ALBERT

Description: The L-band passive and active microwave geophysical model functions (GMFs) of ocean surface winds from the Aquarius data are derived. The matchups of Aquarius data with the Special Sensor Microwave Imager (SSM/I) and National Centers for Environmental Prediction (NCEP) winds were performed and were binned as a function of wind speed and direction. The radar HH GMF is in good agreement with the PALSAR GMF. For wind speeds above 10 $hbox{m}cdothbox{s}^{-1}$ , the L-band ocean backscatter shows positive upwind–crosswind (UC) asymmetry; however, the UC asymmetry becomes negative between about 3 and 8 $hbox{m}cdothbox{s}^{-1}$ . The negative UC (NUC) asymmetry has not been observed in higher frequency (above C-band) GMFs for ASCAT or QuikSCAT. Unexpectedly, the NUC symmetry also appears in the L-band radiometer data. We find direction dependence in the Aquarius $T_{rm BV}$ , $T_{rm BH}$ , and third Stokes data with peak-to-peak modulations increasing from about a few tenths to 2 K in the range of 10–25- $hbox{m}cdothbox{s}^{-1}$ wind speed. The validity of the GMFs is tested through application to wind and salinity retrieval from Aquarius data using the combined active–passive algorithm. Error assessment using the triple collocation analyses of SSM/I, NCEP, and Aquarius winds indicates that the retrieved Aquarius wind speed accuracy is excellent, with a random error of about 0.75 $hbox{m}cdothbox{s}^{-1}$ . The wind direction retrievals also appear reasonable and accurate above 10 $hbox{m}cdothbo- {s}^{-1}$ . The results of the error analysis indicate that the uncertainty of the GMFs for the wind speed correction of vertically polarized brightness temperatures is about 0.14 K for wind speed up to 10 $hbox{m}cdothbox{s}^{-1}$ .

Description: The local oscillators (LOs) of the Soil Moisture and Ocean Salinity mission payload are used to shift the operating frequency of the 72 receivers to an optimal intermediate frequency needed for the signal processing. The LO temperature variations produce phase errors in the visibility, which result in a blurring of the reconstructed brightness temperature (Tb) image. At the end of the commissioning phase, it was decided to calibrate the LO every 10 min while waiting for a more in-depth analysis. During short periods of time, the LO calibration has been performed every 2 min to assess the impact of a higher calibration rate on the quality of the data. In this paper, by means of a decimation experiment, the relative errors of 6- and 10-min calibration interval data sets are estimated using the 2 min as a reference. A noticeable systematic across- and along-track pattern of amplitude $pm$ 0.3 K is observed for Tb differences between 10 and 2 min, whereas this is reduced between 6 and 2 min. A simulation experiment confirms that the nature of such systematic pattern is due to the visibility phase errors induced by the LO calibration rate. Such pattern is propagated into the sea surface salinity (SSS) retrievals. Overall, the SSS error increase (relative to the 2 min SSS data) is about 0.39 and 0.14 psu for the 10- and 6-min data sets, respectively. This paper shows that a LO calibration rate of at least 6 min would noticeably improve the SSS retrievals.

Description: Radiometric measurements could provide continuous information about atmospheric conditions. In this paper, a sky status indicator (SSI) is proposed as a real-time recognition criterion for the detection, in particular, of the presence of rain events along the propagation path. The computation of the SSI is based on ground-based brightness temperature measurements, at 23.8 and 31.4 GHz, collected in Cabauw, Netherlands, in 2009 by the ESA Atmospheric Propagation and Profiling System (ATPROP) multichannel radiometer. A validation analysis is carried out between simulated data, which are computed by applying the radiative transfer equation to a database of radiosonde profiles collected in De Bilt, Netherlands, by the Royal Netherlands Meteorological Institute, and two data sets of radiometric observations at two elevation angles ( $theta$ equal to 90 $^{circ}$ and $theta$ equal to 69.6 $^{circ}$ ). The analysis based on SSI probability distribution functions has allowed for calculation of the boundary threshold values that are able to discriminate the status of the sky. Furthermore, performances of the SSI were validated against rainfall measurements collected at the ground by a rain gauge located near the ESA ATPROP multichannel radiometer.

Description: A new cloud dynamics and radiation database (CDRD) precipitation retrieval algorithm for satellite passive microwave (PMW) radiometer measurements has been developed. It represents a modification to and an improvement upon the conventional cloud radiation database (CRD) algorithms, which have always been prone to ambiguity. This part 2 paper of a series describes the methodology of the algorithm and the modeling verification analysis involved in creating a synthetic CDRD database for the Europe/Mediterranean basin region. This is followed by a proof-of-concept analysis, which demonstrates that the underlying CDRD theory based on use of meteorological parameters for reducing retrieval ambiguity is valid. This paper uses a regional/mesoscale model, applied in cloud resolving model (CRM) mode, to produce a large set of numerical simulations of precipitating storms and extended precipitating systems. The simulations are used for selection of millions of meteorological/microphysical vertical profiles within which surface rainfall is identified. For each of these profiles, top-of-atmosphere brightness temperature (TB) vectors are calculated (the vector dimension associated with the number of relevant cm–mm wavelengths and polarizations), based on an elaborate radiative-transfer equation (RTE) model system (RMS) coupled to the CRM. This entire body of simulation information is organized into the CDRD database, then used as a priori knowledge to guide a physical Bayesian retrieval algorithm in obtaining rainfall and associated precipitation parameters from the PMW satellite observations. We first prove the physical validity of our CRM-RMS simulations, by showing that the simulated TBs are in close agreement with observations. Agreement is demonstrated using dual-channel-frequency TB manifold sections, which quantify the degree of overlap between the simulated and observed TBs extracted from the full manifolds. Nevertheless, the salient result of this paper is a pro- f that the underlying CDRD theory is valid, found by combining subdivisions of the invoked meteorological parameter ranges of values and showing that such meteorological partitioning associates itself with distinct microphysical profiles. It is then shown that these profiles give rise to similar TB vectors, proving the existence of ambiguity in a CRD-type algorithm. Finally, we show that the CDRD methodology provides significant improvements in reducing retrieval ambiguity and retrieval error, especially for land surface backgrounds where contrasts are typically small between the rainfall TB signatures and surface emission signatures.

Description: The results of remote sensing temperature profiles measurements within a 0–600-m altitude range and total water content measurements during total (Kislovodsk, 2006; Novosibirsk, 2008) and partial (Moscow, 2011) solar eclipses, using microwave radiometers are presented. Initially, continuous data on temperature profiles are obtained at different altitudes before, during, and after total solar eclipses, using two single channel elevation scanning microwave temperature profilers. Terrestrial consequences of solar eclipses (especially total ones) are quite noticeable and important. Solar eclipses support unique, specific conditions, which gives the opportunity for various meteorological research. The most important indicator of thermodynamic processes occurring during solar eclipses is air temperature at different altitudes in the atmospheric boundary layer (ABL). The ABL temperature depends, in general, on the flux of solar radiation and some features of the ground (albedo, absorptivity, and emissivity) and the air (humidity). Temperature profile measurements are accompanied by solar radiation (with net-radiometer) and total water vapor (with microwave radiometers) measurements. The observation results of this paper will contribute detailed model calculations for clarifying meteorological effects of solar eclipses. Observations of the next total solar eclipse over Russia (August 12, 2026) can be used to verify our observational results.

Description: Snow grain size is the snowpack parameter that most affects the microwave snow emission. The specific surface area (SSA) of snow is a metric that allows rapid and reproducible field measurements and that well represents the grain size. However, this metric cannot be used directly in microwave snow emission models (MSEMs). The aim of this paper is to evaluate the suitability and the adaptations required for using the SSA in two MSEMs, i.e., the Dense Media Radiative Theory-Multilayer model (DMRT-ML) and the Helsinki University of Technology model (HUT n-layer), based on in situ radiometric measurements. Measurements of the SSA, using snow reflectance in the short-wave infrared, were taken at 20 snowpits in various environments (e.g., grass, tundra, and dry fen). The results show that both models required a scaling factor for the SSA values to minimize the root-mean-square error between the measured and simulated brightness temperatures. For DMRT-ML, the need for a scaling factor is likely due to the oversimplified representation of snow as spheres of ice with a uniform radius. We hypothesize that the need for a scaling factor is related to the grain size distribution of snow and the stickiness between grains. For HUT n-layer, using the SSA underestimates the attenuation by snow, particularly for snowpacks with a significant amount of depth hoar. This paper provides a reliable description of the grain size for DMRT-ML, which is of particular interest for the assimilation of satellite passive microwave data in snow models.

Description: Ice lens formation, which follows rain on snow events or melt-refreeze cycles in winter and spring, is likely to become more frequent as a result of increasing mean winter temperatures at high latitudes. These ice lenses significantly affect the microwave scattering and emission properties, and hence snow brightness temperatures that are widely used to monitor snow cover properties from space. To understand and interpret the spaceborne microwave signal, the modeling of these phenomena needs improvement. This paper shows the effects and sensitivity of ice lenses on simulated brightness temperatures using the microwave emission model of layered snowpacks coupled to a soil emission model at 19 and 37 GHz in both horizontal and vertical polarizations. Results when considering pure ice lenses show an improvement of 20.5 K of the root mean square error between the simulated and measured brightness temperature (Tb) using several in situ data sets acquired during field campaigns across Canada. The modeled Tbs are found to be highly sensitive to the vertical location of ice lenses within the snowpack.

Description: In recent years, there has been growing interest on the part of the remote sensing community in using the Antarctic area for calibrating and validating data of low-frequency satellite-borne microwave radiometers. In particular, the East Antarctic Plateau appears to be suited for this purpose. The reasons for this interest are the size, structure, spatial homogeneity, and thermal stability of this area. This is particularly interesting for low-frequency microwave radiometers since, due to the low extinction of dry snow, the upper ice-sheet layer is almost transparent and the brightness temperature variability is therefore extremely small. In the context of calibration and validation activities of the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) satellite, an experiment called DOMEX-2, which included radiometric L-band measurements, was carried out at the Italian–French base of Concordia located at Dome C in the East Antarctic Plateau from December 2008 to December 2010. Ground measurements (i.e., snow temperature at different depths, snow structure, meteorological data, etc.) were also collected during the experiment. This paper presents information on the experimental campaign, the characteristics of the radiometric measurements, and the main results. A comparison with SMOS data is also presented.

Description: The 12th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment (MicroRad 2012) was held at Villa Mondragone, University of Rome "Tor Vergata," near Frascati, Italy, on March 5-9, 2012. The objective of MicroRad 2012 was to provide an open forum to report and discuss recent advances in the field of microwave radiometry, particularly with application to remote sensing of the environment. The meeting was highly successful, with more than 120 attendees representing 20 countries. There were 76 oral presentations and more than 40 posters. From the papers presented at MicroRad 2012 and others submitted specifically for this special issue, 12 were selected for inclusion in the special issue. The papers were carefully peer reviewed with the usual standards of the IEEE TGRS. As is evident from the table of contents, these papers span a broad range of microwave radiometry and remote sensing applications and reflect the interest in MicroRad and the vitality of research in this area.

Description: Vicarious cold calibration in the frequency range of 85–92 GHz is analyzed. Vicarious cold calibration cannot be applied at these frequencies as easily as at lower frequencies due to greater sensitivity to water vapor and hydrometeor scattering. The effects of that sensitivity are mitigated by selective filtering of the high-frequency brightness temperatures (TBs) to remove those data where large amounts of water vapor and/or hydrometeor scattering are present. Potential filtering algorithms are presented, and the performance of each with respect to vicarious cold calibration TB stability is characterized. A scattering-based precipitation filter that utilizes a combination of both the lower frequencies from 19 to 37 GHz and the frequencies from 85 to 92 GHz is shown to be the most effective and easily implemented filter. For horizontal polarization, the theoretical minimum TB at the higher frequencies occurs at an unphysically high sea surface temperature (SST), which makes the vicarious cold statistic more sensitive to the population of actual SST values as well as the higher amounts of water vapor associated with warm SSTs. The statistic is stabilized in this case by considering the difference between observed and simulated vicarious cold TBs. Intercalibration between two radiometers using the vicarious cold calibration double difference method at high frequencies is shown to be greatly improved when using the precipitation filter.

Description: After 2.5 years of the Soil Moisture and Ocean Salinity (SMOS) mission, the characterization of residual instrumental systematic errors in the measured brightness temperatures $(T_{B})$ is still rather poor. This, in turn, negatively impacts the sea surface salinity retrievals and, as such, notably limits the mission's success. The error mitigation methodology currently used operationally, the so-called Ocean Target Transformation (OTT), mixes both instrumental and model-induced errors. In this paper, it is proposed to distinguish errors by their type of impact on the $T_{B}$ images: mean brightness level, incidence angle dependence, and azimuth angle dependence. A new approach to characterize the azimuth-dependent errors is proposed. First, a careful data selection strategy is applied. Then, an empirically fitted model, which only accounts for the $T_{B}$ incidence angle dependence, is subtracted from the mean $T_{B}$ images of the selected data sets to estimate the systematic antenna-frame errors. The robustness of this methodology is assessed through the estimated anomaly pattern stability when computed for different geophysical conditions, periods of time, and latitudinal bands. The residual variability ranges from 0.03 K to 0.14 K, whereas the OTT variability is about 0.5 K. The new method is forward model independent and generic. It can therefore be applied to estimate the antenna-frame systematic errors over land and ice. Moreover, it proves to be very effective in separating different sources of error and can therefore be used to further characterize other error components and improve the various SMOS forward model terms.

Description: Terrain Observation by Progressive Scans (TOPS) synthetic aperture radar (SAR) and spotlight SAR are advanced SAR imaging modes for wide range swath and high resolution. In order to obtain a wider range coverage, azimuth multichannel is introduced in the literature. Since the azimuth bandwidth of beam steering SAR (BS-SAR; spotlight SAR, sliding spotlight SAR, or TOPS SAR) is much greater than that of a stripmap SAR, a signal reconstruction algorithm used for multichannel stripmap SAR may not be effective for multichannel BS-SAR. In this paper, a multichannel full-aperture azimuth processing algorithm is proposed for a BS-SAR. The key of this algorithm lies in the beam and the azimuth bandwidth compressions of multichannel signals in the Doppler–array and slow time–angle planes, respectively. Through compression processing, the beamwidth and the azimuth bandwidth are smaller than the available angle and equivalent pulse repeating frequency , respectively. Then, an improved post-Doppler STAP method is proposed to recover a 2-D spectrum. With the recovered signal, further processing can be utilized to focus the multichannel signal. Simulation and real data results show the effectiveness of the proposed algorithm.

Description: Remote sensing image fusion can integrate the spatial detail of panchromatic (PAN) image and the spectral information of a low-resolution multispectral (MS) image to produce a fused MS image with high spatial resolution. In this paper, a remote sensing image fusion method is proposed with sparse representations over learned dictionaries. The dictionaries for PAN image and low-resolution MS image are learned from the source images adaptively. Furthermore, a novel strategy is designed to construct the dictionary for unknown high-resolution MS images without training set, which can make our proposed method more practical. The sparse coefficients of the PAN image and low-resolution MS image are sought by the orthogonal matching pursuit algorithm. Then, the fused high-resolution MS image is calculated by combining the obtained sparse coefficients and the dictionary for the high-resolution MS image. By comparing with six well-known methods in terms of several universal quality evaluation indexes with or without references, the simulated and real experimental results on QuickBird and IKONOS images demonstrate the superiority of our method.

Description: The multichromatic analysis (MCA) uses interferometric pairs of SAR images processed at range subbands and explores the phase trend of each pixel as a function of the different central carrier frequencies to infer absolute optical path difference. This approach allows retrieving unambiguous height information on selected pixels, potentially solving the problem of spatial phase unwrapping, which is instead critical in the standard monochromatic processing. The method, based on concepts originally introduced by Madsen and Zebker, has been developed in previous work both theoretically and through simulations. This paper presents the first MCA experimental validation of the procedure, through application to a wideband SAR single-pass interferometric data set acquired by the AES-1 airborne sensor. An evaluation of the impact of the MCA processing parameters on the height estimation performances is obtained through a parametric analysis. The results confirm the indications derived by the theoretical analysis, demonstrating the feasibility of the MCA absolute phase measurement, provided that a sufficient bandwidth is available.

Description: This paper proposes a new semisupervised dimension reduction (DR) algorithm based on a discriminative locally enhanced alignment technique. The proposed DR method has two aims: to maximize the distance between different classes according to the separability of pairwise samples and, at the same time, to preserve the intrinsic geometric structure of the data by the use of both labeled and unlabeled samples. Furthermore, two key problems determining the performance of semisupervised methods are discussed in this paper. The first problem is the proper selection of the unlabeled sample set; the second problem is the accurate measurement of the similarity between samples. In this paper, multilevel segmentation results are employed to solve these problems. Experiments with extensive hyperspectral image data sets showed that the proposed algorithm is notably superior to other state-of-the-art dimensionality reduction methods for hyperspectral image classification.

Description: This paper presents a new framework for the development of generalized composite kernel machines for hyperspectral image classification. We construct a new family of generalized composite kernels which exhibit great flexibility when combining the spectral and the spatial information contained in the hyperspectral data, without any weight parameters. The classifier adopted in this work is the multinomial logistic regression, and the spatial information is modeled from extended multiattribute profiles. In order to illustrate the good performance of the proposed framework, support vector machines are also used for evaluation purposes. Our experimental results with real hyperspectral images collected by the National Aeronautics and Space Administration Jet Propulsion Laboratory's Airborne Visible/Infrared Imaging Spectrometer and the Reflective Optics Spectrographic Imaging System indicate that the proposed framework leads to state-of-the-art classification performance in complex analysis scenarios.

Description: For typical scanning microwave radiometers, a significant source of calibration error arises from thermal gradients on the hot load. Even when direct or reflected solar illumination is blocked, hot load gradients arise from thermal coupling between the target and the surface facing the target which is heated and cooled as the instrument orbits the earth. For the GlobalL Precipitation Measurement (GPM) Microwave Imager (GMI), a rotating metal annular ring called the “hot load tray” serves to guard the hot load against solar intrusion, and is the surface immediately facing the hot load during the majority of the scan. The planned GMI calibration algorithm corrects for the target gradients induced by thermal coupling between the hot load tray and hot load. The correction uses an empirically derived relationship between the target gradient and the temperature differential between the target and the tray. The correction is derived using target-level and GMI system-level calibration testing. The dual calibration of GMI, in connection with thermal vacuum calibration measurements, is a key aid to determining and correcting the hot load gradients.

Description: Satellite images have long been used to study surface manifestations of internal waves (IWs). More recently, marine X-band radar data have been employed to retrieve IW packet parameters. Marine radars have the advantage over satellite systems that their high temporal resolution enables the study of the IW evolution. Until today, no method to automatically detect IW surface signatures in marine radar data has been suggested. In this paper, we present a new fully automated tool to retrieve IW signatures from marine radar image sequences. First, after various preprocessing steps, the IW packet velocity is determined using a combination of localized Radon transform and cross-correlation techniques. Temporal averaging of the marine radar data significantly enhances the IW signatures. The knowledge of the IW packet velocity is used to correct for the IW motion, enabling us to extend the averaging period, which further enhances the IW signal. An IW-motion correction is necessary because, otherwise, the IW signal would become smeared if the averaging period were much longer than the time it takes the IW to propagate between radar resolution cells. The IW-enhanced images are then utilized for the IW signature analysis. Here, we identify local backscatter peaks and exploit the marine radar's high temporal resolution to distinguish signal from noise. The resulting series of IW soliton maps provides information on changes in soliton wavelength, velocity, and backscatter intensity. Our marine radar IW signature analysis tool therefore offers a great opportunity of studying the spatiotemporal evolution of IWs as they grow and decay.

Description: The potential of satellite passive microwave sensors to provide quantitative information about near-source volcanic ash cloud parameters is assessed. To this aim, ground-based microwave weather radar and spaceborne microwave radiometer observations are used together with forward-model simulations. The latter are based on 2-D simulations with the numerical plume model Active Tracer High-Resolution Atmospheric Model (ATHAM), in conjunction with the radiative transfer model Satellite Data Simulator Unit (SDSU) that is based on the deltaEddington approximation and includes Mie scattering. The study area is the Icelandic subglacial volcanic region. The analyzed case study is that of the Grímsvötn eruption in May 2011. ATHAM input parameters are adjusted using available ground data, and sensitivity tests are conducted to investigate the observed brightness temperatures and their variance. The tests are based on the variation of environmental conditions like the terrain emissivity, water vapor, and ice in the volcanic plume. Quantitative correlation analysis between ATHAM/SDSU forward-model columnar content simulations and available microwave radiometric brightness temperature measurements, derived from the Special Sensor Microwave Imager/Sounder (SSMIS), are encouraging in terms of both dynamic range and correlation coefficient. The correlation coefficients are found to vary from $-$ 0.37 to $-$ 0.63 for SSMIS channels from 91 to 183 $pm$ 1 GHz, respectively. The larger sensitivity of the brightness temperature at 183 $pm$ 1 GHz to the columnar content, with respect to other channels, allowed us to consider this channel as the basis for a model-based polynomial relationship of volcanic plume hei- ht as a function of the measured SSMIS brightness temperature.

Description: Bright curvilinear features arising from the geometry of man-made structures are characteristic of synthetic aperture radar (SAR) images of urban areas, particularly due to double-reflection mechanisms. An approach to urban earthquake damage detection using double-reflection line amplitude change in single-look images has been established in previous literature. Based on this method, this paper introduces an automated tool for fast, unsupervised damage detection in urban areas. Ridge-based curvilinear features are extracted from a preevent SAR image, and double-reflection candidates are selected using prior probability distributions derived from a simple geometrical building model. The candidate features are then used with the ratio of a pair of single preevent and postevent SAR single-look amplitude images to estimate damage levels. The algorithm is very efficient, with overall computational complexity of $O(Nlog k)$ for an $N$ -pixel image containing features of mean length $k$ . The technique is demonstrated using COSMO-SkyMed data covering L'Aquila, Italy, and Port-au-Prince, Haiti.

Description: It is necessary to measure the sharpness of distributions in many situations. A class of functions is investigated in this paper. First, the relation between this class and sharpness is clarified, and this justifies this class as sharpness measures. Then, we analyze the performance of different sharpness measures and present a guide to select the sharpness measure. In addition, the relation of this class to the sparsity measure is addressed, which leads to a deeper understanding about sparsity. Finally, we show and discuss the application of this class in inverse synthetic aperture radar imaging.

Description: A detection of radio-frequency interference (RFI) in the space-borne microwave radiometer data is difficult under snow and sea ice-covered conditions. The existing methods such as a spectral difference technique or a principal component analysis (PCA) of RFI indices produce many false RFI signals near the boundary of Greenland and Antarctic ice sheets. In this paper, a double PCA (DPCA) method is developed for RFI detection over Greenland and Antarctic regions. It is shown that the new DPCA method is effective in detecting RFI signals in the C- and X-band radiometer channels of WindSat while removing the false RFI signals over Greenland and Antarctic. It also worked well in other snow-free or snow-rich regions such as winter data over the United States. The proposed DPCA can be applied to satellite radiometer data orbit-by-orbit or granule-by-granule and is thus applicable in an operational environment for fast processing and data dissemination.

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Description: A super-resolution (SR) method based on compressive sensing (CS), structural self-similarity (SSSIM), and dictionary learning is proposed for reconstructing remote sensing images. This method aims to identify a dictionary that represents high resolution (HR) image patches in a sparse manner. Extra information from similar structures which often exist in remote sensing images can be introduced into the dictionary, thereby enabling an HR image to be reconstructed using the dictionary in the CS framework. We use the K-Singular Value Decomposition method to obtain the dictionary and the orthogonal matching pursuit method to derive sparse representation coefficients. To evaluate the effectiveness of the proposed method, we also define a new SSSIM index, which reflects the extent of SSSIM in an image. The most significant difference between the proposed method and traditional sample-based SR methods is that the proposed method uses only a low-resolution image and its own interpolated image instead of other HR images in a database. We simulate the degradation mechanism of a uniform 2 $times$ 2 blur kernel plus a downsampling by a factor of 2 in our experiments. Comparative experimental results with several image-quality-assessment indexes show that the proposed method performs better in terms of the SR effectivity and time efficiency. In addition, the SSSIM index is strongly positively correlated with the SR quality.

Description: This paper presents a method and experimental results for near-surface wind sensing using reflected Global Navigation Satellite Systems (GNSS) signals received on a spacecraft. The estimation method proposed involves four steps. First, the bistatic radar cross section (BRCS) of the received signal is estimated from the measurements. Second, the BRCS measurements are calibrated to agree with existing theoretical and empirical wind–wave models. Next, a geometric optics-based scattering model is used to estimate the sea surface slopes, based on the reflection geometry and the measured BRCS. Finally, the surface winds are estimated using an empirically derived function relating the surface mean square slopes to near-surface wind speed. The accuracy of the proposed inversion technique is then tested using a set of 25 space-based GNSS reflection measurements over a range of wind speeds. These measurements were all taken in the proximity of ocean buoys which provided in situ ocean wind speed information. The wind estimates from the buoys were then compared with the wind retrievals made from the measurements and found to be accurate to a root-mean-square error of 1.84 m/s. Additionally, the potential error sources in the measurements are analyzed, including a simulation of the effects of wind direction on the BRCS measurements. This first demonstration of space-based GNSS scatterometry using a small set of sample measurements will hopefully provide a benchmark and example for future experiments.

Description: There is an error in the above-named article [ibid.,vol. 51, no. 4, pp. 2119??2127, Apr. 2013] regarding the definition and the implementation of equation (3), defining the proposed temporal smoothing index (TSI). The correct formula is provided. These corrections do not change any of the general conclusions of the paper, but some of the comments regarding the interpretation of this table are revised.

Description: We present a novel method for ground moving target imaging using a synthetic aperture radar system transmitting ultranarrowband continuous waveforms (CW). Our method exploits the high Doppler resolution provided by ultranarrowband CW signals to image both the scene reflectivity and to determine the velocity of multiple moving targets. We develop a new forward model based on the temporal Doppler induced by the movement of antennas and moving targets. The forward model relates reflectivity and velocity information at each location to a correlated received signal. We form the reflectivity images of the moving targets and estimate their motion parameters using a filtered-backprojection (FBP) technique combined with the contrast or gradient optimization method. The method results in focused reflectivity images of moving targets and their velocity estimates, regardless of the target location, speed, and velocity direction. We show that the amplitude and visible edges of the targets can be correctly reconstructed when the correct target velocity estimate is used in the FBP imaging. We present the resolution analysis of the reflectivity images. Extensive numerical simulations demonstrate the performance of our method and validate the theoretical results.

Description: The author has written this short message to introduce himself as the new Editor-in-Chief (EiC) of the IEEE JOURNAL OF OCEANIC ENGINEERING. He took on the role last autumn, following the untimely death of his predecessor, Bill Carey. The new EiC gives an indication of his priorities and plans; as they become fleshed out, he will provide more details in subsequent editorial messages.

Description: This paper presents a novel energy management strategy for a hybridized power source small urban electric vehicle (EV). First, an analysis of load requirements for typical urban driving cycles is presented. Thereafter, the energy and power management issues are addressed for a multisource EV to define an improved management architecture. A dynamically restricted search space strategy coupled with a simulated annealing technique is exploited to accomplish the global optimization of the energy management system (EMS). The control of the dc/dc converter operations based on this EMS is also presented. The multiple sources have been simulated using an overall model implemented in Matlab/Simulink. A reduced-scale prototype has been built to analyze the performance of the energy management strategy. The results obtained show that energy management has been enhanced leading to an increase of the vehicle performance with reduced size embarked sources.

Description: Prediction intervals (PIs) are a promising tool for quantification of uncertainties associated with point forecasts of wind power. However, construction of PIs using parametric methods is questionable, as forecast errors do not follow a standard distribution. This paper proposes a nonparametric method for construction of reliable PIs for neural network (NN) forecasts. A lower upper bound estimation (LUBE) method is adapted for construction of PIs for wind power generation. A new framework is proposed for synthesizing PIs generated using an ensemble of NN models in the LUBE method. This is done to guard against NN performance instability in generating reliable and informative PIs. A validation set is applied for short listing NNs based on the quality of PIs. Then, PIs constructed using filtered NNs are aggregated to obtain combined PIs. Performance of the proposed method is examined using data sets taken from two wind farms in Australia. Simulation results indicate that the quality of combined PIs is significantly superior to the quality of PIs constructed using NN models ranked and filtered by the validation set.

Description: This paper presents a supervisory control unit (SCU) combined with short-term ahead wind speed prediction for proper and effective management of the stored energy in a small capacity flywheel energy storage system (FESS) which is used to mitigate the output power fluctuations of an aggregated wind farm. Wind speed prediction is critical for a wind energy conversion system since it may greatly influence the issues related to effective energy management, dynamic control of wind turbine, and improvement of the overall efficiency of the power generation system. In this study, a wind speed prediction model is developed by artificial neural network (ANN) which has advantages over the conventional prediction schemes including data error tolerance and ease in adaptability. The proposed SCU-based control would help to reduce the size of the energy storage system for minimizing wind power fluctuation taking the advantage of prediction scheme. The model for prediction using ANN is developed in MATLAB/Simulink and interfaced with PSCAD/EMTDC. Effectiveness of the proposed control system is illustrated using real wind speed data in various operating conditions.

Description: Due to its reduced communication overhead and robustness to failures, distributed energy management is of paramount importance in smart grids, especially in microgrids, which feature distributed generation (DG) and distributed storage (DS). Distributed economic dispatch for a microgrid with high renewable energy penetration and demand-side management operating in grid-connected mode is considered in this paper. To address the intrinsically stochastic availability of renewable energy sources (RES), a novel power scheduling approach is introduced. The approach involves the actual renewable energy as well as the energy traded with the main grid, so that the supply–demand balance is maintained. The optimal scheduling strategy minimizes the microgrid net cost, which includes DG and DS costs, utility of dispatchable loads, and worst-case transaction cost stemming from the uncertainty in RES. Leveraging the dual decomposition, the optimization problem formulated is solved in a distributed fashion by the local controllers of DG, DS, and dispatchable loads. Numerical results are reported to corroborate the effectiveness of the novel approach.

Description: The present paper investigates the effect of high penetration of photovoltaic (PV) systems on the small signal stability of a large power system. Reduced system inertia and altered power flow patterns as a result of the addition of the utility scale and residential rooftop PVs that replace a portion of conventional generation resources, may lead to decreased damping of the critical modes of the system. To identify the critical modes of the system and the effect of the high PV penetration on these modes, eigenvalue analysis is carried out on the aforementioned system under various PV penetration levels. To substantiate the results observed from the small signal analysis, transient analysis is carried out on the system under various PV penetration levels. The simulation results effectively identify the impact of high PV penetration on small signal stability of the studied system.

Description: A speed exclusion zone algorithm to prevent the rotor rotational frequency from exciting the side-side tower mode in a variable-speed wind turbine was designed. The algorithm was first tested on a simple wind turbine model developed in Simulink and then implemented and tested on an experimental test rig. Different speed exclusion zone widths were tested to observe how the performance of the wind turbine is affected. Results obtained from both simulations and experiments showed the effectiveness of the speed exclusion zone algorithm in preventing excitation of tower resonance. Results from different speed exclusion zone widths showed that with a wider exclusion zone, the tower mode is excited less at the expense of a larger power loss.

Description: A control strategy for the quasi-Z-source inverter (qZSI) with a battery-based photovoltaic (PV) power conversion system is proposed. A battery-assisted qZSI can buck/boost PV panel voltage by introducing shoot-through states, and make full use of PV power by the energy-stored battery paralleled to the quasi-Z-source capacitor. A dynamic small-signal model of the battery-assisted qZSI is established to design a closed-loop controller for regulating shoot-through duty ratio and managing the battery's energy storage. A modified space vector modulation (SVM) technique for the qZSI is applied to achieve low harmonics, high voltage utilization, and high efficiency. A P-Q decoupled grid-tie power injection is fulfilled with the maximum power capture from PV panels and the unity power factor. The validity of the proposed PV system is proved by experimental results, showing an efficient method for the energy-stored PV power generation.

Description: Doubly-fed induction generator (DFIG)-based wind turbines (WTs) are reported to suffer reliability problems due to the presence of slip-rings, brushes, and the gearbox. These disadvantages encouraged several research groups to investigate the viability of employing single or double-frame brushless cascade DFIGs (BCDFIGs) in grid-connected multimegawatt WTs especially offshore and in solar chimney power plants. In this regard, this paper tackles three main issues. Initially, the expected reduction in gear ratio when BCDFIGs are used instead of DFIGs is examined from a steady state perspective. A reduction would lead to less frequent maintenance and an improved return on investment. Next, a detailed comparison between DFIG-WTs and BCDFIG-WTs under unbalanced grid voltage is presented. Finally, the extent to which a multimegawatt BCDFIG-WT is grid code compliant in terms of fault ride-through capability is studied. This is illustrated by comparing and quantifying the response of a DFIG-WT and a BCDFIG-WT to a severe three-phase voltage dip. Simulation and experimental results indicate promising behavior for BCDFIGs during disturbances.

Description: We analyze the potential benefits of colocating wind and concentrating solar power (CSP) plants in the southwestern U.S. Using a location in western Texas as a case study, we demonstrate that such a deployment strategy can improve the capacity factor of the combined plant and the associated transmission investment. This is because of two synergies between wind and CSP: 1) the negative correlation between real-time wind and solar resource availability and 2) the use of low-cost high-efficiency thermal energy storage in CSP. The economic tradeoff between transmission and system performance is highly sensitive to CSP and transmission costs. We demonstrate that a number of deployment configurations, which include up to 67% CSP, yield a positive net return on investment.

Description: Renewable energy resources, especially wind power, are expected to provide a considerable portion of the world energy requirements in the near future. Large-scale wind power penetration impacts the electricity industry in many aspects and raises a number of technical challenges for the electricity network. A day-ahead network-constrained market clearing formulation is proposed which considers demand side resources. The proposed approach can provide flexible load profile and reduce the need for ramp up/down services by the conventional generators. This method can potentially facilitate a large penetration of wind power by shifting the wind power generation from the off-peak periods to the high-peak hours. The validity of the proposed approach has been verified using the IEEE 30 bus and 57 bus test systems.

Description: The small generation units including cogeneration units and renewables have been widely spread during the last decades. These units raise several questions regarding the power system, e.g., their integration into power system control or their role on the supply-side of smart grids. A multiagent model was established in order to facilitate the investigations of the emerged questions. Several viewpoints are taken into account and built into the model considering technical specifications, proper wind-speed and temperature simulations, constraints of heating service, fuel consumption, legal regulation, outages, and services. A state-based method is elaborated in order to generalize different technologies. The different aspects of operation control are represented by agent strategies. Applying these methods resulted in an easy-to-follow and flexible model. This paper is dealing with the developed multiagent-based model applied to study the distributed generation system from many viewpoints like power system balance, virtual power plant, or smart grid scheduling.

Description: The electromagnetic spectrum is a valued shared resource. Its scientific use allows us to learn about our universe, measure and monitor our planet, and communicate scientific data. The use of the spectrum is managed by national, regional, and global regulatory frameworks. There are increasing demands for new or extended allocations because of vast technological advances in the past few years. Understanding spectrum management is important in the successful planning and execution of missions and instruments, as well as in determining the potential source of radio frequency interference in existing data and instruments, and in working to ameliorate its impact. This paper provides a summary of this framework for radio scientists and engineers.

Description: For highly squinted synthetic aperture radar (SAR) imaging, the wavenumber domain SAR processing algorithm is commonly accepted as an ideal solution to SAR focusing in the case of an ideal straight sensor trajectory. However, airborne SAR is very sensitive to atmospheric turbulence that causes serious trajectory deviations. In this paper, we propose a robust autofocusing approach for highly squinted airborne SAR imagery using the extended wavenumber algorithm, being capable of estimating the range-dependent phase errors. To apply the proposed autofocusing scheme, a detailed analysis of the motion error model in the conical reference system is presented, where the formulation of range-dependent phase errors for squinted SAR is given. The proposed autofocusing approach is performed by a three-step process: 1) referring to the inevitable residual phase after deramping for highly squinted SAR, a modified squinted phase gradient autofocusing (SPGA) algorithm is put forward to retrieve the range-independent phase errors; 2) based on the established motion error model, the residual range-dependent phase errors are estimated using a local maximum likelihood-weighted SPGA algorithm; and 3) motion compensation is executed by a two-step approach to reach the range-independent and range-dependent corrections, respectively. Experiments based on measured data have shown that the proposed autofocusing approach performs well for highly squinted SAR imaging.

Description: Hyperspectral radiances from the Infrared Atmospheric Sounding Interferometer (IASI) and Atmospheric Infrared Sounder (AIRS) are used as a reference to improve the calibration accuracy for FengYun-2 (FY-2) infrared (IR) channel radiances. It is shown that the previous FY-2 operational calibration for IR bands produces significant bias in brightness temperatures that can exceed 1.1 K. In particular, the FY-2 IR3 band (6.7 $muhbox{m}$ ) has the largest bias of 2.0 K. The daytime double-difference temperature (DDT) between AIRS and IASI using FY-2 imagers as a transfer medium showed an excellent consistency, is within 0.2 K at 290 K, and is stable over time for FY-2C/2D/2E. This only indicates the robust calibrations applied for both the AIRS and IASI measurements. During the nighttime of the Earth observation, stray light in space affects the long-term stability of the FY-2 DDT, particularly for the Earth scene at 220 K. FY-2E satellite which was launched in 2009 has an instrument design improvement. Intercalibrating FY-2 four times using AIRS and IASI data can reveal the diurnal features of the FY-2 instrument calibration. The temporal DDT appears very large during the spring and autumn eclipse times. Not only can the global-space-based-intercalibration-system intercalibration method provide an excellent operational calibration for the FY-2 imager, but it can also help improve the design of future instruments and onboard blackbody calibration.

Description: Advertisement: Internet television gets a mobile makeover. A mobile version of IEEE.tv is now available for convenient viewing. Plus a new app for IEEE.tv can also be found in your app store.

Description: Compared with traditional remote sensing, multiangular observation provides 3-D structural information of a forest through different directional observations. The MGeoSAIL model, suitable for multiangular observations, was developed based on the single-angle model GeoSAIL. The MGeoSAIL model combines the geometric-optic model with the radiation transfer model and has the advantages of both models. Thus, it is more accurate and feasible. The geometric-optic model calculates the amount of shadowed and illuminated components within a forest scene, while the radiation transfer model [Scattering by Arbitrarily Inclined Leaves (SAIL)] calculates the reflectance and transmittance of tree crowns. The uniform index is introduced to characterize the relationship quantitatively between tree distribution pattern and the bidirectional reflectance distribution function (BRDF). The simulation results show that the MGeoSAIL model could simulate the “hot” spot in red and near-infrared bands, as well as the “bowl” shape in the near-infrared band. The relationship between the uniform index and BRDF is negatively exponential. Finally, the look-up table was calculated using the MGeoSAIL model, and leaf area index (LAI) was inversed from compact high-resolution imaging spectrometry data. The results compared well with the measured LAI in Changbai Mountain area, China.

Description: The trend towards multimegawatt (multi-MW) wind turbines and the increasing interest in direct-drive variable-speed wind energy systems have made multilevel converters a promising candidate for large wind energy conversion systems. This paper presents a new hybrid modular multilevel converter for interfacing a full-scale, permanent magnet synchronous generator (PMSG)-based direct-drive variable-speed wind energy conversion system (WECS). The proposed hybrid converter, which is used on the grid side of the system, consists of a three-level modular multilevel converter (MMC) in series connection with three H-bridge modules. The generator-side converter is based on a conventional three-level neutral-point-clamped converter. The proposed hybrid converter, as opposed to the existing full-scale multilevel converter-based wind energy systems, provides structural modularity and a higher dc-bus voltage utilization. This paper formulates and analyzes the internal dynamics of the proposed hybrid converter including the circulating currents and the capacitor voltage ripples. The ac components of the circulating currents, if not properly reduced, increase the amplitude of the capacitor voltage ripples, rating values of the converter components, and losses. Based on the analysis, closed-loop circulating current and capacitor voltage ripple reduction techniques are developed. The reduction of capacitor voltage ripples help reduce the capacitor value. A mathematical model is also developed for the overall WECS. Performance of the overall WECS, under the proposed multilevel converter-based topology and controls, is evaluated based on time domain simulations in the PSCAD/EMTDC environment.

Description: Electric vehicles (EVs) are likely to have a continued presence in the light-vehicle market in the next few decades. As a result, EV charging will put an extra burden on the distribution grid and adjustments need to be made in some cases. On the other hand, EVs have the potential to support the grid as well. This paper presents a single-phase bidirectional charger topology which pairs up a photovoltaic (PV) source with an EV charger resulting in production cost reduction. The presented topology is then used for vehicle-to-grid (V2G) services. The main focus of this paper is on power quality services which only slightly discharge the battery. Among these services, it studies the possibility of local reactive injection of EVs connected to the grid through a single-phase charger to compensate for voltage drops caused by motor startup or inductive loads. It also studies the possibility of active power injection of EVs for short time periods during PV transients in cloudy weather to keep the system stable. It also studies the potential of EVs to help during low voltage ride-through of the PV sources. The studies are performed using Simulink simulations and a real-time implementation in Real Time Digital Simulator (RTDS). The results demonstrate the effectiveness of power quality V2G services with small wear on the EV battery.

Description: The short-term wind power scenarios have a significant impact on the operation cost and power system reliability due to the stochastic generation scheduling of wind-integrated power systems. In order to obtain the scenarios containing the information of forecast error distribution and fluctuation distribution for short-term wind power, a scenario generation method is proposed. This paper characterizes forecast error via empirical distributions of a set of forecast bins and assumes that wind power fluctuations over unit interval follow $t$ location-scale distribution. An inverse transform sampling from a multivariate normal distribution is adopted to generate a large number of wind power scenarios. The covariance matrix of the multivariate normal distribution is estimated to fit the distribution of historical wind power fluctuations. The proposed scenario generation method is applied to the actual aggregate wind power data in the whole regions of Ireland's Power System. The results indicate that the variability of wind power scenarios can be adjusted by estimating the key range parameter in the exponential covariance structure of a multivariate normal distribution.

Description: Microwave radiometers are very sensitive passive sensors that measure the power of the thermal noise within a determined bandwidth. Therefore, any other signal present in the band modifies the value of the measured power, and the corresponding estimated antenna temperature, from which the geophysical parameters are retrieved. Due to the high sensitivity and accuracy required for these instruments, radio frequency interference (RFI) is becoming more and more a serious problem. On one hand, ground-based or global RFI surveys are helping to understand the occurrence and types of RFI sources. If RFI does not necessarily affect the whole bandwidth, or it is not present during the whole integration time, the application of either frequency blanking, time blanking or signal spectrogram techniques can be applied. However, it would be desirable to apply techniques to estimate the RFI signal so that it can be subtracted from the received signal itself so that some useful measurements are still possible. Such a real-time system is currently being developed for RFI detection and mitigation. This work focuses however in the description and performance of a wavelet-based RFI-mitigation technique implemented in a FPGA hardware back-end. The interfering signal is estimated by using the powerful denoising capabilities of the wavelet transform, and it is then subtracted from the total received signal to obtain a RFI-mitigated noise signal.

Description: The narrow-band interference (NBI) is a common jamming signal against synthetic aperture radar (SAR), which can degrade the imaging quality severely. This paper proposes a new method for NBI suppression in the data domain based on the independent component analysis (ICA). In this method, echoes contaminated by the NBI are identified in the frequency domain. Next, time filtering and whitening are performed to the identified echoes. Then, the ICA is carried out to decompose the echoes into a series of basis signals, and the jamming components are selected by thresholding. Finally, the NBI is reconstructed and subtracted from the echoes, and the well-focused SAR imagery is obtained by conventional imaging methods. The proposed method copes well with the time-varying NBI with little signal loss. Results of simulated and measured data have proved the validity of the proposed method.

Description: The Soil Moisture and Ocean Salinity (SMOS) satellite is strongly affected by radio-frequency interference (RFI). A detection algorithm has been developed to accurately obtain the coordinates of the interfering source emitters from the SMOS images. The results obtained from this detection algorithm are regularly used to locate the on-ground sources of interference. This has allowed the identification and termination of over 200 RFI sources observed by SMOS. In the majority of cases, the accuracy of the coordinates provided was better than 4 km, which is a very important achievement considering that SMOS spatial resolution is larger than 35 km and that the contamination of a single RFI can extend to several thousands of kilometers in some cases.

Description: The Soil Moisture and Ocean Salinity (SMOS) radiometer operates within the Earth Exploration Satellite Service passive band at 1400–1427 MHz. Since its launch in November 2009, SMOS images are strongly impacted by radio frequency interference (RFI). So far ${>}{500}$ RFI sources distributed worldwide have been detected. Up to 42% of these RFIs could be suppressed thanks to the co-operation of the National Spectrum Management Authorities. Some of the strongest RFI sources might mask other weaker sources underneath, hence it is expected the total number of RFI detected may increase as strong ones are progressively identified and switched off. Most RFIs are located in Asia and Europe, which together hold ${sim}{73%}$ of the active sources and ${>}{90%}$ of the strongest interference. The areas affected by RFI may experience either an underestimation in the retrieved values of soil moisture and ocean salinity or data loss, with the associated detrimental impact on the scientific return. ESA and the teams participating in SMOS mission have put in place different strategies to alleviate this RFI situation.

Description: Radio-frequency interference (RFI) is probably today's most serious limitation to the accurate retrieval of geophysical parameters from microwave radiometric measurements. Strong RFI inducing a change in the detected power larger than the natural variability is simple to detect. Moderate or weak RFI can be masked by the natural variability of the measurements, passing undetected and corrupting them. A number of techniques have been devised in the past years to detect and, eventually, mitigate RFI present in microwave radiometry measurements: 1) time domain; 2) frequency domain; 3) spectrogram techniques looking for anomalously high power peaks; 4) statistical techniques testing the hypothesis of Gaussianity of the received signal; 5) polarimetric techniques looking for anomalous signatures in the third and fourth Stokes parameters; or 6) wavelet techniques to estimate the RFI signal and cancel it (if any). In this paper, the first four techniques are evaluated with real data gathered with a multifrequency microwave radiometer. It will be shown how spectrogram techniques can detect RFI signals concentrated in narrow frequency bands and/or time intervals that may pass undetected with time-domain and/or frequency-domain techniques alone or with statistical methods. A combined approach is proposed to take advantage of the best performance of each technique. On one side, for strong localized RFI, the approach is spectrogram blanking or, if it is too demanding in terms of computational resources, simple time- and frequency-domain blanking. On the other side, for weak RFI, the approach is the Kurtosis statistical test, which exhibits the best performance among the ten normality tests evaluated, in conjunction with the Anderson–Darling test to detect potential RFI in the blind spots of the Kurtosis test.

Description: The Soil Moisture Active–Passive (SMAP) mission will launch in late 2014 and will carry a combined L-band radiometer/radar instrument for the retrieval of global soil moisture and surface freeze-thaw state. Radio frequency interference (RFI) is a known challenge for Earth remote sensing in the L-band portion of the spectrum. This paper addresses efforts to characterize and mitigate RFI for the SMAP radar. A model for the RFI environment due to surface-based emitters is developed, and is shown to agree well with the observations of currently operating L-band radar systems. An analysis of the environment due to space-based emitters is also presented. Techniques to mitigate RFI in the radar band are described, and are shown to perform sufficiently well to meet the stringent SMAP measurement requirements. A companion paper addresses the different issues encountered with RFI in the radiometer band.

Description: Radio frequency interference (RFI) is a known issue in low-frequency radar remote sensing. In synthetic aperture radar (SAR) image processing, RFI can cause severe degradation of image quality, distortion of polarimetric signatures, and an increase of the SAR phase noise level. To address this issue, a processing system was developed that is capable of reliably detecting, characterizing, and mitigating RFI signatures in SAR observations. In addition to being the basis for image correction, the robust RFI-detection algorithms developed in this paper are used to retrieve a wealth of RFI-related information that allows for mapping, characterizing, and classifying RFI signatures across large spatial scales. The extracted RFI information is expected to be valuable input for SAR-system design, sensor operations, and the development of effective RFI-mitigation strategies. The concepts of RFI detection, analysis, and mapping are outlined. Large-scale RFI mapping results are shown. In case studies, the benefit of detailed RFI information for customized RFI filtering and sensor operations is exemplified.

Description: Radio-frequency interference (RFI) is a persistent threat to Earth-observing microwave radiometers. A number of test statistics are used for radiometric RFI detection. This paper presents a new RFI detection method that uses the information theoretic quantity known as negentropy. In particular, we study six negentropy-based test statistics and compare their performance against kurtosis, Jarque–Bera, Anderson–Darling, and Shapiro–Wilk normality tests for specific RFI signal models. The Neyman–Pearson decision rule is used to develop receiver operating characteristic curves for each test statistic. We show that although negentropy can be used to detect RFI, it does not outperform kurtosis, except for the kurtosis blind-spot case.

Description: Measurements of both the Soil Moisture and Ocean Salinity (SMOS) and Aquarius L-band microwave radiometers show a significant presence of radio frequency interference (RFI), although they operate in a protected frequency band where transmission is prohibited. RFI detection and mitigation remain a challenging problem for both missions, especially for low or moderate (i.e., on the order of 10 K or less) amplitude contributions. An algorithm for low-level source detection and mitigation is already included in Aquarius data sets, and both Aquarius and SMOS have distinct attributes that can potentially enable further improvements in detection and mitigation of these sources to some degree. The combination of SMOS and Aquarius data sets may enable further future improvements as well. Initial efforts toward this goal are reported in this paper. Similarities and differences in RFI effects on SMOS and Aquarius are examined, with a particular focus on instrument properties that cause differences in received RFI power in SMOS and Aquarius observations of a specific source. A study is also performed of SMOS observations for regions reported by Aquarius to contain “low-level” RFI. It is shown that the detection of these sources in the SMOS data set is challenging and that the dependence of the SMOS third and fourth Stokes parameters on incidence angle makes the polarimetric features of SMOS difficult to utilize for low-level source detection. However, an angular fitting procedure suggested previously in the literature can, in some cases, detect such sources in horizontal and vertical polarizations.

Description: This paper proposes an extended nonlinear chirp scaling (CS) image formation algorithm for the bistatic synthetic aperture radar systems with the squinted transmitter and a fixed receiver. Since the transmitter with the squint mode was adopted in the system, two main problems, i.e., the spatial variance of the frequency-modulation rate and cubic phase terms, were introduced in the image formation algorithm. The former problem was solved by the linearity approximation of parameter $p$ and deduced $q$ (the second- and third-order coefficients of CS factors in range, which could be used to remove the spatial variation and high-order phase in the range direction) along the range domain while the latter one was compensated by a cubic analytical phase term in the frequency domain. A corresponding experimental hardware system and the bistatic experiments were also described in this paper. Both the simulation and experimental results validated the proposed algorithm.

Description: This paper presents a comparative study of the radiometric sensitivity and spatial resolution of three near-field (NF) passive screener systems: real aperture, 1-D synthetic aperture (SA), and 2-D SA radiometers are compared. The analytical expressions for the radiometric resolution, the number of required antennas, and the number of pixels in the image are derived taking into account the distortion produced by the NF geometry at nonboresight directions where the distortion is dominant. Based on the theoretical results, a performance comparison among the studied systems is carried out to show the advantages and drawbacks when using the radiometers in a close-range screening application. Moreover, the screener performance in a close-range environment is discussed from the results obtained in the aforementioned comparison.

Description: Among all types of unwanted signals in high-frequency (HF) surface wave (HFSW) over-the-horizon (OTH) radars, radio-frequency interference (RFI) is dominant since HF band is shared by many radio services. In observation data, there are two types of common RFI. The most common one is the conventional RFI which presents vertical stripe paralleling to range axis in range-Doppler spectrum (RDS) and has been exhaustively reported by previous papers. Meanwhile, a new type of RFI characterized by sloping stripes (called $ hbox{RFI}_{rm SS}$ ) in RDS is also frequently observed. This work concentrates on the new $hbox{RFI}_{rm SS}$ and establishes a unified model for the above two types of RFI. Based on this generalized model, a time-domain RFI suppression algorithm is proposed here. Benefiting from a closed-form approximate maximum likelihood estimator, the proposed algorithm exhibits excellent performance and is computationally efficient. Its operational performance is evaluated using the field data recorded by experimental HFSW OTH radar of Wuhan University.

Description: Wideband autocorrelation radiometry (wideband AR) offers a deterministic method of remotely sensing microwave travel time $tau_{s}$ in planetary surface layers that are quasi-transparent to microwaves. Combining $tau_{s}$ with an independent estimate of the layer's average microwave index of refraction $n_{s}$ yields a measure of layer thickness whose accuracy is primarily limited by the accuracy of $n_{s}$ . The technique requires that four conditions be met: 1) The correlation time of the radiometric signal must be less than the time difference at the radiometer between an upwelling ray that traverses the quasi-transparent layer once and a multiply reflected ray that traverses the quasi-transparent layer three times; 2) interfaces at the top and bottom of the layer must be effectively specular at the frequency of the radiometer; 3) dielectric transitions at the top and bottom of the layer must be distinct; and 4) rays transiting the layer must not be significantly absorbed or scattered. The performance of wideband AR for sensing dry snowpacks is governed by the relationship between system bandwidth and minimum snowpack thicknesses that can be sensed, the microwave indices of refraction of snowpacks and their underlying media, and the integration time required to depress the autocorrelation noise floor below the autocorrelation signal. Findings of this paper are that microwave travel times within dry snowpacks over frozen or thawed soils, or over ice, could be deterministically measured for snowpack thicknesses between 10 cm and 2 m using wideband AR sensors having 10-GHz center frequencies, 1-GHz bandwidths, and 1-ms integration times.

Description: Synthetic aperture radar (SAR) images are inherently affected by multiplicative speckle noise, which will degrade the human interpretation and computer-aided scene analysis. In this paper, we propose a novel Bayesian multiscale method for SAR image despeckling in the non-homomorphic framework. To address the multiplicative nature, we first make the speckle contribution additive by a linear decomposition. Then, in the stationary wavelet transform domain, a two-sided generalized Gamma distribution (G $Gamma$ D) is introduced as a prior to capture the heavy-tailed nature of wavelet coefficients of the noise-free reflectivity. By exploiting this prior together with a Gaussian likelihood, an analytical wavelet shrinkage function is derived based on maximum a posteriori criteria, which further adopts heterogeneity-adaptive thresholding technique to achieve better estimates of noise-free wavelet coefficients. Moreover, a pilot-signal-assisted strategy is proposed to estimate the parameters of two-sided G $Gamma$ D with the estimator based on second-kind cumulants. Finally, experimental results, carried out on the synthetic and actual SAR images, are given to demonstrate the validity of the proposed despeckling method.

Description: Considering that the statistics of the phase and the power of weather signals in the spectral domain are different from those statistics for echoes from stationary objects, a spectrum clutter identification (SCI) algorithm has been developed to detect ground clutter using single polarization radars, but SCI can be extended for dual-pol radars. SCI examines both the power and phase in the spectral domain and uses a simple Bayesian classifier to combine four discriminants: spectral power distribution, spectral phase fluctuations, spatial texture of echo power, and spatial texture of spectrum width to make decisions as to the presence of clutter that can corrupt meteorological measurements. This work is focused on detecting ground clutter mixed with weather signals, even if the clutter power to signal power ratio is low. The performance of the SCI algorithm is shown by applying it to radar data collected by University of Oklahoma-Polarimetric Radar for Innovation in Meteorology and Engineering.

Description: Very high resolution synthetic aperture radar (SAR) sensors represent an alternative to aerial photography for delineating floods in built-up environments where flood risk is highest. However, even with currently available SAR image resolutions of 3 m and higher, signal returns from man-made structures hamper the accurate mapping of flooded areas. Enhanced image processing algorithms and a better exploitation of image archives are required to facilitate the use of microwave remote-sensing data for monitoring flood dynamics in urban areas. In this paper, a hybrid methodology combining backscatter thresholding, region growing, and change detection (CD) is introduced as an approach enabling the automated, objective, and reliable flood extent extraction from very high resolution urban SAR images. The method is based on the calibration of a statistical distribution of “open water” backscatter values from images of floods. Images acquired during dry conditions enable the identification of areas that are not “visible” to the sensor (i.e., regions affected by “shadow”) and that systematically behave as specular reflectors (e.g., smooth tarmac, permanent water bodies). CD with respect to a reference image thereby reduces overdetection of inundated areas. A case study of the July 2007 Severn River flood (UK) observed by airborne photography and the very high resolution SAR sensor on board TerraSAR-X highlights advantages and limitations of the method. Even though the proposed fully automated SAR-based flood-mapping technique overcomes some limitations of previous methods, further technological and methodological improvements are necessary for SAR-based flood detection in urban areas to match the mapping capability of high-quality aerial photography.

Description: An innovative scheme is presented for moving target detection and high-resolution focusing that exploits a bank of chirp scaling algorithms (CSA), each one matched to a different along track target velocity component. The new scheme is thought for multichannel (MC) synthetic aperture radar systems, to provide a high-resolution focusing of the moving targets. Adequate target detection capability is ensured by integrating the aforementioned bank of CSA with a post-Doppler space–time adaptive processing clutter cancellation step. The presented scheme is very efficient from a computational point of view and is able to achieve sub-clutter visibility for the moving targets. The effectiveness of the proposed techniques is shown with reference to an emulated MC data set.

Description: This paper proposes a new method for the classification of synthetic aperture radar (SAR) images based on a novel feature vector. The method aims at combining the intensity information of pixels with spatial information and structural relationships. Unlike classical approaches which define a static neighborhood via a rectangular moving window of predefined size and relate spatial information for each center pixel to all the pixels within that window, the local primitives (LPs) proposed in this study provide us with an adaptive neighborhood so that spatial information for each center pixel is extracted only from the related pixels in its neighborhood. LPs correspond to local homogeneous connected components that describe the pixel neighborhood more consistently than the fixed size window approach. A feature vector, called as the LP pattern (LPP), is constructed for each pixel. The feature vector includes information about the sizes, intensity levels, and contrast differences of LPs within a disk whose center is the pixel under consideration as well as the repetitive frequency of LPs outside that disk. Finally, a kernel-based support vector machine is used with the proposed feature vectors for the classification of SAR images. Experimental analysis presents that the new feature extraction technique is well suited to depict spatial information and structural relationships and it yields promising results for the classification of SAR images when compared to common features such as gray-level co-occurrence matrix and Gabor coefficients.

Description: The application of four techniques for the shape reconstruction of a 2-D metallic cylinder buried in dielectric slab medium by measured the scattered fields outside is studied in the paper. The finite-difference time-domain (FDTD) technique is employed for electromagnetic analyses for both the forward and inverse scattering problems, while the shape reconstruction problem is transformed into optimization one during the course of inverse scattering. Then, four techniques including asynchronous particle swarm optimization (APSO), PSO, dynamic differential evolution (DDE) and self-adaptive DDE (SADDE) are applied to reconstruct the location and shape of the 2-D metallic cylinder for comparative purposes. The statistical performances of these algorithms are compared. The results show that SADDE outperforms PSO, APSO and DDE in terms of the ability of exploring the optima. However, these results are considered to be indicative and do not generally apply to all optimization problems in electromagnetics.

Description: This paper summarizes the studies performed using data from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) to verify cloud variables in the context of the European Centre for Medium-Range Weather Forecasts assimilation system. In the first part of this paper, the cloud-top height (CTH) derived from the Atmospheric Infrared Sounder (AIRS) radiances has been compared with the same quantity from CALIPSO exploiting the spatial and temporal coincidence guaranteed by the A-Train constellation. In the second part, the CTH from CALIPSO has been used to verify the wind height assignment (HA) for the atmospheric motion vectors (AMVs) derived from Meteosat geostationary cloudy radiances. Two different estimates from AIRS have been considered: one derived from the cloud detection scheme and the other used within the assimilation of infrared (IR) cloudy radiances. The verification of cloud detection for hyperspectral IR sounders has revealed some difficulties with cirrus clouds. The verification of CTH for the assimilation of IR cloudy radiances has shown a reasonable agreement when the lidar signal is fully attenuated but a larger scatter in other conditions. The verification of AMV HA revealed an overall tendency to produce CTHs lower than CALIPSO for high clouds and the opposite for low clouds. Interestingly, a similar behavior was observed in the validation of CTHs for the assimilation of IR cloudy radiances.

Description: This paper proposes a novel unsupervised, non-Gaussian, and contextual segmentation method that combines an advanced statistical distribution with spatial contextual information for multilook polarimetric synthetic aperture radar (PolSAR) data. This extends on previous studies that have shown the added value of both non-Gaussian modeling and contextual smoothing individually or for intensity channels only. The method is based on a Markov random field (MRF) model that integrates a ${cal K}$ -Wishart distribution for the PolSAR data statistics conditioned to each image cluster and a Potts model for the spatial context. Specifically, the proposed algorithm is constructed based upon the stochastic expectation maximization (SEM) algorithm. A new formulation of SEM is developed to jointly perform clustering of the data and parameter estimation of the ${cal K}$ -Wishart distribution and the MRF model. Experiments on simulated and real PolSAR data demonstrate the added value of using an appropriate statistical representation, in combination with contextual smoothing.

Description: The thermal infrared hemispherical downwelling irradiance (HDI) emitted by the atmosphere and surrounding elements contributes through reflection to the signal measured over an observed surface by remote sensing. This irradiance must be estimated in order to obtain accurate values of land-surface temperature (LST). There are some fast methods to measure the HDI with a single measurement pointing to the sky at a specified viewing direction, but these methods require completely cloud-free or cloudy skies, and they do not account for the radiative contribution of surrounding elements. Another method is the use of a diffuse reflectance panel (usually, a rough gold-coated surface) with near-Lambertian behavior. This method considers the radiative contribution of surrounding elements and can be used under any sky condition. A third possibility is the use of atmospheric profiles and a radiative transfer code (RTC) in order to simulate the atmospheric signal and to calculate the HDI by integration. This study compares the HDI estimations with these approaches, using measurements made on four different days with a completely clear sky and two days with a partially cloudy sky. The measurements were made with a four-channel CIMEL Electronique radiometer working in the 8–14- $muhbox{m}$ spectral range. The HDI was also estimated by means of National Centers for Environmental Prediction atmospheric profiles introduced in the MODTRAN RTC. Additionally, the measurements were made at two different places with very different environments to quantify the effect of the contributing surroundings. Results showed that, for a clear-sky day with a minimal contribution of the surroundings, all methods differed from each other between 5% and 11%, depending on the spectral range, and any of them could be used to estimate HDI in these conditions. However, in the case of making surface measurements in an area with signi- icant surrounding elements (buildings, trees, etc.), HDI values retrieved from the panel present an increase of $+3 hbox{W}cdothbox{m}^{-2}cdotmuhbox{m}^{-1}$ compared with the other methods; this increase, if ignored, implies to make an error in LST ranging from $+0.5 ^{circ}hbox{C}$ to $+1.5 ^{circ}hbox{C}$ , depending on the spectral range and on surface emissivity and temperature. Comparison under heterogeneous skies with changing cloud coverage showed also large differences between the use of panel and the other methods, reaching a maximum difference of $+4.6 hbox{W}cdothbox{m}^{-2}cdotmuhbox{m}^{-1}$ , which implies to make an error on LST of $+2.2 ^{circ}hbox{C}$ . In these cases, the use of the diffuse reflectance panel is proposed, since it is the unique way to capture the contribution of the surroundings and also to adequately measure HDI for sky changing conditions.

Description: An individual tree within a forest stand will have its height and diameter growth restricted by the influence of neighboring trees. This is because trees in close proximity compete for resources and space to enable growth. In this paper, the position of trees, tree height (LH), tree crown radius (LCR), and growth competition index (LCI) were extracted from a light-detection-and-ranging (LiDAR)-based rasterized canopy height model using the multilevel morphological active-contour algorithm. The diameter and volume of individual trees are tested and validated to be an exponential function of those LiDAR-derived tree parameters. The best LiDAR-based diameter estimation model and volume estimation model were tested as significant with an $R^{2}$ value of 0.84 and 0.9 and evaluated with an estimation bias of 8.7 cm and 0.91 $ hbox{m}^{3}$ , respectively. Results also showed that LH and LCR are positively related to the LiDAR-derived diameter at breast height (DBH) and the LiDAR-derived volume of individual trees in a forest stand, whereas LCI is negatively related. The proposed algorithm of individual tree volume estimation was further applied to predict the volume of three sample plots in mountainous forest stands. It was found that the LVM could be used to predict an acceptable volume estimate of old-aged forest stands. The estimation bias, i.e., percentage RMSE (RMSE%), is averaged at around 4% using the LiDAR metrics $lnhbox{LH}$ , LCI, and LCR, whereas the RMSE% increases to 50% if only $lnhbox{LH}$ is applied. Results suggest that LCI is an important regulation factor in the estimation of forest volume stocks using LiDAR remote sensing.

Description: The estimation of volumetric soil moisture under low agricultural vegetation from fully polarimetric synthetic aperture radar (SAR) data at L-band using a multi-angular polarimetric decomposition is investigated. Radar polarimetry provides the framework to decompose the backscattered signal into different canonical scattering mechanisms referring to scattering contributions from the underlying soil and the vegetation cover. Multi-angular observation diversity further increases the information space for soil moisture inversion enabling higher inversion rates and a stable inversion performance. The developed approach was applied on the multi-angular L-band data set acquired by German Aerospace Center's ESAR sensor as part of the OPAQUE campaign in 2008. The obtained results are compared against ground measurements collected by the OPAQUE team over a variety of vegetated agricultural fields. The validation of the estimated against ground measured soil moisture results in an root mean square error level of 6–8 vol. $%$ including all test fields with a variety of crop types.

Description: Hyperspectral remote sensing imagery contains rich information on spectral and spatial distributions of distinct surface materials. Owing to its numerous and continuous spectral bands, hyperspectral data enable more accurate and reliable material classification than using panchromatic or multispectral imagery. However, high-dimensional spectral features and limited number of available training samples have caused some difficulties in the classification, such as overfitting in learning, noise sensitiveness, overloaded computation, and lack of meaningful physical interpretability. In this paper, we propose a hyperspectral feature extraction and pixel classification method based on structured sparse logistic regression and 3-D discrete wavelet transform (3D-DWT) texture features. The 3D-DWT decomposes a hyperspectral data cube at different scales, frequencies, and orientations, during which the hyperspectral data cube is considered as a whole tensor instead of adapting the data to a vector or matrix. This allows the capture of geometrical and statistical spectral–spatial structures. After the feature extraction step, sparse representation/modeling is applied for data analysis and processing via sparse regularized optimization, which selects a small subset of the original feature variables to model the data for regression and classification purpose. A linear structured sparse logistic regression model is proposed to simultaneously select the discriminant features from the pool of 3D-DWT texture features and learn the coefficients of the linear classifier, in which the prior knowledge about feature structure can be mapped into the various sparsity-inducing norms such as lasso, group, and sparse group lasso. Furthermore, to overcome the limitation of linear models, we extended the linear sparse model to nonlinear classification by partitioning the feature space into subspaces of linearly separable samples. The advantages of our methods are validated on the real h- perspectral remote sensing data sets.

Description: Denoising of hyperspectral imagery in the domain of imaging spectroscopy by conditional random fields (CRFs) is addressed in this work. For denoising of hyperspectral imagery, the strong dependencies across spatial and spectral neighbors have been proved to be very useful. Many available hyperspectral image denoising algorithms adopt multidimensional tools to deal with the problems and thus naturally focus on the use of the spectral dependencies. However, few of them were specifically designed to use the spatial dependencies. In this paper, we propose a multiple-spectral-band CRF (MSB-CRF) to simultaneously model and use the spatial and spectral dependencies in a unified probabilistic framework. Furthermore, under the proposed MSB-CRF framework, we develop two hyperspectral image denoising algorithms, which, thanks to the incorporated spatial and spectral dependencies, can significantly remove the noise, while maintaining the important image details. The experiments are conducted in both simulated and real noisy conditions to test the proposed denoising algorithms, which are shown to outperform the popular denoising methods described in the previous literatures.

Description: In this paper, the wave equation based on phase shift migration technique is extended for terahertz 3-D imaging with quasi-optical transceivers. An analytical expression of the reconstructed 3-D point-spread function for targets under the illumination of a terahertz Gaussian beam was derived with this reconstruction technique. The quantitative relationship between the imaging quality and the parameters of the transmitted Gaussian beam was obtained, which provides a good criterion to be followed when designing the terahertz quasi-optical transceivers in the imaging systems. Moreover, the spatial sampling criterion was derived strictly which is also quantitatively related to the parameters of the transmitted Gaussian beam. Simulation results with fairly good agreement were given to verify the theoretical results derived in this paper. Finally, a monostatic prototype imager with a Gaussian beam transceiver was designed for the proof-of-principle experiments in 0.2-THz band. The 3-D imaging results of different targets and a mannequin with concealed threat objects were given to demonstrate the theoretical results obtained in this paper and the effectiveness of the 3-D terahertz image reconstruction for security applications.

Description: The Moderate Resolution Imaging Spectroradiometer (MODIS) has been used in several remote sensing studies, including land, ocean, and atmospheric applications. The advantages of this sensor are its high spectral resolution, with 36 spectral bands; its high revisiting frequency; and its public domain availability. The first seven bands of MODIS are in the visible, near-infrared, and mid-infrared spectral regions of the electromagnetic spectrum which are sensitive to spectral changes due to deforestation, burned areas, and vegetation regrowth, among other land-use changes, making near-real-time forest monitoring a suitable application. However, the different spatial resolution of the spectral bands placed in these spectral regions imposes challenges to combine them in forest monitoring applications. In this paper, we present an algorithm based on geostatistics to downscale five 500-m MODIS pixel bands to match two 250-m pixel bands. We also discuss the advantages and limitations of this method in relation to existing downscaling algorithms. Our proposed method merges the data to the best spatial resolution and better retains the spectral information of the original data.

Description: Using the field imaging spectrometer system (FISS) recently developed by us, a new operational radiometric calibration (RC) model that takes into account three main adjustable sensor system settings, including the integration time $(t)$ , the aperture $(F)$ , and the detector temperature $(T)$ , is proposed. To better understand the influence of a single setting on the RC model, controlled experiments with one variable and two fixed settings were conducted and analyzed using a well-calibrated integrating sphere. Subsequently, a new variable was constructed with the ratio of $t$ and $F^{2}$ to determine the system-setting-based RC model, where the radiometric offset was derived from system noise estimated by keeping the FISS entrance slit from a light source in a dark environment. Finally, the model was evaluated using experimental calibration results from the integrating-sphere data and real vegetation data. The results indicated that standard and calculated radiances were consistent over most spectral wavelengths. The proposed RC model could be effectively applied not only for the FISS and other ground-based sensors but also for future Chinese-developed intelligent remote sensing satellite systems that can automatically modify imaging settings in line with specific requirements.

Description: Revisiting time constitutes a key constraint for continuous monitoring activities based on space- and airborne synthetic aperture radar (SAR) acquisitions. Conversely, the employment of terrestrial platforms overcomes this limitation and makes it possible to perform time-continuous observations of small space-scale phenomena. New research lines of SAR dealing with the backscattering evolution of different types of scenarios become hence possible through the analysis of ground-based SAR (gbSAR) data collections. The Remote Sensing Laboratory of the Universitat Politècnica de Catalunya drove a one-year measurements campaign in the village of Sallent, northeastern Spain, using its X-Band gbSAR sensor. The field experiment aimed at studying the subsidence phenomenon induced by the salt mining activity carried out in this area during the past decades. In this paper, the polarimetric behavior of an urban environment is investigated at different time scales. After a brief description of the test site and the measurement campaign, the analysis is focused on the stability on man-made structures at different time scales. PolSAR data monthly acquired from June 2006 to July 2007 are employed to stress the presence of nonstationary backscattering processes within the urban scene and the effect they have on differential phase information. Then, a filtering procedure aiming at reducing backscattering randomness in one-day and long-term data collections is then put forward. The improvements provided by the proposed technique are assessed using a new polarimetric descriptor, the time entropy. In the end, the importance of preserving the interferometric phase information from nonstationary backscattering contaminations using fully polarimetric data is discussed.

Description: In the above-named article [ibid., vol. 50, no. 12, pp. 4892??4902, Dec. 2012], Fig. 6 is incorrect. The correct one is printed here.

Description: A general method for correcting out-of-band (OOB) effects to improve radiometric accuracy in multispectral sensors is proposed using an OOB correction transform (OBCT) developed according to linear systems theory. The correction for a particular channel is based on the intensities measured by all the channels. To recover the narrowband signals, the measured signals (with OOB effects) are partitioned by spectral subranges of in-bands and band-gaps. The OBCT matrix is derived using appropriate approximations for the band-gap integrals. For an $N$ -channel multispectral sensor, OOB effects are corrected by applying an $N times N$ OBCT matrix to the measured signals. The OBCT matrix for bands M1–M7 of the VIIRS, which was successfully launched on Oct. 28, 2011, is presented, along with simulation results using hyperspectral data from the Airborne Visible InfraRed Imaging Spectrometer and Hyperspectral Imager for the Coastal Ocean sensors.

Description: A fuzzy fault-tolerant control (FFTC) framework is proposed for wind-diesel-hybrid systems (WDHS) with time-varying bounded sensor faults. The algorithm utilizes fuzzy systems based on “Takagi–Sugeno” (TS) fuzzy models to represent nonlinear systems. A fuzzy proportional-integral estimation observer (FPIEO) design is proposed to achieve fault estimation of TS models with abrupt sensor faults. Sufficient conditions are derived for robust stabilization in the sense of Lyapunov asymptotic stability and are formulated in the format of linear matrix inequalities (LMIs) to obtain controller gains and observer gains. The proposed algorithm maximizes the produced power, minimizes the voltage ripple, and is able to maintain the system's stability during the sensor faults. A physical model of the WDHS is presented and transformed into a TS model. Then, an FFTC algorithm is developed and applied to a WDHS to demonstrate the effectiveness of this method.

Description: Modeling wind speed time series (WSTS) is an essential part of network planning studies in order to generate synthetic wind power time series (WPTS). Hence, this paper proposes a methodology to equip planners with accurate simulation of wind speed and power variations as well as complete temporal dependence structure based on the copula theory. Unlike traditional autoregressive and Markov chain methods, the suggested technique is well-prepared to deal with “nonlinear long-memory temporal dependence” and “non-Gaussian empirical probability distributions” of the WSTS. Meanwhile, the proposed statistical modeling framework is compatible with the scenario-based analysis of active networks as well. Furthermore, a case study for optimal sizing of an autonomous wind/photovoltaic/battery system is presented. The purpose of the presented study is to fully examine the accuracy and effectiveness of the copula-based model of wind generation for planning nonmemoryless power systems. Among a list of commercially available system devices, the optimal number and type of units are calculated ensuring both a minimum 20-year round total system cost and a perfect reliability. The genetic algorithm is used in four wind generation scenarios consisting of real and simulated WPTS. Then, considering the corresponding optimal solutions, the autoregressive moving average (ARMA), nonparametric Markov and proposed copula-based simulations are compared against real data.

Description: This paper presents series grid interface topologies for enhancing the fault ride-through (FRT) performance of doubly-fed induction generator (DFIG)-based wind turbines (WTs). Two voltage booster schemes, 1) dynamic voltage restorer (DVR) and 2) resistive type high temperature superconducting fault current limiter (HTS-FCL), are designed and implemented. The test system represents a WT connected to an electric grid with alternatively employing DVR and HTS-FCL. Both schemes provide fast mitigation of voltage dip that maintains the nominal operating conditions for DFIG-WT. To achieve a flexible control solution for balanced and unbalanced fault conditions, the DVR employs positive and negative sequence controllers while the HTS-FCL is designed to perform fast quenching for each phase individually. The potential of the two booster schemes is evaluated and analyzed in positive and negative sequence reference frames. Comprehensive simulation studies are presented to verify the capability of the series grid interface schemes for ensuring the normal operation and smooth wind power evacuation with effective isolation from grid faults. Furthermore, the grid code requirements of reactive current support are evaluated for both schemes at various fault scenarios.

Description: This paper presents a real-time mechanism to tolerate faults occurring in a wind turbine (WT) system. This system is a FAST coded simulator designed by the U.S. National Renewable Energy Laboratory's (NREL's) National Wind Turbine Center. The demonstrated mechanism lies under the taxonomy of active fault-tolerant control (AFTC) systems, namely the projection-based approach. In the proposed approach, we do not use any a priori information about the model of the turbine in real-time. In fact, we use the online measurements generated by WT. Based on the given control objectives, and the observed measurements, an occurring fault is accommodated by reconfiguring the controller such that the turbine generates the rated power even under faulty conditions. Second, no use of an explicit fault-diagnosis module is seen in this approach. Therefore, the fault accommodation delay in the proposed AFTC structure is smaller than the delay as experienced in the traditional structure of AFTC systems.

Description: Conventionally operated full power converter wind plants show high short-term power output variability caused by variable winds, and does not contribute to the power system inertia due to the decoupled generator speed and grid frequency. There is, however, abundant inertial resources in wind plant rotors for both smoothing of output power and for synthetic inertia contribution. Together with added frequency controlling functionality, this could facilitate inclusion of wind power in islanding systems, enabling greater system loads and enhancing power system stability. This paper describes modeling of power smoothing and frequency controlling wind plants and assesses different control strategies as well as the grid frequency performance gains achievable over hydro powered islanding systems and over islanding systems incorporating both hydro power and conventional wind plants. The results show that wind plant power output could be smoothed in the short time frame, and support frequency in both primary and secondary frequency control timescales including droop functionality.

Description: This paper deals principally with the grid connection problem of a kite-based system, named the “Kite Generator System (KGS).” It presents a control scheme of a closed-orbit KGS, which is a wind power system with a relaxation cycle. Such a system consists of a kite with its orientation mechanism and a power transformation system that connects the previous part to the electric grid. Starting from a given closed orbit, the optimal tether's length rate variation (the kite's tether radial velocity) and the optimal orbit's period are found. The trajectory-tracking problem is not considered in this paper; only the kite's tether radial velocity is controlled via the electric machine rotation velocity. The power transformation system transforms the mechanical energy generated by the kite into electrical energy that can be transferred to the grid. A Matlab/simulink model of the KGS is employed to observe its behavior, and to insure the control of its mechanical and electrical variables. In order to improve the KGS's efficiency in case of slow changes of wind speed, a maximum power point tracking (MPPT) algorithm is proposed.

Description: To maximize the revenue from selling energy, photovoltaic systems (PVs) in general operate in the so-called maximum power point tracking mode. However, the increasing penetration of renewable energy sources in power systems has motivated the design of innovative control to provide ancillary services. The focus of this paper is to develop a new control strategy that enables PVs to adjust the active power outputs and provide frequency regulation to power systems. In this strategy, two different modes are designed: 1) the frequency droop control mode for PVs to provide primary frequency support to power systems, and 2) the emergency control mode to prevent system frequency collapse and, therefore, to prevent too much generation tripping after fault. Based on a detailed PV dynamic model, simulation results show the effectiveness of the proposed control strategy in improving the frequency stability.

Description: Standalone microgrids with renewable sources and battery storage play an important role in solving power supply problems in remote areas such as islands. To achieve reliable and economic operations of a standalone microgrid, in addition to the consideration of utilization of renewable resources, the lifetime characteristics of a battery energy storage system also need to be fully investigated. In this paper, in order to realize the economic operation of a recently developed standalone microgrid on Dongfushan Island in China, an optimization model including battery life loss cost, operation and maintenance cost, fuel cost, and environmental cost is established to obtain a set of optimal parameters of operation strategy. Considering the lifetime characteristics of lead–acid batteries, a multiobjective optimization to minimize power generation cost and to maximize the useful life of lead–acid batteries has been achieved via the nondominated sorting genetic algorithm (NSGA-II). The results show that the proposed method can optimize the system operations under different scenarios and help users obtain the optimal operation schemes of the actual microgrid system.

Description: Plug-in electric vehicles in the future will possibly emerge widely in city areas. Fleets of such vehicles in large numbers could be regarded as considerable stochastic loads in view of the electrical grid. Moreover, they are not stabled in unique positions to define their impact on the grid. Municipal parking lots could be considered as important aggregators letting these vehicles interact with the utility grid in certain positions. A bidirectional power interface in a parking lot could link electric vehicles with the utility grid or any storage and dispersed generation. Such vehicles, depending on their need, could transact power with parking lots. Considering parking lots equipped with power interfaces, in more general terms, parking-to-vehicle and vehicle-to-parking are propose here instead of conventional grid-to-vehicle and vehicle-to-grid concepts. Based on statistical data and adopting general regulations on vehicles (dis)charging, a novel stochastic methodology is presented to estimate total daily impact of vehicles aggregated in parking lots on the grid. Different scenarios of plug-in vehicles' penetration are suggested in this paper and finally, the scenarios are simulated on standard grids that include several parking lots. The results show acceptable penetration level margins in terms of bus voltages and grid power loss.

Description: It is demonstrated that a physical-based equivalent electrical circuit could be developed based on electrochemical impedance spectroscopy (EIS) to describe electrochemical performance of a commercial $hbox{Li}_{1-{x}}hbox{PO}_{4}$ -cathode, Li-ion cell. This model uses only EIS modeling and Fourier transform techniques to provide reasonably accurate voltage performance characteristics while providing insight into the physical processes at work in the cell.

Description: The demand for reducing fossil fuel consumption and carbon emissions has stimulated the development and deployment of electric vehicles (EVs). The reliability of power systems will be significantly influenced by the increasing penetration of EVs. Currently, there are mainly two potential modes for EVs charging: 1) plug-in mode and 2) battery exchange (BE) mode. There have been many papers studying the impact of EVs using plug-in mode. However, research on BE mode is still only limited. This paper aims at estimating the reliability impact caused by EVs using BE mode. First, the behavior of EV users under BE mode are extracted from two reliable databases. The behavior extraction method is developed specifically for BE mode, which is modified based on a method originally designed for plug-in mode. Afterwards, power system reliability is studied from the viewpoint of both the power system and the EV users by introducing a new reliability index named User Demand Not Satisfied (UDNS). The algorithm for reliability assessment is developed based on an algorithm designed for energy storage system (ESS) integration. The numerical results demonstrate the integration of EVs using BE mode can greatly benefit the reliability performances of power systems.

Description: As increasing number of nations, industries, and individuals are involved in the carbon mitigation initiative, it becomes significant to answer what amount of carbon emissions social entities are responsible for, especially from electricity service. In this paper, a carbon flow tracing method is presented to determine carbon emissions obligation from electricity consumption. The method traces energy sources of electricity consumption across the electricity network to determine the indirect carbon emissions caused. From a “generation-to-consumption” perspective, the transmission characteristic of electricity supply and locational energy mix are reflected. The method is employed to address two important issues uniformly, i.e., carbon accounting at the regional level and locational carbon intensity assessment at the user level, respectively. Test results from two examples show that the method is a preferable choice to solve the two problems. Additionally, the method may contribute to carbon reduction cooperation and end user participation in carbon mitigation.

Description: This paper presents an intelligent economic operation of smart grid environment facilitating an advanced quantum evolutionary method. The proposed method models the wind generation (WG) and photovoltaic (PV) generation as renewable power generation sources as a measure of global warming effect. Thermal generators (TGs) are included in this model to provide the maximum amount of energy to meet consumers' demand. On the other hand, plug-in hybrid electric vehicles (PHEVs) are capable of reducing CO $_2$ , NO $_x$ , and gradually becoming an integral part of smart-grid infrastructure. Such integration introduces uncertainties into the system that are addressed by fuzzy-logic-based formulations. Demanded load, wind speed, solar radiation, and number of involved PHEVs are taken under fuzzy formulations. An intelligent quantum inspired evolutionary algorithm (IQEA) is proposed and applied in this model to perform the intelligent economic scheduling operation concerning scheduling and dispatching TG, WG, PV, and PHEV. IQEA features intelligent operators such as sophisticated rotation operator, differential operator, etc. The method is tested on a hypothetical power system with 10 thermal units, equivalent number of PHEVs, equivalent solar and wind farm. The simulation results will show the effectiveness of IQEA that provides excellent operational resource scheduling while reducing the production cost and emission.

Description: Often voltage rise along low voltage (LV) networks limits their capacity to accommodate more renewable energy (RE) sources. This paper proposes a robust and effective approach to coordinate customers' resources and control voltage rise in LV networks, where photovoltaics (PVs) are considered as the RE sources. The proposed coordination algorithm includes both localized and distributed control strategies. The localized strategy determines the value of PV inverter active and reactive power, while the distributed strategy coordinates customers' energy storage units (ESUs). To verify the effectiveness of proposed approach, a typical residential LV network is used and simulated in the PSCAD-EMTC platform.

Description: It is well known that control systems, which can modulate the power takeoff force of a wave energy device, have the capability of extending the effective device bandwidth and thereby improve energy capture. For an array of wave energy devices, there is the additional complication that each device is subject to radiated waves from other devices, which are a function of the device motion and, hence, an indirect function of each of the device controllers. This gives the possibility of enhancing the energy harvesting properties of the wave farm as a whole, by giving each controller information about the motion of other devices and employing a global performance function which allows coordinated control for the overall benefit of the farm. This paper examines the possibilities of using such coordinated control and makes conclusions on the types of devices, and types of arrays, that might benefit from such coordinated control. In addition, the overall benefit of a global array control strategy, compared to independent control of each device, is assessed against the added complexity of a coordinated control strategy.