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Geoscience and Remote Sensing, IEEE Transactions on

Issue 4 • Date April 2007

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Displaying Results 1 - 25 of 36
  • [Front cover]

    Publication Year: 2007 , Page(s): C1
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  • IEEE Transactions on Geoscience and Remote Sensing publication information

    Publication Year: 2007 , Page(s): C2
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  • Table of contents

    Publication Year: 2007 , Page(s): 793 - 794
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  • Introduction to the Special Section on Image Information Mining for Earth Observation Data

    Publication Year: 2007 , Page(s): 795 - 798
    Cited by:  Papers (6)
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  • List of reviewers

    Publication Year: 2007 , Page(s): 799
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  • Use of Neural Networks for Automatic Classification From High-Resolution Images

    Publication Year: 2007 , Page(s): 800 - 809
    Cited by:  Papers (37)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1742 KB)  

    The effectiveness of multilayer perceptron (MLP) networks as a tool for the classification of remotely sensed images has been already proven in past years. However, most of the studies consider images characterized by high spatial resolution (around 15-30 m) while a detailed analysis of the performance of this type of classifier on very high resolution images (around 1-2 m) such as those provided by the Quickbird satellite is still lacking. Moreover, the classification problem is normally understood as the classification of a single image while the capabilities of a single network of performing automatic classification and feature extraction over a collection of archived images has not been explored so far. In this paper, besides assessing the performance of MLP for the classification of very high resolution images, we investigate on the generalization capabilities of this type of algorithms with the purpose of using them as a tool for fully automatic classification of collections of satellite images, either at very high or at high-resolution. In particular, applications to urban area monitoring have been addressed View full abstract»

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  • Relevance Criteria for Spatial Information Retrieval Using Error-Tolerant Graph Matching

    Publication Year: 2007 , Page(s): 810 - 817
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (385 KB)  

    In this paper, we present a graph-based approach for mining geospatial data. The system uses error-tolerant graph matching to find correspondences between the detected image features and the geospatial vector data. Spatial relations between objects are used to find a reliable object-to-object mapping. Graph matching is used as a flexible query mechanism to answer the spatial query. A condition based on the expected graph error has been presented which allows determining the bounds of error tolerance and, in this way, characterizes the relevancy of a query solution. We show that the number of null labels is an important measure to determine relevancy. To be able to correctly interpret the matching results in terms of relevancy, the derived bounds of error tolerance are essential View full abstract»

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  • Interactive Remote-Sensing Image Retrieval Using Active Relevance Feedback

    Publication Year: 2007 , Page(s): 818 - 826
    Cited by:  Papers (31)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (936 KB) |  | HTML iconHTML  

    As the resolution of remote-sensing imagery increases, the full complexity of the scenes becomes increasingly difficult to approach. User-defined classes in large image databases are often composed of several groups of images and span very different scales in the space of low-level visual descriptors. The interactive retrieval of such image classes is then very difficult. To address this challenge, we evaluate here, in the context of satellite image retrieval, two general improvements for relevance feedback using support vector machines (SVMs). First, to optimize the transfer of information between the user and the system, we focus on the criterion employed by the system for selecting the images presented to the user at every feedback round. We put forward an active-learning selection criterion that minimizes redundancy between the candidate images shown to the user. Second, for image classes spanning very different scales in the low-level description space, we find that a high sensitivity of the SVM to the scale of the data brings about a low retrieval performance. We argue that the insensitivity to scale is desirable in this context, and we show how to obtain it by the use of specific kernel functions. Experimental evaluation of both ranking and classification performance on a ground-truth database of satellite images confirms the effectiveness of our approach View full abstract»

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  • Image Time-Series Data Mining Based on the Information-Bottleneck Principle

    Publication Year: 2007 , Page(s): 827 - 838
    Cited by:  Papers (15)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1357 KB) |  | HTML iconHTML  

    Satellite image time series (SITS) consist of a time sequence of high-resolution spatial data. SITS may contain valuable information, but it may be deeply hidden. This paper addresses the problem of extracting relevant information from SITS based on the information-bottleneck principle. The method depends on suitable model selection, coupled with a rate-distortion analysis for determining the optimal number of clusters. We present how to use this method with the Gauss-Markov random fields and the autobinomial random fields model families in order to characterize the spatio-temporal structures contained in SITS. Experimental results on synthetic data and SITS from SPOT demonstrate the performance of the proposed methodology View full abstract»

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  • GeoIRIS: Geospatial Information Retrieval and Indexing System—Content Mining, Semantics Modeling, and Complex Queries

    Publication Year: 2007 , Page(s): 839 - 852
    Cited by:  Papers (47)  |  Patents (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (862 KB) |  | HTML iconHTML  

    Searching for relevant knowledge across heterogeneous geospatial databases requires an extensive knowledge of the semantic meaning of images, a keen eye for visual patterns, and efficient strategies for collecting and analyzing data with minimal human intervention. In this paper, we present our recently developed content-based multimodal Geospatial Information Retrieval and Indexing System (GeoIRIS) which includes automatic feature extraction, visual content mining from large-scale image databases, and high-dimensional database indexing for fast retrieval. Using these underpinnings, we have developed techniques for complex queries that merge information from heterogeneous geospatial databases, retrievals of objects based on shape and visual characteristics, analysis of multiobject relationships for the retrieval of objects in specific spatial configurations, and semantic models to link low-level image features with high-level visual descriptors. GeoIRIS brings this diverse set of technologies together into a coherent system with an aim of allowing image analysts to more rapidly identify relevant imagery. GeoIRIS is able to answer analysts' questions in seconds, such as "given a query image, show me database satellite images that have similar objects and spatial relationship that are within a certain radius of a landmark." View full abstract»

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  • Semantic-Sensitive Satellite Image Retrieval

    Publication Year: 2007 , Page(s): 853 - 860
    Cited by:  Papers (14)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (317 KB) |  | HTML iconHTML  

    Content-based image-retrieval techniques based on query scenes are a powerful means for exploration and mining of large remote sensing image databases. However, the gap between low-level unsupervised extracted features in content-based retrieval and the high-level semantic concepts of user queries limits the performance. Therefore, this paper proposes a specialized approach using a context-sensitive Bayesian network for semantic inference of segmented scenes. The regions' remote sensing related semantic concepts are inferred in a multistage process based on their spectral and textural characteristics as well as the semantics of adjacent regions. During the actual retrieval, the semantics are employed for the extraction of candidate scenes which are evaluated and ranked in a consecutive step. The approach was implemented and compared with a different strategy that utilizes the extracted features from the imagery directly to infer the semantics. In summary, the developed system achieved higher precision and recall rates using the same training data View full abstract»

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  • Detecting Man-Made Structures and Changes in Satellite Imagery With a Content-Based Information Retrieval System Built on Self-Organizing Maps

    Publication Year: 2007 , Page(s): 861 - 874
    Cited by:  Papers (14)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1230 KB) |  | HTML iconHTML  

    The increasing amount and resolution of satellite sensors demand new techniques for browsing remote sensing image archives. Content-based querying allows an efficient retrieval of images based on the information they contain, rather than their acquisition date or geographical extent. Self-organizing maps (SOMs) have been successfully applied in the PicSOM system to content-based image retrieval in databases of conventional images. In this paper, we investigate and extend the potential of PicSOM for the analysis of remote sensing data. We propose methods for detecting man-made structures, as well as supervised and unsupervised change detection, based on the same framework. In this paper, a database was artificially created by splitting each satellite image to be analyzed into small images. After training the PicSOM on this imagelet database, both interactive and off-line queries were made to detect man-made structures, as well as changes between two very high resolution images from different years. Experimental results were both evaluated quantitatively and discussed qualitatively, and suggest that this new approach is suitable for analyzing very high resolution optical satellite imagery. Possible applications of this work include interactive detection of man-made structures or supervised monitoring of sensitive sites View full abstract»

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  • A Systematic Approach to Wavelet-Decomposition-Level Selection for Image Information Mining From Geospatial Data Archives

    Publication Year: 2007 , Page(s): 875 - 878
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (407 KB) |  | HTML iconHTML  

    Recently, wavelet-based methods have been efficiently used for segmentation and primitive feature extraction to expedite the image-retrieval process of semantic-enabled frameworks for image information mining from geospatial data archives. However, the use of wavelets may introduce aliasing effects due to subband decimation at a certain decomposition level. This paper addresses the issue of selecting a suitable wavelet decomposition level, and a systematic selection process is developed. To validate the applicability of this method, a synthetic image is generated to qualitatively and quantitatively assess the performance. In addition, results for a Landsat-7 Enhanced Thematic Mapper Plus imagery archive are illustrated, and the F-measure is used to assess the feasibility of this method for the retrieval of different classes View full abstract»

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  • Multiyear On-Orbit Calibration and Performance of Terra MODIS Reflective Solar Bands

    Publication Year: 2007 , Page(s): 879 - 889
    Cited by:  Papers (43)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (703 KB) |  | HTML iconHTML  

    Terra Moderate Resolution Imaging Spectroradiometer (MODIS) has made continuous global observations for more than six years since its launch in December 1999. MODIS has 36 spectral bands: 20 reflective solar bands (RSBs) with wavelengths from 0.41-2.2 mum and 16 thermal emissive bands with wavelengths from 3.7-14.4 mum. It is a cross-track scanning radiometer that collects data at three nadir spatial resolutions: 0.25 km (2 bands), 0.5 km (5 bands), and 1 km (29 bands). An onboard solar diffuser (SD) and an SD stability monitor (SDSM) are used biweekly for RSB on-orbit radiometric calibration. Another onboard calibrator (OBC), a spectroradiometric calibration assembly, is used periodically to evaluate and monitor RSB spatial and spectral performance. In addition to measurements made using OBCs, lunar observations at nearly identical phase angles are used to track RSB calibration stability. This paper provides an overview of MODIS RSB on-orbit calibration algorithms and operational activities. It discusses sensor characteristics that could impact RSB calibration accuracy and data product quality, including degradation of the SD bidirectional reflectance factor (BRF), degradation of the scan mirror reflectance in the visible spectral region, and changes in operational configuration. The Terra MODIS OBCs have performed well in monitoring SD degradation and tracking changes in RSB response. Band 8 (0.41 mum) has experienced the largest response decrease with an approximate annual rate of 4.5% (mirror side 1). Band 9 (0.44 mum) has an annual response decrease of about 2.3% (mirror side 1). For most RSB bands with wavelengths greater than 0.5 mum, the annual response changes are generally less than 1.0%. Results from the SDSM on-orbit observations show that the SD BRF also has a similar wavelength-dependent degradation, with the largest degradation appearing at the shortest wavelengths. Among the 330 RSB detectors, there are no inoperable detectors, and only a few noisy d- etectors have appeared postlaunch View full abstract»

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  • Analysis of Random Step Frequency Radar and Comparison With Experiments

    Publication Year: 2007 , Page(s): 890 - 904
    Cited by:  Papers (20)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1999 KB) |  | HTML iconHTML  

    Linear stepped frequency radar is used in wide-band radar applications, such as airborne synthetic aperture radar (SAR), turntable inverse SAR, and ground penetration radar. The frequency is stepped linearly with a constant frequency change, and range cells are formed by fast Fourier transform processing. The covered bandwidth defines the range resolution, and the length of the frequency step restricts the nonambiguous range interval. A random choice of the transmitted frequencies suppresses the range ambiguity, improves covert detection, and reduces the signal interference between adjacent sensors. As a result of the random modulation, however, a noise component is added to the range/Doppler sidelobes. In this paper, relationships of random step frequency radar are compared with frequency-modulated continuous wave noise radar and the statistical characteristics of the ambiguity function and the sidelobe noise floor are analyzed. Algorithms are investigated, which reduce the sidelobes and the noise-floor contribution from strong dominating reflectors in the scene. Theoretical predictions are compared with Monte Carlo simulations and experimental data View full abstract»

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  • Combining Airborne Photographs and Spaceborne SAR Data to Monitor Temperate Glaciers: Potentials and Limits

    Publication Year: 2007 , Page(s): 905 - 924
    Cited by:  Papers (18)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3118 KB) |  | HTML iconHTML  

    Monitoring temperate glacier activity has become more and more necessary for economical and security reasons and as an indicator of the local effects of global climate change. Remote sensing data provide useful information on such complex geophysical objects, but they require specific processing techniques to cope with the difficult context of moving and changing features in high-relief areas. This paper presents the first results of a project involving four laboratories developing and combining specific methods to extract information from optical and synthetic aperture radar (SAR) data. Two different information sources are processed, namely: 1) airborne photography and 2) spaceborne C-band SAR interferometry. The difficulties and limitations of their processing in the context of Alpine glaciers are discussed and illustrated on two glaciers located in the Mont-Blanc area. The results obtained by aerial triangulation techniques provide digital terrain models with an accuracy that is better than 30 cm, which is compatible with the computation of volume balance and useful for precise georeferencing and slope measurement updating. The results obtained by SAR differential interferometry using European Remote Sensing Satellite images show that it is possible to measure temperate glacier surface velocity fields from October to April in one-day interferograms with approximately 20-m ground sampling. This allows to derive ice surface strain rate fields required to model the glacier flow. These different measurements are complementary to results obtained during the summer from satellite optical data and ground measurements that are available only in few accessible points View full abstract»

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  • Exploiting Spin Echo Decay in the Detection of Nuclear Quadrupole Resonance Signals

    Publication Year: 2007 , Page(s): 925 - 933
    Cited by:  Papers (12)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (356 KB) |  | HTML iconHTML  

    Nuclear quadrupole resonance (NQR) is a radio-frequency technique that can be used to detect the presence of quadrupolar nuclei, such as the 14N nucleus prevalent in many explosives and narcotics. In a typical application, one observes trains of decaying NQR echoes, in which the decay is governed by the spin echo decay time(s) of the resonant line(s). In most detection algorithms, these echoes are simply summed to produce a single echo with a higher signal-to-noise ratio, ignoring the decaying echo structure of the signal. In this paper, after reviewing current NQR signal models, we propose a novel NQR data model of the full echo train and detail why and how these echo trains are produced. Furthermore, we refine two recently proposed approximative maximum-likelihood detectors that enable the algorithms to optimally exploit the proposed echo train model. Extensive numerical evaluations based on both simulated and measured NQR data indicate that the proposed detectors offer a significant improvement as compared to current state-of-the-art detectors View full abstract»

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  • Electromagnetic Target Detection in Uncertain Media: Time-Reversal and Minimum-Variance Algorithms

    Publication Year: 2007 , Page(s): 934 - 944
    Cited by:  Papers (34)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (932 KB) |  | HTML iconHTML  

    An experimental study is performed on imaging targets that are situated in a highly scattering environment, employing electromagnetic time-reversal methods. A particular focus is placed on performance when the electrical properties of the background environment (medium) are uncertain. It is assumed that the (unknown) medium characteristic of the scattered fields represents one sample from an underlying random process, with this random process representing our uncertainty in the media properties associated with the scattering measurement. While the specific Green's function associated with the scattered fields is unknown, we assume access to an ensemble of Green's functions sampled from the aforementioned distribution. This ensemble of Green's functions may be used in several ways to mitigate uncertainty in the true Green's function. Specifically, when performing time-reversal imaging, we consider a Green's function as a representative of the average of the ensemble, as well as Green's functions based on a principal components analysis of the ensemble. We also develop a wideband minimum-variance beamformer with environment perturbation constraints, in which the unknown Green's function is constrained to reside in a subspace spanned by the Green's function ensemble. These algorithms are examined using electromagnetic scattering data measured in a canonical set of laboratory experiments. The qualitative performance of the different techniques is presented in the form of images, with quantitative results presented in the form of receiver operating characteristic performance View full abstract»

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  • Experimental Demonstration of the Corbella Equation for Aperture Synthesis Microwave Radiometry

    Publication Year: 2007 , Page(s): 945 - 957
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (915 KB) |  | HTML iconHTML  

    The fundamental equation of aperture synthesis microwave radiometry has been recently revised into a new so-called Corbella equation to include antenna coupling effects and the interferometry formulation into a single equation. This equation has been experimentally demonstrated for the first time with a full prototype of the Microwave Imaging Radiometer with Aperture Synthesis. Several tests have been carried out to demonstrate the new visibility equation with different targets. The test results show better agreement with simulations based on the Corbella equation than the old fundamental equation. An initial analysis of the influence of the antenna pattern characterization errors shows the importance of these errors in the final results according to the new formulation View full abstract»

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  • Diurnal Variation of the AMSU-A Brightness Temperatures Over the Amazon Rainforest

    Publication Year: 2007 , Page(s): 958 - 969
    Cited by:  Papers (10)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (736 KB) |  | HTML iconHTML  

    Brightness temperatures over the Amazon rainforest are obtained from the Advanced Microwave Sounding Units (AMSU-A and AMSU-B) instruments onboard three NOAA satellites (NOAA-15, -16, and -17, respectively) for the months of July, August, and October, 2002. The three AMSU-A instruments provided six daily measurements, separated by 2.5-5.5 h of the diurnal time intervals, over the Amazon rainforest region, and these measurements offer a unique opportunity for investigation of the diurnal variation of the brightness temperatures over the Amazon rainforests. The angular distributions of brightness temperatures over the Amazon rainforest are very stable and can be simulated with a radiative transfer model, which consists of an atmospheric radiative component and a rainforest-canopy model that treats the rainforest as a uniform layer with an effective canopy temperature. The simulated results agree well with the observations. The diurnal variation of brightness temperatures over the Amazon rainforest is simulated with a Fourier-series model. It shows that a second order of Fourier series can reproduce the observed pattern of diurnal variation of the brightness temperatures at zenith angles of 0deg, 28.7deg, and 58.1deg, respectively. In a practical application, the coefficients of Fourier-series expansion can be used to generate the brightness temperatures as a function of diurnal hours. These results can be applied to postlaunch calibration of satellite-borne microwave radiometer with different equator crossing time. In addition, the results presented in this paper indicate that the Amazon rainforest can be used as a hot calibration reference target. The availability of a land calibration target is important for calibration and validation of spaceborne microwave radiometers View full abstract»

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  • GEOSAT Follow-On Water Vapor Radiometer: Performance With a Shared Active/Passive Antenna

    Publication Year: 2007 , Page(s): 970 - 977
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (612 KB) |  | HTML iconHTML  

    The GEOSAT Follow-On mission marks the first time that an Earth-orbiting microwave radiometer and radar have shared the same antenna. The antenna design must simultaneously accommodate the radar's high-gain requirement and the high beam efficiency needed by the radiometer. Guaranteeing sufficiently high isolation between the radar transmitter and the radiometer receivers is also a critical part of the antenna design. The radiometer receiver includes a transmitter blanking circuit to further mitigate possible radar interference. Preflight and on-orbit tests of the antenna and radiometer receivers and an evaluation of end-to-end radiometric accuracy are presented. Together, they demonstrate that the shared-antenna approach can be implemented without compromising radiometric performance View full abstract»

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  • Snow-Covered Area Estimation Using Satellite Radar Wide-Swath Images

    Publication Year: 2007 , Page(s): 978 - 989
    Cited by:  Papers (12)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1257 KB) |  | HTML iconHTML  

    Satellite radar-based remote sensing of snow cover during the snow-melt season has been widely studied for different geographical regions, such as mountainous, open, and forested areas. However, a single method has not been found to function well on all regions. The investigations on boreal forest zone have allowed the Helsinki University of Technology (TKK) to develop a snow-covered area (SCA) method that is feasible using spatially limited European Remote Sensing-1/2 Satellite data. This paper investigates the use of wide-swath radar data for boreal forest SCA estimation for the first time. The TKK SCA method is adapted here for HH-polarization Radarsat data. The predominant aspect originated by the use of wide-swath synthetic aperture radar (SAR) data is the large variation in the radar incidence angle. The effect of incidence angle variation on SCA estimation is characterized in this paper. The foundation for operational implementation of the TKK SCA method is also established by an error propagation analysis presented in this paper. The error propagation analysis is compared with accuracy characteristics acquired between SAR and optical SCA evaluation. The performance of forest compensation, which is a key element of the TKK method, was analyzed for the wide-swath radar data. Furthermore, the correlation between the topography and the SCA estimation accuracy was examined in this paper. This paper lays the foundation for operational SCA estimation on boreal forest zone using wide-swath SAR data View full abstract»

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  • Modeling Active Microwave Remote Sensing of Snow Using Dense Media Radiative Transfer (DMRT) Theory With Multiple-Scattering Effects

    Publication Year: 2007 , Page(s): 990 - 1004
    Cited by:  Papers (34)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1408 KB) |  | HTML iconHTML  

    Dense media radiative transfer (DMRT) theory is used to study the multiple-scattering effects in active microwave remote sensing. Simplified DMRT phase matrices are obtained in the 1-2 frame. The simplified expressions facilitate solutions of the DMRT equations and comparisons with other phase matrices. First-order, second-order, and full multiple-scattering solutions of the DMRT equations are obtained. To solve the DMRT equation, we decompose the diffuse intensities into Fourier series in the azimuthal direction. Each harmonic is solved by the eigen-quadrature approach. The model is applied to the active microwave remote sensing of terrestrial snow. Full multiple-scattering effects are important as the optical thickness for snow at frequencies above 10 GHz often exceed unity. The results are illustrated as a function of frequency, incidence angle, and snow depth. The results show that cross polarization for the case of densely packed spheres can be significant and can be merely 6 to 8 dB below copolarization. The magnitudes of the cross polarization are consistent with the experimental observations. The results show that the active 13.5-GHz backscattering coefficients still have significant sensitivity to snow thickness even for snow thickness exceeding 1 m View full abstract»

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  • Observations of Snow Water Equivalent Change on Landfast First-Year Sea Ice in Winter Using Synthetic Aperture Radar Data

    Publication Year: 2007 , Page(s): 1005 - 1015
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1433 KB) |  | HTML iconHTML  

    In this paper, we examine the utility of synthetic aperture radar (SAR) backscatter data to detect a change in snow water equivalent (SWE) over landfast first-year sea ice during winter at relatively cold temperatures. We begin by reviewing the theoretical framework for linking microwave scattering from SAR to the thermodynamic and electrical properties of first-year sea ice. Previous research has demonstrated that for a given ice thickness and air-temperature change, a thick snow cover will result in a smaller change in the snow-ice interface temperature than will a thin snow cover. This small change in the interface temperature will result in a relatively small change in the brine volume at the interface and the resulting complex permittivity, thereby producing a relatively small change in scattering. A thin snow cover produces the opposite effect-a greater change in interface temperature, brine volume, permittivity, and scattering. This work is extended here to illustrate a variation of this effect over landfast first-year sea ice using in situ measurements of physical snow properties and RADARSAT-1 SAR imagery acquired during the winter of 1999 in the central Canadian Archipelago at cold (~-26degC) and moderately cold (~-14degC) snow-sea-ice interface temperatures. We utilize in situ data from five validation sites to demonstrate how the change in microwave scattering covaries and is inversely proportional with the change in the magnitude of SWE. These changes are shown to be detectable over both short (2 days) and longer (45 days) time durations View full abstract»

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  • Impact of Multiresolution Active and Passive Microwave Measurements on Soil Moisture Estimation Using the Ensemble Kalman Smoother

    Publication Year: 2007 , Page(s): 1016 - 1028
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (740 KB) |  | HTML iconHTML  

    An observing system simulation experiment is developed to test tradeoffs in resolution and accuracy for soil moisture estimation using active and passive L-band remote sensing. Concepts for combined radar and radiometer missions include designs that will provide multiresolution measurements. In this paper, the scientific impacts of instrument performance are analyzed to determine the measurement requirements for the mission concept. The ensemble Kalman smoother (EnKS) is used to merge these multiresolution observations with modeled soil moisture from a land surface model to estimate surface and subsurface soil moisture at 6-km resolution. The model used for assimilation is different from that used to generate "truth." Consequently, this experiment simulates how data assimilation performs in real applications when the model is not a perfect representation of reality. The EnKS is an extension of the ensemble Kalman filter (EnKF) in which observations are used to update states at previous times. Previous work demonstrated that it provides a computationally inexpensive means to improve the results from the EnKF, and that the limited memory in soil moisture can be exploited by employing it as a fixed lag smoother. Here, it is shown that the EnKS can be used in large problems with spatially distributed state vectors and spatially distributed multiresolution observations. The EnKS-based data assimilation framework is used to study the synergy between passive and active observations that have different resolutions and measurement error distributions. The extent to which the design parameters of the EnKS vary depending on the combination of observations assimilated is investigated View full abstract»

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Aims & Scope

 

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (TGRS) is a monthly publication that focuses on the theory, concepts, and techniques of science and engineering as applied to sensing the land, oceans, atmosphere, and space; and the processing, interpretation, and dissemination of this information.

 

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Meet Our Editors

Editor-in-Chief
Antonio J. Plaza
University of Extremadura