While the aircraft could measure high concentrations below the cloud base and above the low clouds in the presence of high clouds. The aircraft derived liquid water content (LWC) and droplet effective radii (R e ) showed steady increase from cloud base to cloud top with a variable cloud droplet number concentration (CDNC). While the CloudSat derived LWC, CDNC and R e showed increase from the cloud top to cloud base in contradiction to the aircraft measurements. The CloudSat profiles are underestimated as compared to the corresponding aircraft profiles. Validation of satellite retrieved vertical profiles with aircraft measurements is very much essential over the tropics to improve the retrieval algorithms and to constrain the uncertainties in the regional cloud parameterization schemes.

The ionosphere, which extends from 50-450 kms in earth’s atmosphere, is a particularly important region with regards electromagnetic wave propagation and radio communications in the L-band and lower frequencies. These ions interact with the traversing electromagnetic wave and cause rotation of polarization of the radar signal. In this paper, a potentially computable method for quantifying Faraday rotation (FR), is discussed with the knowledge of full polarimetric ALOS/PALSAR data and ALOS-2/PALSAR-2 data. For a well calibrated monostatic, full-pol ALOS-2/PALSAR-2 data, the reciprocal symmetry of the received scattering matrix is violated due to FR. Apart from FR, other system parameters like residual system noise, channel amplitude, phase imbalance and cross-talk, also account for the non-symmetry. To correct for the FR effect, firstly the noise correction was performed. PALSAR/PALSAR-2 data was converted into 4×4 covariance matrix to calculate the coherence between cross-polarized elements. Covariance matrix was modified by the coherence factor. For FR corrections, the covariance matrix was converted into 4×4 coherency matrix. The elements of coherency matrix were used to estimate FR angle and correct for FR. Higher mean FR values during ALOS-PALSAR measurements can be seen in regions nearer to the equator and the values gradually decrease with increase in latitude. Moreover, temporal variations in FR can also be noticed over different years (2006-2010), with varying sunspot activities for the Niigata, Japan test site. With increasing sunspot activities expected during ALOS-2/PALSAR-2 observations, more striping effects were observed over Mumbai, India. This data has also been FR corrected, with mean FR values of about 8°, using the above mentioned technique.

Few systematic attempts to interpret the measurements of mm-wave radiometers over clouds and precipitation have been made to date because the scattering signatures of hydrometeors at these frequencies are very difficult to model. The few algorithms that have been developed try to retrieve surface precipitation, to which the observations are partially correlated but not directly sensitive. In fact, over deep clouds, mm-wave radiometers are most sensitive to the scattering from solid hydrometeors within the upper levels of the cloud. In addition, mm-wave radiometers have a definite advantage over the lower-frequency window-channel radiometers in that they have finer resolution and can therefore explicitly resolve deep convection. Preliminary analyses (in particular of NOAA's MHS brightness temperatures, as well as Megha-Tropiques's SAPHIR observations) indicate that the measurements are indeed very sensitive to the depth and intensity of convection. The challenge is to derive a robust approach to make quantitative estimates of the convection, for example the height and depth of the condensed water, directly from the mm-wave observations, as a function of horizontal location. To avoid having to rely on a specific set of microphysical assumptions, this analysis exploits the substantial amount of nearly- simultaneous coincident observations by mm-wave radiometers and orbiting atmospheric profiling radars in order to enforce unbiased consistency between the calculated brightness temperatures and the radar and radiometer observations.

Synthetic Aperture Radar (SAR) is one of the most recent imaging technology to study the forest parameters. The invincible characteristics of microwave acquisition in cloudy regions and night imaging makes it a powerful tool to study dense forest regions. A coherent combination of radar polarimetry and interferometry (PolInSAR) enhances the accuracy of retrieved biophysical parameters. This paper attempts to address the issue of estimation of forest structural information caused due to instability of radar platforms through simulation of SAR image. The Terai Central Forest region situated at Haldwani area in Uttarakhand state of India was chosen as the study area. The system characteristics of PolInSAR dataset of Radarsat-2 SAR sensor was used for simulation process. Geometric and system specifications like platform altitude, center frequency, mean incidence angle, azimuth and range resolution were taken from metadata. From the field data it was observed that average tree height and forest stand density were 25 m and 300 stems/ha respectively. The obtained simulated results were compared with the sensor acquired master and slave intensity images. It was analyzed that for co-polarized horizontal component (HH), the mean values of simulated and real master image had a difference of 0.3645 with standard deviation of 0.63. Cross-polarized (HV) channel showed better results with mean difference of 0.06 and standard deviation of 0.1 while co-polarized vertical component (VV) did not show similar values. In case of HV polarization, mean variation between simulated and real slave images was found to be the least. Since cross-polarized channel is more sensitive to vegetation feature therefore better simulated results were obtained for this channel. Further the simulated images were processed using PolInSAR inversion modelling approach using three different techniques DEM differencing, Coherence Amplitude Inversion and Random Volume over Ground Inversion. DEM differencing technique calculates tree height by generating Digital Elevation Models (DEM) from interferograms in different polarizations and differences in DEM estimates the vegetation height. In CAI technique the phase of coherence is ignored and volume scattering is mainly considered for estimating height. The RVoG model considers both vegetation layer and ground interactions. In this model, the vertical distribution of scatterers do not change with the change in polarization. It was found that with vertical wavenumber values between 0.2113 to .2249 rad/m for mean incidence angle 34.226 degrees the range of tree height achieved by Coherence Amplitude Inversion and RVoG was better among the three inversion techniques.

Forest biomass acts as a backbone in regulating the climate by storing carbon within itself. Thus the assessment of forest biomass is crucial in understanding the dynamics of the environment. Traditionally the destructive methods were adopted for the assessment of biomass which were further advanced to the non-destructive methods. The allometric equations developed by destructive methods were further used in non-destructive methods for the assessment, but they were mostly applied for woody/commercial timber species. However now days Remote Sensing data are primarily used for the biomass geospatial pattern assessment. The Optical Remote Sensing data (Landsat8, LISS III, etc.) are being used very successfully for the estimation of above ground biomass (AGB). However optical data is not suitable for all atmospheric/environmental conditions, because it can’t penetrate through clouds and haze. Thus Radar data is one of the alternate possible ways to acquire data in all-weather conditions irrespective of weather and light. The paper examines the potential of ALOS PALSAR L-band dual polarisation data for the estimation of AGB in the Corbett Tiger Reserve (CTR) covering an area of 889 km2. The main focus of this study is to explore the accuracy of Polarimetric Scattering Model (Extended Water Cloud Model (EWCM) with respect to Backscatter model in the assessment of AGB. The parameters of the EWCM were estimated using the decomposition components (Raney Decomposition) and the plot level information. The above ground biomass in the CTR ranges from 9.6 t/ha to 322.6 t/ha.

Experimentations with applications of machine learning algorithms such as random forest (RF), support vector machines (SVM) and fuzzy inference system (FIS) to lithological classification of multispectral datasets are described. The input dataset such as LANDSAT-8 and Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) in conjunction with Shuttle Radar Topography Mission (SRTM) digital elevation are used. The training data included image pixels with known lithoclasses as well as the laboratory spectra of field samples of the major lithoclasses. The study area is a part of Ajmer and Pali Districts, Western Rajasthan, India. The main lithoclasses exposed in the area are amphibolite, granite, calc-silicates, mica-schist, pegmatite and carbonates. In a parallel implementation, spectral parameters derived from the continuum-removed laboratory spectra of the field samples (e.g., band depth) were used in spectral matching algorithms to generate geological maps from the LANDSAT-8 and ASTER data. The classification results indicate that, as compared to the SVM, the RF algorithm provides higher accuracy for the minority class, while for the rest of the classes the two algorithms are comparable. The RF algorithm effectively deals with outliers and also ranks the input spectral bands based on their importance in classification. The FIS approach provides an efficient expert-driven system for lithological classification. It based on matching the image spectral features with the absorption features of the laboratory spectra of the field samples, and returns comparable results for some lithoclasses. The study also establishes spectral parameters of amphibolite, granite, calc-silicates, mica-schist, pegmatite and carbonates that can be used in generating geological maps from multispectral data using spectral matching algorithms.

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.

Only the displacement along the radar line of sight can be got in Ground Based Synthetic Aperture Radar (GBSAR). In order to extract high-precision three-dimensional displacement field of research area, in this article, we research deeply the method which integrates both three-dimensional laser scanning and GBSAR techniques. It is proved that high precision three-dimensional displacement field information can be extracted with this method through analyzing case and assessing the accuracy of three-dimensional displacement field. The method has a good practical value.

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.

Floods are suddenly and temporary natural events, affecting areas which are not normally covered by water. The influence of floods plays a significant role both in society and the natural environment, therefore flood mapping is crucial. Remote sensing data can be used to develop flood map in an efficient and effective way. This work is focused on expansion of water bodies overtopping natural levees of the river Evros, invading the surroundings areas and converting them in flooded. Different techniques of flood mapping were used using data from active and passive remote sensing sensors like Sentinlel-1 and Landsat-8 respectively. Space borne pairs obtained from Sentinel-1 were processed in this study. Each pair included an image during the flood, which is called “crisis image” and another one before the event, which is called “archived image”. Both images covering the same area were processed producing a map, which shows the spread of the flood. Multispectral data From Landsat-8 were also processed in order to detect and map the flooded areas. Different image processing techniques were applied and the results were compared to the respective results of the radar data processing.

In remote sensing, the change detection topic represents a broad field of research. If time series data is available, change detection can be used for monitoring applications. These applications require regular image acquisitions at identical time of day along a defined period. Focusing on remote sensing sensors, radar is especially well-capable for applications requiring regularity, since it is independent from most weather and atmospheric influences. Furthermore, regarding the image acquisitions, the time of day plays no role due to the independence from daylight. Since 2007, the German SAR (Synthetic Aperture Radar) satellite TerraSAR-X (TSX) permits the acquisition of high resolution radar images capable for the analysis of dense built-up areas. In a former study, we presented the change analysis of the Stuttgart (Germany) airport. The aim of this study is the categorization of detected changes in the time series. This categorization is motivated by the fact that it is a poor statement only to describe where and when a specific area has changed. At least as important is the statement about what has caused the change. The focus is set on the analysis of so-called high activity areas (HAA) representing areas changing at least four times along the investigated period. As first step for categorizing these HAAs, the matching HAA changes (blobs) have to be identified. Afterwards, operating in this object-based blob level, several features are extracted which comprise shape-based, radiometric, statistic, morphological values and one context feature basing on a segmentation of the HAAs. This segmentation builds on the morphological differential attribute profiles (DAPs). Seven context classes are established: Urban, infrastructure, rural stable, rural unstable, natural, water and unclassified. A specific HA blob is assigned to one of these classes analyzing the CovAmCoh time series signature of the surrounding segments. In combination, also surrounding GIS information is included to verify the CovAmCoh based context assignment. In this paper, the focus is set on the features extracted for a later change categorization procedure.

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.