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 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: 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: This paper presents the results of an experiment that is performed with a network of bidirectional corner reflector (CR) multipass scattering equipment (MUSE), which enable 3-D-displacement measurements. We describe the results of an experiment which was designed to assess the precision of the measurements of ground displacement using MUSE CRs and the permanent scatterer (PS) technique. The CR displacements are applied by micrometric vernier controls during the acquisition of a TerraSAR-X time series of ten images. The relative displacements are estimated between each date, using a PS technique. This paper shows that the relative displacements between the reflectors are estimated with a precision of 0.48 mm along the line of sight. This precision is defined as the standard deviation of the difference between the measured and the applied displacements along the time series. The linear displacement rates of the reflectors are then estimated using the spatiotemporal unwrapping network algorithm, with a 0.4-mm/year precision. We finally show that the experimental results are well predicted by theorical simulations.

Description: A novel model is presented to address the problem of semantic clustering of geo-objects in very high resolution panchromatic satellite images. The proposed model combines a probabilistic topic model with a multiscale image representation into an automatic framework by embedding both document and scale selections. The probabilistic topic model is used to characterize the statistical distributions of both intraclass appearance and inter-class coherence of geo-objects within documents, i.e., squared sub-images. Because the bag-of-words assumption involved in the probabilistic topic models does not consider the spatial coherence between topic labels, the multiscale image representation is designed to provide a self-adaptive spatial regularization for various geo-object categories. By introducing scale and document selections, the automatic framework integrates the probabilistic topic model and the multiscale image representation to ensure that words on a site should be allocated the same topic label no matter what documents they reside in. Consequently, unlike the traditional method of applying topic models for analyzing satellite images, the process of explicitly generating a set of documents before modeling and then combining multiple labels for a word on a given site is unnecessary. Gibbs sampling is adopted for parameter estimation and image clustering. Extensive experimental evaluations are designed to first analyze the effect of parameters in the proposed model and then compare the results of our model with those of some state-of-the-art methods for three different types of images. The results indicate that the proposed algorithm consistently outperforms these exiting state-of-the-art methods in all of the experiments.

Description: Our final objective is the automatic recognition of targets from full-polarimetric bistatic synthetic aperture radar images. To this end, the theories of Huynen and Cameron are generalized to bistatic radar scattering, and their respective characteristic parameters are compared. We have then studied first how to characterize and compare bistatic mechanisms free from physical rotations and second how to build “coherent” reference memories. Two algorithms are proposed and tested.

Description: The National Aeronautics and Space Administration's (NASA) proposed Soil Moisture Active Passive (SMAP) satellite mission ( $sim$ 2014) will include a radar system that will provide L-band multi-polarization backscatter at a constant incidence angle of 40 $^{circ}$ . During the pre-launch phase of the project, there is a need for observations that will support the radar-based soil moisture algorithm development and validation. A valuable resource for providing these observations is the NASA Jet Propulsion Laboratory Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR). However, SMAP will observe at a constant incidence angle of 40 $^{ circ}$ , and UAVSAR collects data over a wide range of incidence angles (25 $^{circ}$ –60 $^{circ}$ ). In this investigation, a technique was developed and tested for normalizing UAVSAR data to a constant incidence angle. The approach is based on a histogram matching procedure. The data used to develop and demonstrate this approach were collected as part of the Canadian Soil Moisture Experiment 2010 (CanEx-SM10). Land cover in the region included agriculture and forest. Evaluation was made possible by the acquisition of numerous overlapping UAVSAR flight lines that provided multiple incidence angle observations of the same locations. Actual observations at a 40 $^{circ}$ incidence angle were compared to the normalized data to assess performance of the normalization technique. An optimum technique should be able to reduce the systematic error (Bias) to 0 dB and to lower the total root mean square error (RMSE) computed after correcti- n to the level of the initial residual error $(hbox{RMSE}_{rm res})$ present in the data set. The normalization approach developed here achieved both of these. Bias caused by the incidence angle variability was minimized to $sim$ 0 dB, whereas the residual error caused by instrument related random errors and amplitude fluctuations due to ground variability was reduced to approximately 3 dB for agricultural areas and 2.6 dB for forests; these values were consistent with the initial $hbox{RMSE}_{rm res}$ estimated using the un-corrected data. The residual error can be reduced further by aggregating the radar observations to a coarser grid spacing. The technique adequately adjusted the backscatter over the full swath width irrespective of the original incidence angle, polarization, and ground conditions (vegetation cover and soil moisture). In addition to providing a basis for fully exploiting UAVSAR (or similar aircraft systems) for SMAP algorithm development and validation, the technique could also be adapted to satellite radar systems. This normalization approach will also be beneficial in terms of reducing the number of flight lines required to cover a study area, which would eventually result in more cost-effective soil moisture field campaigns.

Description: This paper presents a new unsupervised land cover/land use classification scheme using polarimetric synthetic aperture radar (PolSAR) imagery based on polarimetric scattering similarity. Compared with the $H$ /alpha classification scheme based on a dominant “average” scattering mechanism, the proposed scheme has such advantages as the following: 1) The major scattering mechanism represents a target scattering in the low-entropy case; 2) it also represents both the major and minor scattering mechanisms in the medium-entropy case; and 3) all the scattering mechanisms in the high-entropy case can be represented. The major and minor scattering mechanisms have been identified automatically based on the relative magnitude of multiple-scattering similarities. The canonical scattering corresponding to maximum scattering similarity is regarded as the major scattering mechanism. The result obtained using the National Aeronautics and Space Administration/Jet Propulsion Laboratory's AIRSAR L-band PolSAR imagery reveals that the proposed scheme is more effective as compared to the existing models and promises to increase the accuracy of the classification and interpretation.

Description: The advancement of Earth observing sensors, data, and information systems enhances significantly the capabilities to access and process large volumes of geoscience data, which are often consumed by scientific workflows and processed in a distributed information environment. Consequently, data provenance becomes important since it allows users to determine the usability and reliability of data products. Motivation for capturing and sharing provenance also comes from the distributed data and information infrastructure that has been benefiting the Earth science community in the past decade, such as spatial data and information infrastructure, e-Science, and cyberinfrastructure. This paper provides an overview of geoscience data provenance in supporting provenance-aware geoscience data and information systems by summarizing state-of-the-art technologies and methodologies of geoscience data provenance and highlighting key considerations and possible solutions for geoscience data provenance.

Description: Data provenance, also called data lineage, records the derivation history of a data product. In the earth science domain, geospatial data provenance is important because it plays a significant role in data quality and usability evaluation, data trail audition, workflow replication, and product reproducibility. The generation of the geospatial provenance metadata is usually coupled with the execution of geo-processing workflow. Their symbiotic relationship makes them complementary to each other and promises great benefit once they are integrated. However, the heterogeneity of data and computing resources in the distributed environment constructed under the service-oriented architecture (SOA) brings a great challenge to resource integration. Specifically, the issues, such as the lack of interoperability and compatibility among provenance metadata models and between provenance and workflow, create obstacles for the integration of provenance, and geo-processing workflow. In order to tackle these issues, on one hand, this paper breaks the provenance heterogeneity through recording provenance information in a standard lineage model defined in ISO 19115:2003 and ISO 19115-2:2009 standards. On the other hand, this paper bridges the gap between provenance and geo-processing workflow through extending both workflow language and service interface, making it possible for the automatic capture of provenance information in the geospatial web service environment. The proposed method is implemented in the GeoBrain, a SOA-based geospatial web service system. The testing result from implementation shows that the geospatial provenance information is successfully captured throughout the life cycle of geo-processing workflows and properly recorded in the ISO standard lineage model.

Description: Accurate provenance information facilitates improved understanding of Earth science data and scientific reproducibility and can serve as an indicator of data quality. Provenance capture is an integral part of many modern workflow systems but may not have been considered in the design of legacy data production systems. Furthermore, in addition to data lineage, it is also important to capture contextual information needed for understanding how a data set was produced. This paper describes our experience in retrofitting a legacy data system to support capture, storage, and dissemination of provenance. Data inputs and transformations are logged automatically, while broader context information describing science algorithms and ancillary files is manually compiled. Provenance and context information are integrated for interactive user access and embedded into data files as XML documents compliant with the “Lineage” specification for geographic metadata defined by the International Organization for Standardization in the ISO 19115-2 standard. Lessons learned from this approach can inform others who need to incorporate provenance into a data system after the fact.

Description: Geospatial data provenance records sources and processing steps that are used in deriving geospatial data products. In the Web of Data environment enabled by Linked Data technologies, sources and processing steps, such as geospatial data and geoprocessing services, can be published as part of the Web of Data. To take full advantages of the machine-understandable format and linkages among heterogeneous data items in the Web of Data, this paper proposes to publish geospatial data provenance into the Web of Data. In particular, it analyzes how a catalogue for provenance, i.e., geospatial data provenance managed by a geospatial metadata catalog service, can be published into the Web of Data using a Linked Data approach. Consequently, queries over linked geospatial data provenance are analyzed and tested to demonstrate the benefits of the approach.

Description: We consider zenith/nadir pointing atmospheric radars and explore the effects of different dual-polarization architectures on the retrieved variables: reflectivity, depolarization ratio, cross-polar coherence, and degree of polarization. Under the assumption of azimuthal symmetry, when the linear depolarization ratio (LDR) and circular depolarization ratio (CDR) modes are compared, it is found that for most atmospheric scatterers reflectivity is comparable, whereas the depolarization ratio dynamic range is maximized at CDR mode by at least 3 dB. In the presence of anisotropic (aligned) scatterers, that is, when azimuthal symmetry is broken, polarimetric variables at CDR mode do have the desirable property of rotational invariance and, further, the dynamic range of CDR can be significantly larger than the dynamic range of LDR. The physical meaning of the cross-polar coherence is revisited in terms of scattering symmetries, that is, departure from reflection symmetry for the LDR mode and departure from rotation symmetry for the CDR mode. The Simultaneous Transmission and Simultaneous Reception mode (STSR mode or hybrid mode or $Z_{rm DR}$ mode) is also theoretically analyzed for the case of zenith/nadir pointing radars and, under the assumption of azimuthal symmetry, relations are given to compare measurements obtained at hybrid mode with measurements obtained from orthogonal (LDR and CDR) modes.

Description: The polar format algorithm (PFA) is a wavenumber domain imaging method for spotlight synthetic aperture radar (SAR). The classic fixed-parameter PFA employs interpolation technique for data correction. However, such an operation will induce heavy computational load and cause degradation in computation precision. To optimize image formation processing performance, this study presents a novel parameter-adjusting PFA, which can implement SAR image formation at an extremely highly squint angle with obviously improved computation efficiency and imaging precision. In the parameter-adjusting PFA, radar parameters, such as center frequency, chirp rate, pulse duration, sampling rate, and pulse repeat frequency (PRF), vary for each azimuth sampling position. Due to the parameter adjusting strategy, the echoed signal can be acquired directly in keystone format with uniformly distributed azimuth intervals. In this case, range interpolation, which is necessary in the fixed-parameter PFA to convert data from polar format to keystone format, can be eliminated. Chirp z-transform (CZT) can be employed to focus SAR data along the azimuth direction. Compared with truncated sinc-interpolation, CZT was found to perform better in inducing less phase and amplitude errors in data processing. When residual video phase (RVP) compensation was accomplished for dechirped signal, the processing steps of the parameter-adjusting PFA were simplified as azimuth CZTs and range inverse fast Fourier transforms (IFFT). Lastly, computer simulation of multiple point targets validated the presented approach.

Description: An approach to the detection of specific polarimetric SAR signatures is presented. The polarimetric response in a resolution cell can be viewed as a sample of the electromagnetic scattering matrix for that resolution cell. Through the use of multiple coherent apertures, multiple samples of the scattering matrix can be obtained. With the use of a suitable decomposition and a weighted log-likelihood formulation, it is possible to estimate the relative likelihoods that the observed scattering matrix responses match known electromagnetic signatures.

Description: The Soil Moisture Active Passive (SMAP) radiometer operates in the L-band protected spectrum (1400–1427 MHz) that is known to be vulnerable to radio-frequency interference (RFI). Although transmissions are forbidden at these frequencies by international regulations, ground-based, airborne, and spaceborne radiometric observations show substantial evidence of out-of-band emissions from neighboring transmitters and possibly illegally operating emitters. The spectral environment that SMAP faces includes not only occasional large levels of RFI but also significant amounts of low-level RFI equivalent to a brightness temperature of 0.1–10 K at the radiometer output. This low-level interference would be enough to jeopardize the success of a mission without an aggressive mitigation solution, including special flight hardware and ground software with capabilities of RFI detection and removal. SMAP takes a multidomain approach to RFI mitigation by utilizing an innovative onboard digital detector back end with digital signal processing algorithms to characterize the time, frequency, polarization, and statistical properties of the received signals. Almost 1000 times more measurements than what is conventionally necessary are collected to enable the ground processing algorithm to detect and remove harmful interference. Multiple RFI detectors are run on the ground, and their outputs are combined for maximum likelihood of detection to remove the RFI within a footprint. The capabilities of the hardware and software systems are successfully demonstrated using test data collected with a SMAP radiometer engineering test unit.

Description: Global Positioning System (GPS) multipath signals can be used to infer volumetric soil moisture around a GPS antenna. While most GPS users concentrate on the signal that travels directly from the satellite to the antenna, the signal that is reflected by nearby surfaces contains information about the environment surrounding the antenna. The interference between the direct and reflected signals produces a modulation that can be observed in temporal variations of the signal-to-noise ratio (SNR) data recorded by the GPS receiver. Changes in the dielectric constant of the soil, which are associated with fluctuations in soil moisture, affect the effective reflector height, amplitude, and phase of the multipath modulation. Empirical studies have shown that these changes in SNR data are correlated with near-surface volumetric soil moisture. This study uses an electrodynamic single-scattering forward model to test the empirical relationships observed in field data. All three GPS interferogram metrics (effective reflector height, phase, and amplitude) are affected by soil moisture in the top 5 cm of the soil; surface soil moisture ( $〈 1hbox{-}hbox{cm}$ depth) exerts the strongest control. Soil type exerts a negligible impact on the relationships between GPS interferogram metrics and soil moisture. Phase is linearly correlated with surface soil moisture. The slope of the relationship is similar to that observed in field data. Amplitude and effective reflector height are also affected by soil moisture, although the relationship is nonlinear. Phase is the best metric derived from GPS data to use as a proxy for soil moisture variations.

Description: In the field of remote sensing, the unmixing of hyperspectral images is usually based on the use of a mixing model. Most existing spectral unmixing methods, used in the reflective range (0.4–2.5 $muhbox{m}$ ), rely on a linear model of endmember reflectances. Nevertheless, such a model supposes the pixels at the ground level to be uniformly irradiated and the scene to be flat. When considering a 3-D landscape, such a model is no longer valid as irradiated and shadowed areas are present, as well as radiative interactions between facing surfaces. This paper introduces a new mixing model adapted to urban environments and which aims to overcome these limitations. This model is derived from physical equations based on radiative transfer theory, and its analytic expression is linear–quadratic. Similar models have already been used in the literature for unmixing purposes but without being justified by physical analysis. Our proposed model is validated using a synthetic but realistic European 3-D urban scene. Then, simplifications are introduced, based on a study of the different radiative components contributing to the signal in a way to make the model easy to use for spectral unmixing. This paper also shows that the quadratic term cannot be neglected in many cases in urban environments since it can, e.g., range from 15% to 20% of the reflectances in canyons.

Description: The detection of marine oil slicks using satellite sun-glittered optical imagery has been recently assessed. As the nature of the imaging mechanism involves the altered features of the wind-roughened oil-covered sea surface, it is expected that the radiation reflected from the oil–water system carries information about the physical properties of the floating oil layer. In this paper, we report an investigation on the capability to retrieve the average thickness of thin marine oil slicks by using the sun-glittered component of the solar radiation in the near-infrared (NIR) bands of MEdium Resolution Imaging Spectrometer Instrument (MERIS) and MODerate Resolution Imaging Spectroradiometer (MODIS) images. The developed procedure exploits the Cox and Munk model to compute sun glint reflectance at the sea surface level for both clean and oil polluted sea surface as well. It is assumed that the Fresnel reflection coefficient of the oil-water system carries the relevant optical dependence on oil layer thickness and oil type. The expected oil-water system reflectance is computed by taking into account the non-uniform spatial distribution of the oil volume. This is achieved by considering a pdf of oil thicknesses that matches the observations on controlled oil slicks already reported in the scientific literature. MERIS and MODIS images gathered during the Lebanon oil spill occurred on July and August 2006 were selected as case study. When available, co-located SAR imagery was also considered to corroborate NIR-detected oil slicks.

Description: Robust small target detection of low signal-to-noise ratio (SNR) is very important in infrared search and track applications for self-defense or attacks. Consequently, an effective small target detection algorithm inspired by the contrast mechanism of human vision system and derived kernel model is presented in this paper. At the first stage, the local contrast map of the input image is obtained using the proposed local contrast measure which measures the dissimilarity between the current location and its neighborhoods. In this way, target signal enhancement and background clutter suppression are achieved simultaneously. At the second stage, an adaptive threshold is adopted to segment the target. The experiments on two sequences have validated the detection capability of the proposed target detection method. Experimental evaluation results show that our method is simple and effective with respect to detection accuracy. In particular, the proposed method can improve the SNR of the image significantly.

Description: Dual and compact polarimetric (CP) synthetic aperture radar (SAR) provide more target information than single-polarization SAR systems with less stringent data requirements than fully polarimetric SAR systems. Considering the incomplete nature of CP data to establish the quad or fully polarimetric scattering matrix, the applied CP mode determines the captured scattering properties. Present formalism enables investigation of various CP modes (e.g., general elliptical transmission) and products for general target backscattering characterization. This is done through decomposition of quad-pol covariance and CP modeling of respective basic scattering mechanisms. On this basis, derivation of optimal CP modes and design of suitable CP products are explored. In this approach, the need for a priori target assumptions is removed. The link established between general CP and quad polarimetric covariance matrices can be used to examine target property assumptions using various CP modes data. Accordingly, means is also provided to explore a priori target property assumptions and their suitability required for expansion of 2 $times$ 2 CP covariance matrix to perform the pseudo-quad-polarimetric covariance analysis.

Description: A new image-registration method is presented by integrating the area-based and feature-based methods. The integrated method is characterized by a novel similarity metric based on spatial and mutual information (SMI), the ant colony optimization for continuous domain $(ACO_{BBR})$ , and a two-phase searching strategy. The SMI-based metric takes into account both spatial relations of detected features [spatial information (SI)] and the mutual information (MI) between the reference and sensed images. The spatial relation is to derive a fast transformation of the near global optimum without specifying the initial searching range. The MI is to obtain an optimal transformation with high accuracy. $ACO_{ BBR}$ is adopted to optimize SMI for the first time in this paper, as the function of SMI is generally non-convex and irregular. In addition, a two-phase searching strategy is designed to improve the performance of $ACO_{BBR}$ . Phase-1 only considers the SI and finds some low-accurate solutions. Phase-2 considers both SI and MI so it is to search for a more accurate solution. These two phases are switched according to the diversity of the solutions. The proposed integrated method has been tested using the remote-sensing images acquired from different sensors, including TM, SPOT, and SAR. The experimental results indicate that the SMI-based metric is more robust than the conventional metrics which consider SI or MI alone. This method is able to achieve a highly accurate automatic registration of multisensor images.

Description: Directional effects in airborne imaging spectrometer (IS) data are mainly caused by anisotropic reflectance behavior of surfaces, commonly described by bi-directional reflectance distribution functions (BRDF). The radiometric and spectral accuracy of IS data is known to be highly influenced by such effects, which prevents consistent comparison of products. Several models were developed to approximate surface reflectance anisotropy for multi-angular observations. Few studies were carried out using such models for airborne flight lines where only a single observation is available for each ground location. In the present work, we quantified and corrected reflectance anisotropy on a single airborne HyMap flight line using a Ross–Li model. We stratified the surface in two vegetation structural types (different in vertical structuring) using spectral angle mapping, to generate a structure dependent set of angular observations. We then derived a suite of products [indices (structure insensitive pigment index, normalized difference vegetation index, simple ratio index, and anthocyanin reflectance index) and inversion-based (SAIL/PROSPECT—leaf area index, Cw, Cdm, Cab)] from corrected and uncorrected images. Non-parametric analysis of variance (Kruskal–Wallis test) showed throughout significant improvements in products from corrected images. Data correction resulting in airborne nadir BRDF adjusted reflectance (aNBAR) showed uncertainty reductions from 60 to 100% $(hbox{p-value} = 0.05)$ as compared to uncorrected and nadir observations. Using sparse IS data acquisitions, the use of fully parametrized BRDF models is limited. Our normalization scheme is straightforward and can be applied with illumination and observation geometry being the only a priori information. We recommend aNBAR generation to precede any higher level airborne IS product generation based on reflectance data.

Description: To deliver sample estimates provided with the necessary probability foundation to permit generalization from the sample data subset to the whole target population being sampled, probability sampling strategies are required to satisfy three necessary not sufficient conditions: 1) All inclusion probabilities be greater than zero in the target population to be sampled. If some sampling units have an inclusion probability of zero, then a map accuracy assessment does not represent the entire target region depicted in the map to be assessed. 2) The inclusion probabilities must be: a) knowable for nonsampled units and b) known for those units selected in the sample: since the inclusion probability determines the weight attached to each sampling unit in the accuracy estimation formulas, if the inclusion probabilities are unknown, so are the estimation weights. This original work presents a novel (to the best of these authors' knowledge, the first) probability sampling protocol for quality assessment and comparison of thematic maps generated from spaceborne/airborne very high resolution images, where: 1) an original Categorical Variable Pair Similarity Index (proposed in two different formulations) is estimated as a fuzzy degree of match between a reference and a test semantic vocabulary, which may not coincide, and 2) both symbolic pixel-based thematic quality indicators (TQIs) and sub-symbolic object-based spatial quality indicators (SQIs) are estimated with a degree of uncertainty in measurement in compliance with the well-known Quality Assurance Framework for Earth Observation (QA4EO) guidelines. Like a decision-tree, any protocol (guidelines for best practice) comprises a set of rules, equivalent to structural knowledge, and an order of presentation of the rule set, known as procedural knowledge. The combination of these two levels of knowledge makes an original protocol worth more than the sum of its parts. The several degrees of novelty of the proposed probability s- mpling protocol are highlighted in this paper, at the levels of understanding of both structural and procedural knowledge, in comparison with related multi-disciplinary works selected from the existing literature. In the experimental session, the proposed protocol is tested for accuracy validation of preliminary classification maps automatically generated by the Satellite Image Automatic Mapper (SIAM™) software product from two WorldView-2 images and one QuickBird-2 image provided by DigitalGlobe for testing purposes. In these experiments, collected TQIs and SQIs are statistically valid, statistically significant, consistent across maps, and in agreement with theoretical expectations, visual (qualitative) evidence and quantitative quality indexes of operativeness (OQIs) claimed for SIAM™ by related papers. As a subsidiary conclusion, the statistically consistent and statistically significant accuracy validation of the SIAM™ pre-classification maps proposed in this contribution, together with OQIs claimed for SIAM™ by related works, make the operational (automatic, accurate, near real-time, robust, scalable) SIAM™ software product eligible for opening up new inter-disciplinary research and market opportunities in accordance with the visionary goal of the Global Earth Observation System of Systems initiative and the QA4EO international guidelines.

Description: Modern research in the field of synthetic aperture radar (SAR) technology requires intensive simulations. The most accurate solution would be achieved by applying full-wave electromagnetic (EM) simulations. However, such an approach requires extremely huge computational efforts, mainly due to the large dimensions of the considered objects with respect to a wavelength. For that reason, most of currently used radar simulators are based on an optical approach, such as geometrical optics (GO). Full-wave EM methods require much more computational resources and are usually applicable to the analysis of geometries no larger than several wavelengths. Nowadays, standard desktop computer platforms are still equipped with too small computational resources to carry out full-wave EM simulations of large scenes considered in radar applications. This paper presents a new concept of hybrid analysis, based on GO enhanced with full-wave EM simulations of larger facets—of the size of a few radar resolution cells. The SAR raw radar simulator described in this paper allows a complex and realistic simulation of any scene under radar observation to be performed. The scene can be defined using any computer-aided-design software generating digital terrain model (DTM). It also allows using real DTMs gathered with, e.g., light detection and ranging systems.

Description: An image fusion method must ideally preserve both the detail of the panchromatic image and the color of the multispectral image. Existing image fusion methods incur the gamut problem of creating new colors which fall out of the RGB cube. These methods solve the problem by color clipping which yields undesirable color distortions and contrast reductions. An improved nonlinear IHS (intensity, hue, saturation; iNIHS) color space and related color transformations are proposed in this paper to solve the gamut problem without appealing to color clipping. The iNIHS space includes two halves, one being constructed from the lower half of the RGB cube by RGB to IHS transformations, and the other from the upper half of the RGB cube by CMY to IHS transformations. While incurring no out-of-gamut colors, desired intensity substitutions and additions in substitutive and additive image fusions, respectively, are all achievable, with the saturation component regulated within the maximum attainable range. Good experimental results show the feasibility of the proposed method.

Description: Existing models for the short-wave spectrum of the sea surface are not consistent with microwave satellite data when multi-bands and multi-incidence data sets are considered. We devise a simple parametric model for the short-wave omnidirectional spectrum of the sea surface on the basis of a three-band (C, Ku, and Ka) and multi-incidence (low, moderate, and large) data set and an improved analytical scattering model, namely the non-Gaussian Weighted Curvature Approximation. This spectrum is also constrained by several optical measurements which provide a priori conditions on the total and filtered mean-square slopes. It is compared with classical models such as Elfouhaily and Kudryavtsev unified curvature spectra. Significant differences are observed at wave numbers corresponding to the range of decimeter scales. The new spectrum is by construction fully consistent with the omnidirectional normalized radar cross section of the multi-band data set.

Description: Full-wave modeling of ground-penetrating radar (GPR) data using Green's functions for wave propagation in planar layered media and antenna characteristic global reflection and transmission functions for describing far-field antenna effects, including antenna-medium interactions, has shown a great potential for nondestructive characterization of soils and materials. The accuracy of the retrieved parameters in the GPR data inversion depends on the accuracy of the GPR external calibration. In this research we studied the stability and the repeatability of two different GPR systems, namely, frequency- and time-domain systems. A combination of a vector network analyzer and 800–5200 MHz horn antenna was used as a frequency-domain GPR (FD-GPR) whereas a GSSI GPR system using a 900 MHz bowtie antenna was used as a time-domain GPR (TD-GPR). Both GPR systems including their antennas were calibrated several times using measurements with the antennas at different heights over a perfect electric conductor (PEC) in the laboratory as well as over a water layer. In addition, measurements were performed over a thin water layer and a relatively thick sandy soil layer as validating medium. The results showed that the FD-GPR is relatively stable while the TD-GPR presents a significant drift which can be accounted for using corrections based on the air direct-coupling waves (free-space measurements). Water- and PEC-based calibrations provided very similar results for the GPR calibration functions. Inversions for the water layer and the sandy soil layer provided reliable results and showed a high degree of the repeatability for both radar systems. The error on the calibration based on inaccurate antenna heights over PEC showed the significant errors on the inversion results for the directive antenna (horn antenna) but less error for the bowtie antenna. This analysis demonstrated the general validity of the proposed far-field radar modeling approach, not only with respect to fre- uency and time domain radars but as well with respect to the calibrating medium.

Description: Overlapping soil moisture time series derived from two satellite microwave radiometers (the Soil Moisture and Ocean Salinity (SMOS) and the Advanced Microwave Scanning Radiometer–Earth Observing System) are used to generate a soil moisture time series from 2003 to 2010. Two statistical methodologies for generating long homogeneous time series of soil moisture are considered. Generated soil moisture time series using only morning satellite overpasses are compared to ground measurements from four watersheds in the U.S. with different climatologies. The two methods, cumulative density function (CDF) matching and copulas, are based on the same statistical theory, but the first makes the assumption that the two data sets are ordered the same way, which is not needed by the second. Both methods are calibrated in 2010, and the calibrated parameters are applied to the soil moisture data from 2003 to 2009. Results from these two methods compare well with ground measurements. However, CDF matching improves the correlation, whereas copulas improve the root-mean-square error.

Description: Building change detection is a major issue for urban area monitoring. Due to different imaging conditions and sensor parameters, 2-D information delivered by satellite images from different dates is often not sufficient when dealing with building changes. Moreover, due to the similar spectral characteristics, it is often difficult to distinguish buildings from other man-made constructions, like roads and bridges, during the change detection procedure. Therefore, stereo imagery is of importance to provide the height component which is very helpful in analyzing 3-D building changes. In this paper, we propose a change detection method based on stereo imagery and digital surface models (DSMs) generated with stereo matching methodology and provide a solution by the joint use of height changes and Kullback–Leibler divergence similarity measure between the original images. The Dempster–Shafer fusion theory is adopted to combine these two change indicators to improve the accuracy. In addition, vegetation and shadow classifications are used as no-building change indicators for refining the change detection results. In the end, an object-based building extraction method based on shape features is performed. For evaluation purpose, the proposed method is applied in two test areas, one is in an industrial area in Korea with stereo imagery from the same sensor and the other represents a dense urban area in Germany using stereo imagery from different sensors with different resolutions. Our experimental results confirm the efficiency and high accuracy of the proposed methodology even for different kinds and combinations of stereo images and consequently different DSM qualities.

Description: There is a need for scattering models that link quantitatively synthetic aperture radar (SAR) interferometric observables to soil moisture. In this paper, we propose a model based on plane waves and the Born approximation, deriving first the vertical complex wavenumbers in the soil as a function of geometrical and dielectric properties and successively the complex interferometric coherences. It is observed that soil moisture behaves on the phase in a similar way as tomography does, breaking the phase consistency in triplets of interferograms. The proposed model is validated with L-band airborne SAR data; preliminary inversion results based on interferogram triplets and coherence magnitudes are presented.

Description: Statistical measures of patterns (textures) in surface roughness are used to quantitatively differentiate volcanic deposit facies on the Pumice Plain, on the northern flank of Mount St. Helens (MSH). Surface roughness values are derived from a Light Detection and Ranging (LiDAR) point cloud collected in 2004 from a fixed-wing airborne platform. Patterns in surface roughness are characterized using co-occurrence texture statistics. Pristine-pyroclastic, reworked-pyroclastic, mudflow, boulder beds, eroded lava flows, braided streams, and other units within the Pumice Plain are all found to have significantly distinct roughness textures. The MSH deposits are reasonably accessible, and the textural variations have been verified in the field. Results of this work indicate that by affecting the distribution of large clasts and tens-of-meter scale landforms, modification of pyroclastic deposits by lahars alters the morphology of the surface in detectable quantifiable ways. When a lahar erodes a pyroclastic deposit, surface roughness increases, as does the randomness in the deposit surface. Conversely, when a lahar deposits material, the resulting landforms are less rough but more random than pristine pumice-rich pyroclastic deposits. By mapping these relationships and others, volcanic deposit facies can be differentiated. This new method of mapping, based on roughness texture, has the potential to aid mapping efforts in more remote regions, both on this planet and elsewhere in the solar system.

Description: The rich data provided by high-resolution satellite imagery allow us to directly model aerial scenes by understanding their spatial and structural patterns. While pixel- and object-based classification approaches are widely used for satellite image analysis, often these approaches exploit the high-fidelity image data in a limited way. In this paper, we explore an unsupervised feature learning approach for scene classification. Dense low-level feature descriptors are extracted to characterize the local spatial patterns. These unlabeled feature measurements are exploited in a novel way to learn a set of basis functions. The low-level feature descriptors are encoded in terms of the basis functions to generate new sparse representation for the feature descriptors. We show that the statistics generated from the sparse features characterize the scene well producing excellent classification accuracy. We apply our technique to several challenging aerial scene data sets: ORNL-I data set consisting of 1-m spatial resolution satellite imagery with diverse sensor and scene characteristics representing five land-use categories, UCMERCED data set representing twenty one different aerial scene categories with sub-meter resolution, and ORNL-II data set for large-facility scene detection. Our results are highly promising and, on the UCMERCED data set we outperform the previous best results. We demonstrate that the proposed aerial scene classification method can be highly effective in developing a detection system that can be used to automatically scan large-scale high-resolution satellite imagery for detecting large facilities such as a shopping mall.

Description: The Advanced Microwave Scanning Radiometer-EOS (AMSR-E) on Aqua and WindSat on Coriolis instruments have collected multichannel passive microwave data over the global land and oceans since 2002 and 2003, respectively. AMSR-E on Aqua ceased operation in October 2011 due to a malfunction in the antenna scanning mechanism. AMSR-E and WindSat have similar frequencies, bandwidths, polarizations, incidence angles and instantaneous fields of view (IFOVs), but there are some differences in their configurations. The altitudes and local overpass times also differ between the AMSR-E and WindSat sensors. The time series of data from the two instruments have a long period of overlap, which can be used to intercompare and cross-calibrate the instrument data sets taking into account the instrument differences. This would allow retrieval of geophysical parameters using common algorithms that could take advantage of the increased time duration and sampling coverage afforded by combining data from the two sensors. In this paper, we focus on land applications and compare the multichannel data from these two sensors over land. Channels useful primarily for soil moisture and vegetation water content studies (i.e., $sim$ 6, $sim$ 10, $sim$ 18, and $sim$ 37 GHz at H- and V-pol) are used in the comparisons. To minimize differences caused by surface temperature effects related to local overpass times, only descending passes (with Equator crossing times for AMSR-E of 1:30 a.m. and WindSat 6:00 a.m.) are considered. Homogeneous and temporally stable sites such as Dome-C, Antarctica and the Amazon forest, and a flat and bare region in the Sahara desert are chosen to evaluate similarities and differences among comparable channel obs- rvations. Taking into consideration the sensor configurations and geophysical conditions during the descending overpasses, reasonably good agreement is observed between AMSR-E and WindSat measurements over the globe.

Description: Optical remote sensing imagery offers a cost-effective alternative to echo sounding and bathymetric light detection and ranging surveys for deriving high density bottom depth estimates for coastal and inland water bodies. The common practice of previous studies has been to calibrate a single global bathymetric inversion model for an entire image scene. The performance of conventional global models is limited when the bottom type and water quality vary spatially within the scene. To address the inadequacy of the conventional global models, this paper presents a geographically adaptive inversion model to better estimate bottom depth. Although the general mathematical form of the geographically adaptive model is the same, model parameters are optimally determined within a geographical region or a local area, in contrast to the entire scene in the global inversion model. By using high-resolution IKONOS and moderate-resolution Landsat satellite images, we demonstrated that regionally and locally calibrated inversion models can effectively address the problems introduced by spatial heterogeneity in water quality and bottom type, and provide significantly improved bathymetric estimates for more complex coastal waters.

Description: A classifier that couples nearest-subspace classification with a distance-weighted Tikhonov regularization is proposed for hyperspectral imagery. The resulting nearest-regularized-subspace classifier seeks an approximation of each testing sample via a linear combination of training samples within each class. The class label is then derived according to the class which best approximates the test sample. The distance-weighted Tikhonov regularization is then modified by measuring distance within a locality-preserving lower-dimensional subspace. Furthermore, a competitive process among the classes is proposed to simplify parameter tuning. Classification results for several hyperspectral image data sets demonstrate superior performance of the proposed approach when compared to other, more traditional classification techniques.

Description: NASA's Soil Moisture Active Passive (SMAP) mission will carry the first combined spaceborne L-band radiometer and Synthetic Aperture Radar (SAR) system with the objective of mapping near-surface soil moisture and freeze/thaw state globally every 2–3 days. SMAP will provide three soil moisture products: i) high-resolution from radar ( $sim$ 3 km), ii) low-resolution from radiometer ( $sim$ 36 km), and iii) intermediate-resolution from the fusion of radar and radiometer ( $sim$ 9 km). The Soil Moisture Active Passive Experiments (SMAPEx) are a series of three airborne field experiments designed to provide prototype SMAP data for the development and validation of soil moisture retrieval algorithms applicable to the SMAP mission. This paper describes the SMAPEx sampling strategy and presents an overview of the data collected during the three experiments: SMAPEx-1 (July 5–10, 2010), SMAPEx-2 (December 4–8, 2010) and SMAPEx-3 (September 5–23, 2011). The SMAPEx experiments were conducted in a semi-arid agricultural and grazing area located in southeastern Australia, timed so as to acquire data over a seasonal cycle at various stages of the crop growth. Airborne L-band brightness temperature ( $sim$ 1 km) and radar backscatter ( $sim$ 10 m) observations were collected over an area the size of a single SMAP footprint (38 km $times$ 36 km at 35 $^{circ}$ latitude) with a 2–3 days revisit time, providing SMAP-like data for testing of radiometer-only, radar-only and combined radiomete- -radar soil moisture retrieval and downscaling algorithms. Airborne observations were supported by continuous monitoring of near-surface (0–5 cm) soil moisture along with intensive ground monitoring of soil moisture, soil temperature, vegetation biomass and structure, and surface roughness.

Description: Time-domain autocovariance processing is widely accepted as a computationally efficient method to estimate the first three spectral moments of Doppler weather radar signals (i.e., mean signal power, mean Doppler velocity, and spectrum width). However, when signals with different frequency content (e.g., ground clutter) contaminate the weather signal, spectral processing using the periodogram estimator of the power spectral density (PSD) is the preferred tool of analysis. After spectral processing (i.e., filtering), a PSD-based autocorrelation estimator is typically employed to produce unbiased estimates of the weather-signal spectral moments. However, the PSD does not convey explicit phase information, which has the potential to aid in the spectral analysis of radar signals. In this paper, the autocorrelation spectral density (ASD) is introduced for spectral analysis of weather-radar signals as a generalization of the classical PSD, and an ASD-based autocorrelation estimator is proposed to produce unbiased estimates of the weather-signal spectral moments. A significant advantage of the ASD over the PSD is that it provides explicit phase information that can be exploited to identify and remove certain types of contaminant signals. Thus, the ASD provides an alternative means for spectral analysis, which can lead to improved quality of meteorological data from weather radars.