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Special Issue "Microwave Remote Sensing"

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (30 November 2009)

Special Issue Editors

Guest Editor
Prof. Dr. Wolfgang Wagner (Website)

Research Group Remote Sensing, Department of Geodesy and Geoinformation (GEO), Vienna University of Technology (TU Wien), Gusshausstrasse 27-29, 1040 Vienna, Austria
Fax: +43 1 58801 12299
Interests: remote sensing; geophysical parameter retrieval; airborne laser scanning; full-waveform lidar; radar remote sensing; soil moisture
Guest Editor
Dr. Iain H. Woodhouse (Website)

Edinburgh Earth Observatory, School of GeoSciences, Geography Building, Drummond, Edinburgh EH8 9XP, UK
Fax: +44 (0) 131 650 2524
Interests: radar remote sensing; polar decomposition methods for visualising SAR data; novel visualisation techniques for the analysis of multichannel remote sensing data; DEM generation and regional scale geomorphology; synergistic remote sensing of vegetation; macroecology and telemacroscopics

Special Issue Information

Dear Colleagues,

Microwave remote sensing is a very dynamic field driven by advances in space technique and sensor design, improvements in processing techniques and retrieval algorithms, and the increasing demand of data in geosciences. The big challenges for the future are to develop global climate datasets based on microwave observations and to establish new operational monitoring services in support to various applications such as weather forecasting, oceanography or hydrology. This special issue seeks contributions from all research and development areas in microwave remote sensing – from the design of future sensors, scattering and emission theory, geophysical parameter retrieval, to the development of operational microwave products and data assimilation capabilities. Particularly welcome are contributions dealing with the generation of essential climate variables (soil moisture, biomass, snow, precipitation, ocean salinity, sea ice, etc.) and studies preparing for the launch of new spaceborne microwave sensors.

Prof. Dr. Wolfgang Wagner
Dr. Iain H. Woodhouse
Guest Editors

Keywords

  • active and passive microwave sensors
  • scattering and emission theory
  • geophysical parameter retrieval and uncertainty analysis
  • SAR processing
  • interferometry
  • polarimetry
  • image and time series analysis
  • operational microwave products
  • applications and data assimilation

Published Papers (15 papers)

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Research

Jump to: Review

Open AccessArticle Comparing Accuracy of Airborne Laser Scanning and TerraSAR-X Radar Images in the Estimation of Plot-Level Forest Variables
Remote Sens. 2010, 2(2), 432-445; doi:10.3390/rs2020432
Received: 7 December 2009 / Revised: 13 January 2010 / Accepted: 22 January 2010 / Published: 28 January 2010
Cited by 18 | PDF Full-text (1281 KB) | HTML Full-text | XML Full-text
Abstract
In this study we compared the accuracy of low-pulse airborne laser scanning (ALS) data, multi-temporal high-resolution noninterferometric TerraSAR-X radar data and a combined feature set derived from these data in the estimation of forest variables at plot level. The TerraSAR-X data set [...] Read more.
In this study we compared the accuracy of low-pulse airborne laser scanning (ALS) data, multi-temporal high-resolution noninterferometric TerraSAR-X radar data and a combined feature set derived from these data in the estimation of forest variables at plot level. The TerraSAR-X data set consisted of seven dual-polarized (HH/HV or VH/VV) Stripmap mode images from all seasons of the year. We were especially interested in distinguishing between the tree species. The dependent variables estimated included mean volume, basal area, mean height, mean diameter and tree species-specific mean volumes. Selection of best possible feature set was based on a genetic algorithm (GA). The nonparametric k-nearest neighbour (k-NN) algorithm was applied to the estimation. The research material consisted of 124 circular plots measured at tree level and located in the vicinity of Espoo, Finland. There are large variations in the elevation and forest structure in the study area, making it demanding for image interpretation. The best feature set contained 12 features, nine of them originating from the ALS data and three from the TerraSAR-X data. The relative RMSEs for the best performing feature set were 34.7% (mean volume), 28.1% (basal area), 14.3% (mean height), 21.4% (mean diameter), 99.9% (mean volume of Scots pine), 61.6% (mean volume of Norway spruce) and 91.6% (mean volume of deciduous tree species). The combined feature set outperformed an ALS-based feature set marginally; in fact, the latter was better in the case of species-specific volumes. Features from TerraSAR-X alone performed poorly. However, due to favorable temporal resolution, satellite-borne radar imaging is a promising data source for updating large-area forest inventories based on low-pulse ALS. Full article
(This article belongs to the Special Issue Microwave Remote Sensing)
Open AccessArticle Analysis of a Least-Squares Soil Moisture Retrieval Algorithm from L-band Passive Observations
Remote Sens. 2010, 2(1), 352-374; doi:10.3390/rs2010352
Received: 25 November 2009 / Revised: 23 December 2009 / Accepted: 11 January 2010 / Published: 20 January 2010
Cited by 2 | PDF Full-text (306 KB) | HTML Full-text | XML Full-text
Abstract
The Soil Moisture and Ocean Salinity (SMOS) mission of the European Space Agency (ESA), launched on November 2009, is an unprecedented initiative to globally monitor surface soil moisture using a novel 2-D L-band interferometric radiometer concept. Airborne campaigns and ground-based field experiments [...] Read more.
The Soil Moisture and Ocean Salinity (SMOS) mission of the European Space Agency (ESA), launched on November 2009, is an unprecedented initiative to globally monitor surface soil moisture using a novel 2-D L-band interferometric radiometer concept. Airborne campaigns and ground-based field experiments have proven that radiometers operating at L-band are highly sensitive to soil moisture, due to the large contrast between the dielectric constant of soil minerals and water. Still, soil moisture inversion from passive microwave observations is complex, since the microwave emission from soils depends strongly on its moisture content but also on other surface characteristics such as soil type, soil roughness, surface temperature and vegetation cover, and their contributions must be carefully de-coupled in the retrieval process. In the present study, different soil moisture retrieval configurations are examined, depending on whether prior information is used in the inversion process or not. Retrievals are formulated in terms of vertical (Tvv) and horizontal (Thh) polarizations separately and using the first Stokes parameter (TI ), over six main surface conditions combining dry, moist and wet soils with bare and vegetation-covered surfaces. A sensitivity analysis illustrates the influence that the geophysical variables dominating the Earth’s emission at L-band have on the precision of the retrievals, for each configuration. It shows that, if adequate constraints on the ancillary data are added, the algorithm should converge to more accurate estimations. SMOS-like brightness temperatures are also generated by the SMOS End-to-end Performance Simulator (SEPS) to assess the retrieval errors produced by the different cost function configurations. Better soil moisture retrievals are obtained when the inversion is constrained with prior information, in line with the sensitivity study, and more robust estimates are obtained using TI than using Tvv and Thh. This paper analyzes key issues to devise an optimal soil moisture inversion algorithm for SMOS and results can be readily transferred to the upcoming SMOS data to produce the much needed global maps of the Earth’s surface soil moisture. Full article
(This article belongs to the Special Issue Microwave Remote Sensing)
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Open AccessArticle Use of Soil Moisture Variability in Artificial Neural Network Retrieval of Soil Moisture
Remote Sens. 2010, 2(1), 166-190; doi:10.3390/rs2010166
Received: 10 October 2009 / Revised: 9 December 2009 / Accepted: 23 December 2009 / Published: 31 December 2009
Cited by 7 | PDF Full-text (538 KB) | HTML Full-text | XML Full-text
Abstract
Passive microwave remote sensing is one of the most promising techniques for soil moisture retrieval. However, the inversion of soil moisture from brightness temperature observations is not straightforward, as it is influenced by numerous factors such as surface roughness, vegetation cover, and [...] Read more.
Passive microwave remote sensing is one of the most promising techniques for soil moisture retrieval. However, the inversion of soil moisture from brightness temperature observations is not straightforward, as it is influenced by numerous factors such as surface roughness, vegetation cover, and soil texture. Moreover, the relationship between brightness temperature, soil moisture and the factors mentioned above is highly non-linear and ill-posed. Consequently, Artificial Neural Networks (ANNs) have been used to retrieve soil moisture from microwave data, but with limited success when dealing with data different to that from the training period. In this study, an ANN is tested for its ability to predict soil moisture at 1 km resolution on different dates following training at the same site for a specific date. A novel approach that utilizes information on the variability of soil moisture, in terms of its mean and standard deviation for a (sub) region of spatial dimension up to 40 km, is used to improve the current retrieval accuracy of the ANN method. A comparison between the ANN with and without the use of the variability information showed that this enhancement enables the ANN to achieve an average Root Mean Square Error (RMSE) of around 5.1% v/v when using the variability information, as compared to around 7.5% v/v without it. The accuracy of the soil moisture retrieval was further improved by the division of the target site into smaller regions down to 4 km in size, with the spatial variability of soil moisture calculated from within the smaller region used in the ANN. With the combination of an ANN architecture of a single hidden layer of 20 neurons and the dual-polarized brightness temperatures as input, the proposed use of variability and sub-region methodology achieves an average retrieval accuracy of 3.7% v/v. Although this accuracy is not the lowest as comparing to the research in this field, the main contribution is the ability of ANN in solving the problem of predicting “out-of-range” soil moisture values. However, the applicability of this method is highly dependent on the accuracy of the mean and standard deviation values within the sub-region, potentially limiting its routine application. Full article
(This article belongs to the Special Issue Microwave Remote Sensing)
Open AccessArticle Integrating Vegetation Indices Models and Phenological Classification with Composite SAR and Optical Data for Cereal Yield Estimation in Finland (Part I)
Remote Sens. 2010, 2(1), 76-114; doi:10.3390/rs2010076
Received: 14 October 2009 / Revised: 11 November 2009 / Accepted: 11 December 2009 / Published: 29 December 2009
Cited by 9 | PDF Full-text (1610 KB) | HTML Full-text | XML Full-text
Abstract
During 1996–2006 the Ministry of Agriculture and Forestry in Finland, MTT Agrifood Research Finland and the Finnish Geodetic Institute carried out a joint remote sensing satellite research project. It evaluated the applicability of composite multispectral SAR and optical satellite data for cereal [...] Read more.
During 1996–2006 the Ministry of Agriculture and Forestry in Finland, MTT Agrifood Research Finland and the Finnish Geodetic Institute carried out a joint remote sensing satellite research project. It evaluated the applicability of composite multispectral SAR and optical satellite data for cereal yield estimations in the annual crop inventory program. Three Vegetation Indices models (VGI, Infrared polynomial, NDVI and Composite multispetral SAR and NDVI) were validated to estimate cereal yield levels using solely optical and SAR satellite data (Composite Minimum Dataset). The average R2 for cereal yield (yb) was 0.627. The averaged composite SAR modeled grain yield level was 3,750 kg/ha (RMSE = 10.3%, 387 kg/ha) for high latitude spring cereals (4,018 kg/ha for spring wheat, 4,037 kg/ha for barley and 3,151 kg/ha for oats). Full article
(This article belongs to the Special Issue Microwave Remote Sensing)
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Open AccessArticle Application of Microwave Remote Sensing to Dynamic Testing of Stay-Cables
Remote Sens. 2010, 2(1), 36-51; doi:10.3390/rs2010036
Received: 20 October 2009 / Revised: 14 December 2009 / Accepted: 23 December 2009 / Published: 28 December 2009
Cited by 12 | PDF Full-text (1121 KB) | HTML Full-text | XML Full-text
Abstract
Recent advances in radar techniques and systems have favoured the development of microwave interferometers, suitable for the non-contact vibration monitoring of large structures. The paper addresses the application of microwave remote sensing to the measurement of the vibration response in the stay-cables [...] Read more.
Recent advances in radar techniques and systems have favoured the development of microwave interferometers, suitable for the non-contact vibration monitoring of large structures. The paper addresses the application of microwave remote sensing to the measurement of the vibration response in the stay-cables of cable-stayed bridges. The reliability and accuracy of the proposed technique were investigated by comparing the natural frequencies (and the cable tensions predicted from natural frequencies) identified from radar data and the corresponding quantities obtained using more conventional techniques. The investigation, carried out on the cables of two different cable-stayed bridges, clearly highlights: (a) the accuracy of the results provided by the microwave remote sensing; (b) the simplicity of use of the radar technique (especially when compared with conventional approaches) and its effectiveness to simultaneously measuring the dynamic response of all the stay-cables of an array. Full article
(This article belongs to the Special Issue Microwave Remote Sensing)
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Open AccessArticle “Group Inversion Approach” for Detection of Soil Moisture Temporal-Invariant Locations
Remote Sens. 2009, 1(4), 1338-1352; doi:10.3390/rs1041338
Received: 26 October 2009 / Revised: 9 December 2009 / Accepted: 14 December 2009 / Published: 21 December 2009
PDF Full-text (386 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents an approach denominated Group Inversion Approach (GIA) which aims at detecting soil moisture temporal invariants, i.e., the stable temporal soil moisture locations, by using mainly remotely sensed data. The soil moisture temporal invariants are those locations [...] Read more.
This paper presents an approach denominated Group Inversion Approach (GIA) which aims at detecting soil moisture temporal invariants, i.e., the stable temporal soil moisture locations, by using mainly remotely sensed data. The soil moisture temporal invariants are those locations where independently of the absolute value changes, the relative spatial distribution of soil moisture remains almost constant. In this procedure, the soil moisture values estimated from different inversion approaches and sensor configurations are compared among themselves and with the ground data. The procedure has been tested in a watershed of around 7,000 km2 with data collected during the SMEX’02 experiment in Iowa (USA). The results indicate that fields with inversion errors lower than five times the soil moisture variability detected with ground measurements represent well the mean watershed soil moisture values. The GIA technique has been also found in good agreement with the classical technique used to detect the stable soil moisture features, based exclusively on ground measurements. Full article
(This article belongs to the Special Issue Microwave Remote Sensing)
Open AccessArticle Estimating Flow Resistance of Wetlands Using SAR Images and Interaction Models
Remote Sens. 2009, 1(4), 992-1008; doi:10.3390/rs1040992
Received: 23 September 2009 / Revised: 4 November 2009 / Accepted: 10 November 2009 / Published: 13 November 2009
Cited by 6 | PDF Full-text (1231 KB) | HTML Full-text | XML Full-text
Abstract
The inability to monitor wetland drag coefficients at a regional scale is rooted in the difficulty to determine vegetation structure from remote sensing data. Based on the fact that the backscattering coefficient is sensitive to marsh vegetation structure, this paper presents a [...] Read more.
The inability to monitor wetland drag coefficients at a regional scale is rooted in the difficulty to determine vegetation structure from remote sensing data. Based on the fact that the backscattering coefficient is sensitive to marsh vegetation structure, this paper presents a methodology to estimate the drag coefficient from a combination of SAR images, interaction models and ancillary data. We use as test case a severe fire event occurred in the Paraná River Delta (Argentina) at the beginning of 2008, when 10% of the herbaceous vegetation was burned up. A map of the reduction of the wetland drag coefficient is presented. Full article
(This article belongs to the Special Issue Microwave Remote Sensing)
Open AccessArticle Deriving Ocean Surface Drift Using Multiple SAR Sensors
Remote Sens. 2009, 1(3), 266-277; doi:10.3390/rs1030266
Received: 3 June 2009 / Revised: 9 July 2009 / Accepted: 9 July 2009 / Published: 13 July 2009
Cited by 8 | PDF Full-text (693 KB) | HTML Full-text | XML Full-text
Abstract
Tracking and monitoring ocean features which have short coherent time periods from sequential satellite images requires that the images have both very high spatial resolutions and short temporal sampling intervals (i.e., repeated cycles). Satellite images from a single sensor in a polar-orbiting [...] Read more.
Tracking and monitoring ocean features which have short coherent time periods from sequential satellite images requires that the images have both very high spatial resolutions and short temporal sampling intervals (i.e., repeated cycles). Satellite images from a single sensor in a polar-orbiting satellite usually cannot meet these requirements since high spatial resolution of the sensor may result in relatively long temporal sampling interval and vice versa, such as the case of Synthetic Aperture Radar (SAR). This paper presents a new multi-sensor approach to overcome the long temporal sampling interval associated with a single SAR sensor while taking advantage of high spatial resolution of SAR images for the application of ocean feature tracking.Currently, there are two SAR sensors on different satellites, the European Remote Sensing Satellite-2 (ERS-2) and the ENVIronment SATellite (ENVISAT), having acquisition time offset around 28 minutes with almost exactly the same path.That is, ERS-2 is following ENVISAT with a 28-minutes delay, which is a good time-scale for ocean mesoscale feature tracking.A pair of SAR images from ERS-2 and ENVISAT collected on April 27, 2005 has been chosen to track ocean surface features by using wavelet analysis. As demonstrated in the case studies, this technique is robust and capable to derive ocean surface drift near an oil slick and around a big eddy in the South China Sea (SCS). Full article
(This article belongs to the Special Issue Microwave Remote Sensing)
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Open AccessArticle Similarity Measures of Remotely Sensed Multi-Sensor Images for Change Detection Applications
Remote Sens. 2009, 1(3), 122-143; doi:10.3390/rs1030122
Received: 6 May 2009 / Revised: 11 June 2009 / Accepted: 23 June 2009 / Published: 3 July 2009
Cited by 26 | PDF Full-text (725 KB) | HTML Full-text | XML Full-text
Abstract
Change detection of remotely sensed images is a particularly challenging task when the time series data come from different sensors. Indeed, many change indicators are based on radiometry measurements, used to calculate differences or ratios, that are no longer meaningful when the [...] Read more.
Change detection of remotely sensed images is a particularly challenging task when the time series data come from different sensors. Indeed, many change indicators are based on radiometry measurements, used to calculate differences or ratios, that are no longer meaningful when the data have been acquired by different instruments. For this reason, it is interesting to study those indicators that do not rely completely on radiometric values. In this work a new approach is proposed based on similarity measures. A series of such measures is employed for automatic change detection of optical and SAR images and a comparison of their performance is carried out to establish the limits of their applicability and their sensitivity to the occurred changes. Initial results are promising and suggest similarity measures as possiblechange detectors in multi-sensor configurations. Full article
(This article belongs to the Special Issue Microwave Remote Sensing)
Open AccessArticle Polarimetric Emission of Rain Events: Simulation and Experimental Results at X-Band
Remote Sens. 2009, 1(2), 107-121; doi:10.3390/rs1020107
Received: 4 May 2009 / Revised: 5 June 2009 / Accepted: 9 June 2009 / Published: 12 June 2009
Cited by 5 | PDF Full-text (452 KB) | HTML Full-text | XML Full-text
Abstract
Accurate models are used today for infrared and microwave satellite radiance simulations of the first two Stokes elements in the physical retrieval, data assimilation etc. of surface and atmospheric parameters. Although in the past a number of theoretical and experimental works have [...] Read more.
Accurate models are used today for infrared and microwave satellite radiance simulations of the first two Stokes elements in the physical retrieval, data assimilation etc. of surface and atmospheric parameters. Although in the past a number of theoretical and experimental works have studied the polarimetric emission of some natural surfaces, specially the sea surface roughened by the wind (Windsat mission), very limited studies have been conducted on the polarimetric emission of rain cells or other natural surfaces. In this work, the polarimetric emission (four Stokes elements) of a rain cell is computed using the polarimetric radiative transfer equation assuming that raindrops are described by Pruppacher-Pitter shapes and that their size distribution follows the Laws-Parsons law. The Boundary Element Method (BEM) is used to compute the exact bistatic scattering coefficients for each raindrop shape and different canting angles. Numerical results are compared to the Rayleigh or Mie scattering coefficients, and to Oguchi’s ones, showing that above 1-2 mm raindrop size the exact formulation is required to model properly the scattering. Simulation results using BEM are then compared to the experimental data gathered with a X-band polarimetric radiometer. It is found that the depolarization of the radiation caused by the scattering of non-spherical raindrops induces a non-zero third Stokes parameter, and the differential phase of the scattering coefficients induces a non-zero fourth Stokes parameter. Full article
(This article belongs to the Special Issue Microwave Remote Sensing)
Open AccessArticle Non-parametric Methods for Soil Moisture Retrieval from Satellite Remote Sensing Data
Remote Sens. 2009, 1(1), 3-21; doi:10.3390/rs1010003
Received: 20 February 2009 / Revised: 24 March 2009 / Accepted: 25 March 2009 / Published: 27 March 2009
Cited by 10 | PDF Full-text (658 KB) | HTML Full-text | XML Full-text
Abstract
Satellite remote sensing observations have the potential for efficient and reliable mapping of spatial soil moisture distributions. However, soil moisture retrievals from active microwave remote sensing data are typically complex due to inherent difficulty in characterizing interactions among land surface parameters that [...] Read more.
Satellite remote sensing observations have the potential for efficient and reliable mapping of spatial soil moisture distributions. However, soil moisture retrievals from active microwave remote sensing data are typically complex due to inherent difficulty in characterizing interactions among land surface parameters that contribute to the retrieval process. Therefore, adequate physical mathematical descriptions of microwave backscatter interaction with parameters such as land cover, vegetation density, and soil characteristics are not readily available. In such condition, non-parametric models could be used as possible alternative for better understanding the impact of variables in the retrieval process and relating it in the absence of exact formulation. In this study, non-parametric methods such as neural networks, fuzzy logic are used to retrieve soil moisture from active microwave remote sensing data. The inclusion of soil characteristics and Normalized Difference Vegetation Index (NDVI) derived from infrared and visible measurement, have significantly improved soil moisture retrievals and reduced root mean square error (RMSE) by around 30% in the retrievals. Soil moisture derived from these methods was compared with ESTAR soil moisture (RMSE ~4.0%) and field soil moisture measurements (RMSE ~6.5%). Additionally, the study showed that soil moisture retrievals from highly vegetated areas are less accurate than bare soil areas. Full article
(This article belongs to the Special Issue Microwave Remote Sensing)
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Review

Jump to: Research

Open AccessReview Ten Years of SeaWinds on QuikSCAT for Snow Applications
Remote Sens. 2010, 2(4), 1142-1156; doi:10.3390/rs2041142
Received: 2 March 2010 / Revised: 24 March 2010 / Accepted: 30 March 2010 / Published: 16 April 2010
Cited by 18 | PDF Full-text (771 KB) | HTML Full-text | XML Full-text
Abstract
The scatterometer SeaWinds on QuikSCAT provided regular measurements at Ku-band from 1999 to 2009. Although it was designed for ocean applications, it has been frequently used for the assessment of seasonal snowmelt patterns aside from other terrestrial applications such as ice cap [...] Read more.
The scatterometer SeaWinds on QuikSCAT provided regular measurements at Ku-band from 1999 to 2009. Although it was designed for ocean applications, it has been frequently used for the assessment of seasonal snowmelt patterns aside from other terrestrial applications such as ice cap monitoring, phenology and urban mapping. This paper discusses general data characteristics of SeaWinds and reviews relevant change detection algorithms. Depending on the complexity of the method, parameters such as long-term noise and multiple event analyses were incorporated. Temporal averaging is a commonly accepted preprocessing step with consideration of diurnal, multi-day or seasonal averages. Full article
(This article belongs to the Special Issue Microwave Remote Sensing)
Open AccessReview Near-Space Microwave Radar Remote Sensing: Potentials and Challenge Analysis
Remote Sens. 2010, 2(3), 717-739; doi:10.3390/rs2030717
Received: 16 November 2009 / Revised: 11 January 2010 / Accepted: 26 January 2010 / Published: 9 March 2010
Cited by 15 | PDF Full-text (465 KB) | HTML Full-text | XML Full-text
Abstract
Near-space, defined as the region between 20 km and 100 km, offers many new capabilities that are not accessible to low earth orbit (LEO) satellites and airplanes, because it is above storm and not constrained by either the orbital mechanics of satellites [...] Read more.
Near-space, defined as the region between 20 km and 100 km, offers many new capabilities that are not accessible to low earth orbit (LEO) satellites and airplanes, because it is above storm and not constrained by either the orbital mechanics of satellites or the high fuel consumption of airplanes. By placing radar transmitter/receiver in near-space platforms, many functions that are currently performed with satellites or airplanes could be performed in a cheaper way. Inspired by these advantages, this paper introduces several near-space vehicle-based radar configurations, such as near-space passive bistatic radar and high-resolution wide-swath (HRWS) synthetic aperture radar (SAR). Their potential applications, technical challenges and possible solutions are investigated. It is shown that near-space is a satisfactory solution to some specific remote sensing applications. Firstly, near-space passive bistatic radar using opportunistic illuminators offers a solution to persistent regional remote sensing, which is particularly interest for protecting homeland security or monitoring regional environment. Secondly, near-space provides an optimal solution to relative HRWS SAR imaging. Moreover, as motion compensation is a common technical challenge for the described radars, an active transponder-based motion compensation is also described. Full article
(This article belongs to the Special Issue Microwave Remote Sensing)
Open AccessReview Antarctic Ice Sheet and Radar Altimetry: A Review
Remote Sens. 2009, 1(4), 1212-1239; doi:10.3390/rs1041212
Received: 12 October 2009 / Revised: 13 November 2009 / Accepted: 28 November 2009 / Published: 7 December 2009
Cited by 26 | PDF Full-text (2645 KB) | HTML Full-text | XML Full-text
Abstract
Altimetry is probably one of the most powerful tools for ice sheet observation. Our vision of the Antarctic ice sheet has been deeply transformed since the launch of the ERS1 satellite in 1991. With the launch of ERS2 and Envisat, the series [...] Read more.
Altimetry is probably one of the most powerful tools for ice sheet observation. Our vision of the Antarctic ice sheet has been deeply transformed since the launch of the ERS1 satellite in 1991. With the launch of ERS2 and Envisat, the series of altimetric observations now provides 19 years of continuous and homogeneous observations that allow monitoring of the shape and volume of ice sheets. The topography deduced from altimetry is one of the relevant parameters revealing the processes acting on ice sheet. Moreover, altimeter also provides other parameters such as backscatter and waveform shape that give information on the surface roughness or snow pack characteristics. Full article
(This article belongs to the Special Issue Microwave Remote Sensing)
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Open AccessReview Ultrawideband Microwave Sensing and Imaging Using Time-Reversal Techniques: A Review
Remote Sens. 2009, 1(3), 466-495; doi:10.3390/rs1030466
Received: 27 May 2009 / Revised: 13 July 2009 / Accepted: 10 August 2009 / Published: 24 August 2009
Cited by 30 | PDF Full-text (1131 KB) | HTML Full-text | XML Full-text
Abstract
This paper provides an overview of some time-reversal (TR) techniques for remote sensing and imaging using ultrawideband (UWB) electromagnetic signals in the microwave and millimeter wave range. The TR techniques explore the TR invariance of the wave equation in lossless and stationary [...] Read more.
This paper provides an overview of some time-reversal (TR) techniques for remote sensing and imaging using ultrawideband (UWB) electromagnetic signals in the microwave and millimeter wave range. The TR techniques explore the TR invariance of the wave equation in lossless and stationary media. They provide superresolution and statistical stability, and are therefore quite useful for a number of remote sensing applications. We first discuss the TR concept through a prototypal TR experiment with a discrete scatterer embedded in continuous random media. We then discuss a series of TR-based imaging algorithms employing UWB signals: DORT, space-frequency (SF) imaging and TR-MUSIC. Finally, we consider a dispersion/loss compensation approach for TR applications in dispersive/lossy media, where TR invariance is broken. Full article
(This article belongs to the Special Issue Microwave Remote Sensing)
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