<![CDATA[ IEEE Transactions on Geoscience and Remote Sensing - new TOC ]]>
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TOC Alert for Publication# 36 2016June 23<![CDATA[Two-Year Comparison of Airborne Measurements of CO<sub>2</sub> and CH<sub>4</sub> With GOSAT at Railroad Valley, Nevada]]>3) regularly over California and Nevada. Airborne instruments measuring GHGs and O_{3} are installed in a wing pod of an Alpha Jet aircraft and operated from the National Aeronautics and Space Administration Ames Research Center at Moffett Field, CA. The instruments yield precise and accurate in situ vertical profiles of atmospheric carbon dioxide (CO_{2}), methane (CH_{4}), and O_{3}. Measurements of vertical profiles of GHGs and O_{3} over Railroad Valley, NV have been conducted directly under the Greenhouse gases Observing SATellite (GOSAT) over passes on a monthly basis as part of the AJAX project since June 2011. The purpose of this work is to calculate aircraft-based dry-air mole fractions of the GHGs for the validation of GOSAT data products. This study expands and improves our previous comparisons by evaluating three algorithms against 24 months of in situ data collected over a Gain-M target. We used three different algorithms: Atmospheric CO_{2} Observations from Space (ACOS v3.4r3), Remote Sensing of Greenhouse Gases for Carbon Cycle Modeling (RemoteC v2.3.5FP), and National Institute for Environmental Studies (NIES v2.11). We find that the CO_{2} average differences of ACOS and RemoteC from AJAX are 0.26% and 0.24%, respectively. The difference between NIES and AJAX is 0.96%, which is higher than that of ACOS and RemoteC. The CH_{4} average differences for RemoteC and NIES are 2.1% and 1.7%, respectively.]]>548436743751248<![CDATA[Underground Incrementally Deployed Magneto-Inductive 3-D Positioning Network]]>548437643911756<![CDATA[Incoherent Target Scattering Decomposition of Polarimetric SAR Data Based on Vector Model Roll-Invariant Parameters]]>548439244013442<![CDATA[Semisupervised Subspace-Based DNA Encoding and Matching Classifier for Hyperspectral Remote Sensing Imagery]]>548440244184954<![CDATA[Wind Speed Retrieval Algorithm for the Cyclone Global Navigation Satellite System (CYGNSS) Mission]]>et al ., 2014. The approach is applied to the specific orbital measurement geometry, antenna, and receiver hardware characteristics of the CYGNSS mission. Several additional processing steps have also been added to improve the performance. A best weighted estimator is used to optimally combine two different partially correlated estimates of the winds by taking their weighted average. The optimal weighting dynamically adjusts for variations in the signal-to-noise ratio of the observations that result from changes in the measurement geometry. Variations in the incidence angle of the measurements are accounted for by the use of a 2-D geophysical model function that depends on both wind speed and incidence angle. Variations in the propagation time and signal Doppler shift at different measurement geometries affect the instantaneous spatial resolution of the measurements, and these effects are compensated by a variable temporal integration of the data. In addition to a detailed description of the algorithm itself, the root-mean-square wind speed retrieval error is characterized as a function of the measurement geometry and the wind speed using a detailed mission end-to-end simulator.]]>548441944322737<![CDATA[Calibration of Compact Polarimetric SAR Images Using Distributed Targets and One Corner Reflector]]>548443344442364<![CDATA[Soil Moisture Retrieval in Agricultural Fields Using Adaptive Model-Based Polarimetric Decomposition of SAR Data]]>3/m^{3}, respectively, for surface and double-bounce components. However, large variability in the achieved soil moisture accuracy was observed, depending on the presence of row structures in the underlying soil surface.]]>548444544603225<![CDATA[Efficient Multiple Feature Fusion With Hashing for Hyperspectral Imagery Classification: A Comparative Study]]>548446144784943<![CDATA[Estimation of ATMS Antenna Emission From Cold Space Observations]]>548447944871386<![CDATA[Radiometric Correction of Airborne Radar Images Over Forested Terrain With Topography]]>548448845005614<![CDATA[Application of AMSR-E and AMSR2 Low-Frequency Channel Brightness Temperature Data for Hurricane Wind Retrievals]]>B) observations acquired at the 6.9-GHz horizontal polarization channel by the AMSR-E and AMSR2 onboard the Earth Observing System Aqua and Global Change Observation Mission-Water 1 satellites are selected for wind retrieval due to the fact that the signal at this frequency is sensitive to high wind speeds but less sensitive to rain scatter than those acquired at other higher frequency channels. The AMSR-E and AMSR2 observations of 53 hurricanes between 2002 and 2014 are collected and collocated with stepped-frequency microwave radiometer (SFMR) measurements. Based on the small slope approximation/small perturbation method model and an ocean surface roughness spectrum, the wind speeds are retrieved from the T_{B} data and validated against the SFMR measurements. The statistical comparison of the entire data set shows that the bias and root-mean-square error (RMSE) of the retrieved wind speeds are 1.11 and 4.34 m/s, respectively, which suggests that the proposed method can obtain high wind speeds under hurricane conditions. Two case studies show that the wind speed retrieval bias and RMSE are 1.08 and 3.93 m/s for Hurricane Earl and 0.09 and 3.23 m/s for Hurricane Edouard, respectively. The retrieved wind speeds from the AMSR-E and AMSR2 continuous three-day observations clearly show the process of hurricane intensification and weakening.]]>548450145121981<![CDATA[MST Radars of Chinese Meridian Project: System Description and Atmospheric Wind Measurement]]>2. This antenna array arrangement forms the five symmetric radar beams of vertex, east, west, south, and north. The beamwidth is 3.2°, the maximum directive gain is 34.8 dB, and the total transmitting peak power is ~172 kW. The zenith angle of the oblique beams is adjustable between 0 and 20° with a 1° step. The average power aperture product of the radars is 3.2×10^{8} Wm^{2}. There are three operating modes of the MST radars, including the low, middle, and high modes, applied to observe the troposphere, stratosphere, and mesosphere, respectively. Thus, these two coherent pulse Doppler MST radars have the ability to study the features of the midlatitude atmospheric turbulence and wind field vector from the troposphere to the lower thermosphere with high spatiotemporal resolution. In this paper, the antenna array, system hardware, and signal processing methods, as well as the typical observation results of the troposphere, stratosphere, and mesosphere, are introduced.]]>548451345233358<![CDATA[Sea Ice Concentration Estimation During Melt From Dual-Pol SAR Scenes Using Deep Convolutional Neural Networks: A Case Study]]>548452445331577<![CDATA[Spectral Processing for Step Scanning Phased-Array Radars]]>5484534454312146<![CDATA[Spectral–Spatial Feature Extraction for Hyperspectral Image Classification: A Dimension Reduction and Deep Learning Approach]]>548454445542590<![CDATA[Scattering From Inhomogeneous Dielectric Cylinders With Finite Length]]>548455545693163<![CDATA[A Parameterized ASCAT Measurement Spatial Response Function]]> . The SRF results from a combination of the antenna response and the onboard processing and is computed during ground processing by modeling in detail the measurement geometry, as this is required for an accurate estimation. However, the computed SRF is not disseminated as part of the L1B data. For some applications of the L1B data, the SRF is additionally required. For these applications, an approximate description of the SRF is often sufficiently accurate, estimated from information contained in the L1B data, rather than from a full calculation based on the measurement geometry. This paper describes a parameterized model of the ASCAT SRF for each measurement. First, an SRF reference estimate that incorporates details on the ASCAT design and onboard measurement processing is created. A parameterized model is fit to the reference estimate. The parameterized SRF is computationally less demanding than the reference estimate and as such more useful for near-real-time processing. The two estimates are validated with the computed SRF used in ground processing and with the transponder data from calibration campaigns. Finally, to validate the SRF in a simple application, the land fraction (a measure of land contamination in near-coastal ocean measurements) is computed and compared to actual data for a sample region.]]>548457045791508<![CDATA[Detection of Fragmented Rectangular Enclosures in Very High Resolution Remote Sensing Images]]>548458045933398<![CDATA[A Generalized Metaphor of Chinese Restaurant Franchise to Fusing Both Panchromatic and Multispectral Images for Unsupervised Classification]]>548459446047989<![CDATA[Magnetic Induction-Based Positioning in Distorted Environments]]>548460546121316<![CDATA[A Radiative Transfer Model for Heterogeneous Agro-Forestry Scenarios]]>548461346282786<![CDATA[Disaggregation of Remotely Sensed Soil Moisture in Heterogeneous Landscapes Using Holistic Structure-Based Models]]>3/m^{3}. The clusters generated represented the data well and reduced the rmse by up to 40% during periods of high heterogeneity in LC and meteorological conditions. The Kullback-Leibler divergence (KLD) between the true SM and the disaggregated estimates is close to zero, for both vegetated and bare-soil LCs. The disaggregated estimates were compared with those generated by the principle of relevant information (PRI) method. The rmse for the PRI disaggregated estimates is higher than the rmse for the SRRM on each day of the season. The KLD of the disaggregated estimates generated by the SRRM is at least four orders of magnitude lower than those for the PRI disaggregated estimates, whereas the computational time needed was reduced by three times. The results indicate that the SRRM can be used for disaggregating SM with complex nonlinear correlations on a grid with high accuracy.]]>548462946413199<![CDATA[Hyperspectral Image Restoration via Iteratively Regularized Weighted Schatten <inline-formula> <img src="/images/tex/387.gif" alt="p"> </inline-formula>-Norm Minimization]]>5484642465910932<![CDATA[Retrieval of Leaf, Sunlit Soil, and Shaded Soil Component Temperatures Using Airborne Thermal Infrared Multiangle Observations]]>548466046711676<![CDATA[A Physics-Based Method to Retrieve Land Surface Temperature From MODIS Daytime Midinfrared Data]]>548467246791316<![CDATA[Relative Trajectory Estimation During Chang'e-2 Probe's Flyby of Asteroid Toutatis Using Dynamics, Optical, and Radio Constraints]]>548468046931802<![CDATA[Evapotranspiration Variations in the Mississippi River Basin Estimated From GPS Observations]]>548469447011741<![CDATA[Edge-Guided Image Object Detection in Multiscale Segmentation for High-Resolution Remotely Sensed Imagery]]>548470247112538<![CDATA[Spaceborne Synthetic Aperture Radar Data Focusing on Multicore-Based Architectures]]>548471247311881<![CDATA[An Efficient Solution to the Factorized Geometrical Autofocus Problem]]>5484732474810411<![CDATA[A Local Structure and Direction-Aware Optimization Approach for Three-Dimensional Tree Modeling]]>548474947571970<![CDATA[Assessment of Electromagnetic Absorption of Ice From Ice Core Measurements]]>in situ. In particular, using known temperature and dielectric profiles (DEP measurements), it is possible to evaluate the ice electromagnetic power absorption profile, valid at the drilling site. In the last decades, bedrock characterization through radio echo sounding surveys has been improved by the analysis of the power of radar echoes. This way, analysis of the electromagnetic properties of bedrock interfaces makes it possible to assess the physical characteristics and to distinguish between wet and dry conditions. Power variation of the received echoes also depends on ice absorption and on bedrock reflectivity due to specific physical conditions of the ice. In this paper, the propagation of electromagnetic waves through the ice sheet is examined, and in particular, a new method for establishing the electromagnetic absorption profile for ice from core drilling measurements is proposed and discussed. Variation in the ice absorption is deduced, starting from the analysis of ice core data from the European Project for Ice Coring in Antarctica (EPICA) at the Concordia station (Antarctica) and from the Greenland Ice Core Project (GRIP) site (Greenland). This direct method of measurement is proposed with the aim of defining common characteristics of the ice absorption rate that are valid both in Antarctica and in Greenland.]]>54847584763818<![CDATA[A Novel Hybrid Method for the Correction of the Theoretical Model Inversion in Bio/Geophysical Parameter Estimation]]>548476447742049<![CDATA[Simultaneously Sparse and Low-Rank Abundance Matrix Estimation for Hyperspectral Image Unmixing]]>1 and the weighted nuclear norm of the abundance matrix corresponding to a small area of the image determined by a sliding square window. This penalty term is then used to regularize a conventional quadratic cost function and impose simultaneous sparsity and low rankness on the abundance matrix. The resulting regularized cost function is minimized by: 1) an incremental proximal sparse and low-rank unmixing algorithm; and 2) an algorithm based on the alternating direction method of multipliers. The effectiveness of the proposed algorithms is illustrated in experiments conducted both on simulated and real data.]]>548477547892308<![CDATA[Robust Segmentation for Large Volumes of Laser Scanning Three-Dimensional Point Cloud Data]]>548479048052860<![CDATA[Target Classification Using the Deep Convolutional Networks for SAR Images]]>548480648172963<![CDATA[InSAR-Based Model Parameter Estimation of Probability Integral Method and Its Application for Predicting Mining-Induced Horizontal and Vertical Displacements]]>548481848323331<![CDATA[Dual-Polarization Radar Characteristics of Wind Turbines With Ground Clutter and Precipitation]]>548483348462967<![CDATA[Cross-Calibration of GF-1 PMS Sensor With Landsat 8 OLI and Terra MODIS]]>548484748541904<![CDATA[Wireless Transmission of MWD and LWD Signal Based on Guidance of Metal Pipes and Relay of Transceivers]]>548485548662401<![CDATA[A Comparison of Tropical Rainforest Phenology Retrieved From Geostationary (SEVIRI) and Polar-Orbiting (MODIS) Sensors Across the Congo Basin]]>548486748813763<![CDATA[Dirichlet Process Based Active Learning and Discovery of Unknown Classes for Hyperspectral Image Classification]]>548488248952438<![CDATA[Determination of Differential Code Bias of GNSS Receiver Onboard Low Earth Orbit Satellite]]>548489649053275<![CDATA[Spatial Correlations in SMOS Antenna: The Role of Effective Point Spread Functions]]>548490649161928<![CDATA[A Novel Synergetic Classification Approach for Hyperspectral and Panchromatic Images Based on Self-Learning]]>548491749283064<![CDATA[A Preliminary Evaluation of the SMAP Radiometer Soil Moisture Product Over United States and Europe Using Ground-Based Measurements]]>3 · m^{-3}, well within the mission requirement of 0.04 m^{3} · m^{-3}. The error sources of SMAP soil moisture product may be associated with the parameterization of vegetation and surface roughness but still needs to be tested and confirmed in more extent. Considering that the algorithms are still under refinement, it can be reasonably expected that hydrometeorological applications will benefit from the SMAP radiometer soil moisture product.]]>548492949402050<![CDATA[Open-Ended Coaxial Probe Technique for Dielectric Spectroscopy of Artificially Grown Sea Ice]]>548494149511375<![CDATA[A Multibaseline Pol-InSAR Inversion Scheme for Crop Parameter Estimation at Different Frequencies]]>zh_{V} <; 2.8 rad (κ_{z} is the vertical wavenumber). Furthermore, the variance of the estimates is inversely related to the number of baselines Nb. Compared with the dual-baseline case, the RMSD of the differential extinction is reduced by 45% (from 1.1 to 0.6 dB/m) when Nb = 5 baselines are employed, whereas its mean bias is independent of Nb. The proposed scheme has been assessed using a set of repeat-pass F-SAR acquisitions at L-, C-, and X-band of an agricultural area in Germany. Using two baselines, the height of maize and rape fields is estimated with an average 10% %RMSD if the inversion is carried out over L-band acquisitions. On the other hand, when X-band data are employed, one can obtain reliable estimates of wheat and barley height, with a %RMSD better than 24%. The study also indicates the existence of differential wave propagation effects through maize (Δσ = σ_{VV} - σ_{HH} between 0.7 and 1 dB/m) and rape (Δσ = -0.8 dB/m) canopies at L-band.]]>548495249704918<![CDATA[Spectral–Spatial Classification of Hyperspectral Images Using ICA and Edge-Preserving Filter via an Ensemble Strategy]]>548497149822956<![CDATA[Hyperspectral Unmixing With Endmember Variability via Alternating Angle Minimization]]>548498349931840<![CDATA[Assessment of the SMAP Passive Soil Moisture Product]]>3/m^{3} unbiased root-mean-square difference (ubRMSD), which approaches the SMAP mission requirement of 0.040 m^{3}/m^{3}.]]>5481143628