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Gerhard Krieger - One of the best experts on this subject based on the ideXlab platform.
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digital beam forming techniques for spaceborne reflector sar systems
Synthetic Aperture Radar (EUSAR) 2010 8th European Conference on, 2010Co-Authors: Sigurd Huber, Marwan Younis, Anton Patyuchenko, Gerhard KriegerAbstract:A new generation of space borne Radar Remote Sensing systems is currently being under investigation. The concept of reflector antennas in conjunction with feed arrays, which is well established on communication satellites, is exploited for Synthetic Aperture Radar (SAR) sensors. One of the main goals for future SAR systems is to replace parts of the analog radio frequency hardware, being a major cost driver, in the receive chain with digital components. These innovative systems allow a flexible data handling and the implementation of digital beam forming (DBF) concepts. The main advantage of multi channel systems is to overcome the inherent limitations of state-of-the-art SAR instruments and enable the production of wide swath high resolution imagery at the same time. In the frame of these developments DBF algorithms, aiming at a performance improvement, are analyzed and adapted to the requirements of reflector based SAR sensors. The DBF concepts presented in this paper are illustrated by means of numerical simulations.
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development of the tandem x calibration concept analysis of systematic errors
IEEE Transactions on Geoscience and Remote Sensing, 2010Co-Authors: Jaime Hueso Gonzalez, Gerhard Krieger, M Bachmann, Hauke FiedlerAbstract:The TanDEM-X mission, result of the partnership between the German Aerospace Center (DLR) and Astrium GmbH, opens a new era in spaceborne Radar Remote Sensing. The first bistatic satellite synthetic aperture Radar mission is formed by flying TanDEM-X and TerraSAR-X in a closely controlled helix formation. The primary mission goal is the derivation of a high-precision global digital elevation model (DEM) according to High-Resolution Terrain Information (HRTI) level 3 accuracy. The finite precision of the baseline knowledge and uncompensated Radar instrument drifts introduce errors that may compromise the height accuracy requirements. By means of a DEM calibration, which uses absolute height references, and the information provided by adjacent interferogram overlaps, these height errors can be minimized. This paper summarizes the exhaustive studies of the nature of the residual-error sources that have been carried out during the development of the DEM calibration concept. Models for these errors are set up and simulations of the resulting DEM height error for different scenarios provide the basis for the development of a successful DEM calibration strategy for the TanDEM-X mission.
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performance comparison of reflector and planar antenna based digital beam forming sar
International Journal of Antennas and Propagation, 2009Co-Authors: Marwan Younis, Sigurd Huber, Anton Patyuchenko, Federica Bordoni, Gerhard KriegerAbstract:The trend in the conception of future spaceborne Radar Remote Sensing is clearly toward the use of digital beamforming techniques. These systems will comprise multiple digital channels, where the analog-to-digital converter is moved closer to the antenna. This dispenses the need for analog beam steering and by this the used of transmit/receive modules for phase and amplitude control. Digital beam-forming will enable Synthetic Aperture Radar (SAR) which overcomes the coverage and resolution limitations applicable to state-of-the-art systems. On the other hand, new antenna architectures, such as reflectors, already implemented in communication satellites, are being considered for SAR applications. An open question is the benefit of combining digital beam-forming techniques with reflector antennas. The paper answers this question by comparing the system architecture and digital beam-forming requirements of a planar and a reflector antenna SAR. Further elaboration yields the resulting SAR performance of both systems. This paper considers multiple novel aspects of digital beam-forming SAR system design, which jointly flow into the presented system performance.
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multidimensional waveform encoding a new digital beamforming technique for synthetic aperture Radar Remote Sensing
IEEE Transactions on Geoscience and Remote Sensing, 2008Co-Authors: Gerhard Krieger, Nicolas Gebert, Alberto MoreiraAbstract:This paper introduces the innovative concept of multidimensional waveform encoding for spaceborne synthetic aperture Radar (SAR). The combination of this technique with digital beamforming on receive enables a new generation of SAR systems with improved performance and flexible imaging capabilities. Examples are high-resolution wide-swath Radar imaging with compact antennas, enhanced sensitivity for applications like alongtrack interferometry and moving object indication, and the implementation of hybrid SAR imaging modes that are well suited to satisfy hitherto incompatible user requirements. Implementation-specific issues are discussed and performance examples demonstrate the potential of the new technique for different Remote Sensing applications.
Kyle C Mcdonald - One of the best experts on this subject based on the ideXlab platform.
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Classification of Alaska Spring Thaw Characteristics Using Satellite L-Band Radar Remote Sensing
IEEE Transactions on Geoscience and Remote Sensing, 2015Co-Authors: Jinyang Du, John S Kimball, Marzieh Azarderakhsh, Scott R. Dunbar, Mahta Moghaddam, Kyle C McdonaldAbstract:Spatial and temporal variability in landscape freeze- thaw (FT) status at higher latitudes and elevations significantly impacts land surface water mobility and surface energy partitioning, with major consequences for regional climate, hydrological, ecological, and biogeochemical processes. With the development of new-generation spaceborne Remote Sensing instruments, future L-band missions, including the NASA Soil Moisture Active and Passive mission, will provide new operational retrievals of landscape FT state dynamics at moderate (~3 km) spatial resolution. We applied theoretical simulations of L-band Radar backscatter using first-order radiative transfer models with two and three-layer modeling schemes to develop a modified seasonal threshold algorithm (STA) and FT classification study over Alaska using 100-m-resolution satellite Phased Array L-band Synthetic Aperture Radar (PALSAR) observations. The backscatter threshold distinguishes between frozen and nonfrozen states, and it is used to classify the predominant frozen or thawed status of a grid cell. An Alaska FT map for April 2007 was generated from PALSAR (ScanSAR) observations and showed a regionally consistent but finer FT spatial pattern than an alternative surface air temperature-based classification derived from global reanalysis data. Validation of the STA-based FT classification against regional soil climate stations indicated approximately 80% and 75% spatial classification accuracy values in relation to respective station air temperature and soil temperature measurement-based FT estimates. An investigation of relative spatial scale effects on FT classification accuracy indicates that the relationship between grid cell size and classified frozen or thawed area follows a general logarithmic function.
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effect of salinity on the dielectric properties of geological materials implication for soil moisture detection by means of Radar Remote Sensing
IEEE Transactions on Geoscience and Remote Sensing, 2008Co-Authors: Y Lasne, Kyle C Mcdonald, T G Farr, P Paillou, A Freeman, G Ruffie, J M Malezieux, Bruce Chapman, Francois DemontouxAbstract:We consider the exploitation of dielectric properties of saline deposits for the detection and mapping of moisture in arid regions on both Earth and Mars. We present simulated and experimental study in order to assess the effect of salinity on the complex permittivity of geological materials and, therefore, on the Radar backscattering coefficient in the [1-7 GHz] frequency range. Laboratory measurements are performed on sand/sodium chloride aqueous mixtures using a vectorial network analyzer coupled to an open-ended coaxial dielectric probe. We aim at calibrating and validating semiempirical dielectric mixing models. In particular, we evaluated the dependence of the real and imaginary parts of complex permittivity on the microwave frequency, water content, and salinity. Our results confirm that if the real part is mainly affected by the moisture content, the imaginary part is more sensitive to salinity. In addition to the classic formulas of mixing models, the ionic-conductivity losses, which are due to mobile ions in the saline solution, are taken into account in order to better assess the effect of salinity on the dielectric properties of mixtures. Dielectric mixing models are then used as input parameters for the simulation of the Radar backscattering coefficients by means of an analytical model: the integral equation model. Simulation results show that salinity should have a significant impact on the Radar backscattering recorded in synthetic aperture Radar data in terms of the magnitude of the backscattering coefficient. Moreover, our results suggest that VV polarization provides a greater sensitivity to salinity than HH polarization.
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satellite Radar Remote Sensing of seasonal growing seasons for boreal and subalpine evergreen forests
Remote Sensing of Environment, 2004Co-Authors: John S Kimball, Kyle C Mcdonald, Steven W Running, Steve FrolkingAbstract:Abstract We evaluated whether satellite Radar Remote Sensing of landscape seasonal freeze–thaw cycles provides an effective measure of active growing season timing and duration for boreal and subalpine evergreen forests. Landscape daily Radar backscatter measurements from the SeaWinds scatterometer on-board the QuikSCAT satellite were evaluated across a regional network of North American coniferous forest sites for 2000 and 2001. Radar Remote Sensing measurements of the initiation and length of the growing season corresponded closely with both site measurements and ecosystem process model (BIOME-BGC) simulations of these parameters because of the sensitivity of the Ku-band scatterometer to snow cover freeze–thaw dynamics and associated linkages between growing season initiation and the timing of seasonal snowmelt. In contrast, Remote Sensing estimates of the timing of growing season termination were either weakly or not significantly associated with site measurements and model simulation results, due to the relative importance of light availability and other environmental controls on stand phenology in the fall. Regional patterns of estimated annual net primary production (NPP) and component photosynthetic and autotrophic respiration rates for the evergreen forest sites also corresponded favorably with Remote Sensing estimates of the seasonal timing of spring thaw and associated growing season length, indicating the importance of these parameters in determining spatial and temporal patterns of NPP and the potential utility of satellite Radar Remote Sensing for regional monitoring of the terrestrial biosphere.
Mahta Moghaddam - One of the best experts on this subject based on the ideXlab platform.
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advancing nasa s airmoss p band Radar root zone soil moisture retrieval algorithm via incorporation of richards equation
Remote Sensing, 2016Co-Authors: Morteza Sadeghi, Mahta Moghaddam, Alireza Tabatabaeenejad, Markus Tuller, Scott B JonesAbstract:P-band Radar Remote Sensing applied during the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) mission has shown great potential for estimation of root zone soil moisture. When retrieving the soil moisture profile (SMP) from P-band Radar observations, a mathematical function describing the vertical moisture distribution is required. Because only a limited number of observations are available, the number of free parameters of the mathematical model must not exceed the number of observed data. For this reason, an empirical quadratic function (second order polynomial) is currently applied in the AirMOSS inversion algorithm to retrieve the SMP. The three free parameters of the polynomial are retrieved for each AirMOSS pixel using three backscatter observations (i.e., one frequency at three polarizations of Horizontal-Horizontal, Vertical-Vertical and Horizontal-Vertical). In this paper, a more realistic, physically-based SMP model containing three free parameters is derived, based on a solution to Richards’ equation for unsaturated flow in soils. Evaluation of the new SMP model based on both numerical simulations and measured data revealed that it exhibits greater flexibility for fitting measured and simulated SMPs than the currently applied polynomial. It is also demonstrated that the new SMP model can be reduced to a second order polynomial at the expense of fitting accuracy.
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advancing the airmoss p band Radar root zone soil moisture retrieval algorithm via incorporation of richards equation
Sciprints, 2016Co-Authors: Morteza Sadeghi, Mahta Moghaddam, Alireza Tabatabaeenejad, Markus Tuller, Scott B JonesAbstract:P-band Radar Remote Sensing applied during the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) mission has shown great potential for estimation of root zone soil moisture. When retrieving the soil moisture profile (SMP) from P-band Radar, a mathematical function describing the vertical moisture distribution is required. Because only a limited number of observations are available, the number of free parameters of the mathematical model must not exceed the number of observed data. For example, a second order polynomial that contains 3 free parameters was presumed based on in-situ SMP data. The polynomial is currently parameterized based on 3 backscatter observations provided by AirMOSS (i.e. one frequency at three polarizations of HH, VV and HV). In this paper, a more realistic, physically-based SMP model containing 3 free parameters is derived based on a solution to Richards' equation for unsaturated flow in soils. Evaluation of the new SMP model based on both numerical simulations and measured data revealed that it exhibits greater flexibility for fitting measured and simulated SMPs than the currently applied polynomial. It is also demonstrated that the new SMP model can be reduced to a second order polynomial at the expense of fitting accuracy.
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Classification of Alaska Spring Thaw Characteristics Using Satellite L-Band Radar Remote Sensing
IEEE Transactions on Geoscience and Remote Sensing, 2015Co-Authors: Jinyang Du, John S Kimball, Marzieh Azarderakhsh, Scott R. Dunbar, Mahta Moghaddam, Kyle C McdonaldAbstract:Spatial and temporal variability in landscape freeze- thaw (FT) status at higher latitudes and elevations significantly impacts land surface water mobility and surface energy partitioning, with major consequences for regional climate, hydrological, ecological, and biogeochemical processes. With the development of new-generation spaceborne Remote Sensing instruments, future L-band missions, including the NASA Soil Moisture Active and Passive mission, will provide new operational retrievals of landscape FT state dynamics at moderate (~3 km) spatial resolution. We applied theoretical simulations of L-band Radar backscatter using first-order radiative transfer models with two and three-layer modeling schemes to develop a modified seasonal threshold algorithm (STA) and FT classification study over Alaska using 100-m-resolution satellite Phased Array L-band Synthetic Aperture Radar (PALSAR) observations. The backscatter threshold distinguishes between frozen and nonfrozen states, and it is used to classify the predominant frozen or thawed status of a grid cell. An Alaska FT map for April 2007 was generated from PALSAR (ScanSAR) observations and showed a regionally consistent but finer FT spatial pattern than an alternative surface air temperature-based classification derived from global reanalysis data. Validation of the STA-based FT classification against regional soil climate stations indicated approximately 80% and 75% spatial classification accuracy values in relation to respective station air temperature and soil temperature measurement-based FT estimates. An investigation of relative spatial scale effects on FT classification accuracy indicates that the relationship between grid cell size and classified frozen or thawed area follows a general logarithmic function.
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a simulation study of compact polarimetry for Radar retrieval of soil moisture
IEEE Transactions on Geoscience and Remote Sensing, 2014Co-Authors: Jeffrey D Ouellette, Mahta Moghaddam, J T Johnson, M Spencer, Leung Tsang, Dara EntekhabiAbstract:A compact polarimetric (CP) Radar system requires fewer measurements than a fully polarimetric (FP) system, thus allowing added flexibility in Radar system design. Previous studies have shown the potential of using compact polarimetry for Radar Remote Sensing of soil moisture. This paper extends previous studies by considering a time series data cube retrieval algorithm and measurements in the presence of vegetation. Vegetation information is assumed to be provided by an ancillary data source in the retrieval process. The performance of an algorithm for reconstructing FP information from CP measurements of vegetated soil surfaces is also examined. The results of the study show that only a modest degradation in soil moisture retrieval performance occurs when compact-pol measurements are used in place of full-pol data.
Fangni Lei - One of the best experts on this subject based on the ideXlab platform.
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data assimilation of high resolution thermal and Radar Remote Sensing retrievals for soil moisture monitoring in a drip irrigated vineyard
Remote Sensing of Environment, 2020Co-Authors: Fangni Lei, Wade T Crow, William P Kustas, Jianzhi Dong, Yun Yang, Kyle R Knipper, Martha C Anderson, Feng Gao, Claudia NotarnicolaAbstract:Efficient water use assessment and irrigation management is critical for the sustainability of irrigated agriculture, especially under changing climate conditions. Due to the impracticality of maintaining ground instrumentation over wide geographic areas, Remote Sensing and numerical model-based fine-scale mapping of soil water conditions have been applied for water resource applications at a range of spatial scales. Here, we present a prototype framework for integrating high-resolution thermal infrared (TIR) and synthetic aperture Radar (SAR) Remote Sensing data into a soil-vegetation-atmosphere-transfer (SVAT) model with the aim of providing improved estimates of surface- and root-zone soil moisture that can support optimized irrigation management strategies. Specifically, Remotely-sensed estimates of water stress (from TIR) and surface soil moisture retrievals (from SAR) are assimilated into a 30-m resolution SVAT model over a vineyard site in the Central Valley of California, U.S. The efficacy of our data assimilation algorithm is investigated via both the synthetic and real data experiments. Results demonstrate that a particle filtering approach is superior to an ensemble Kalman filter for handling the nonlinear relationship between model states and observations. In addition, biophysical conditions such as leaf area index are shown to impact the relationship between observations and states and must therefore be represented accurately in the assimilation model. Overall, both surface and root-zone soil moisture predicted via the SVAT model are enhanced through the assimilation of thermal and Radar-based retrievals, suggesting the potential for improving irrigation management at the agricultural sub-field scale using a data assimilation strategy.
John S Kimball - One of the best experts on this subject based on the ideXlab platform.
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Classification of Alaska Spring Thaw Characteristics Using Satellite L-Band Radar Remote Sensing
IEEE Transactions on Geoscience and Remote Sensing, 2015Co-Authors: Jinyang Du, John S Kimball, Marzieh Azarderakhsh, Scott R. Dunbar, Mahta Moghaddam, Kyle C McdonaldAbstract:Spatial and temporal variability in landscape freeze- thaw (FT) status at higher latitudes and elevations significantly impacts land surface water mobility and surface energy partitioning, with major consequences for regional climate, hydrological, ecological, and biogeochemical processes. With the development of new-generation spaceborne Remote Sensing instruments, future L-band missions, including the NASA Soil Moisture Active and Passive mission, will provide new operational retrievals of landscape FT state dynamics at moderate (~3 km) spatial resolution. We applied theoretical simulations of L-band Radar backscatter using first-order radiative transfer models with two and three-layer modeling schemes to develop a modified seasonal threshold algorithm (STA) and FT classification study over Alaska using 100-m-resolution satellite Phased Array L-band Synthetic Aperture Radar (PALSAR) observations. The backscatter threshold distinguishes between frozen and nonfrozen states, and it is used to classify the predominant frozen or thawed status of a grid cell. An Alaska FT map for April 2007 was generated from PALSAR (ScanSAR) observations and showed a regionally consistent but finer FT spatial pattern than an alternative surface air temperature-based classification derived from global reanalysis data. Validation of the STA-based FT classification against regional soil climate stations indicated approximately 80% and 75% spatial classification accuracy values in relation to respective station air temperature and soil temperature measurement-based FT estimates. An investigation of relative spatial scale effects on FT classification accuracy indicates that the relationship between grid cell size and classified frozen or thawed area follows a general logarithmic function.
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satellite Radar Remote Sensing of seasonal growing seasons for boreal and subalpine evergreen forests
Remote Sensing of Environment, 2004Co-Authors: John S Kimball, Kyle C Mcdonald, Steven W Running, Steve FrolkingAbstract:Abstract We evaluated whether satellite Radar Remote Sensing of landscape seasonal freeze–thaw cycles provides an effective measure of active growing season timing and duration for boreal and subalpine evergreen forests. Landscape daily Radar backscatter measurements from the SeaWinds scatterometer on-board the QuikSCAT satellite were evaluated across a regional network of North American coniferous forest sites for 2000 and 2001. Radar Remote Sensing measurements of the initiation and length of the growing season corresponded closely with both site measurements and ecosystem process model (BIOME-BGC) simulations of these parameters because of the sensitivity of the Ku-band scatterometer to snow cover freeze–thaw dynamics and associated linkages between growing season initiation and the timing of seasonal snowmelt. In contrast, Remote Sensing estimates of the timing of growing season termination were either weakly or not significantly associated with site measurements and model simulation results, due to the relative importance of light availability and other environmental controls on stand phenology in the fall. Regional patterns of estimated annual net primary production (NPP) and component photosynthetic and autotrophic respiration rates for the evergreen forest sites also corresponded favorably with Remote Sensing estimates of the seasonal timing of spring thaw and associated growing season length, indicating the importance of these parameters in determining spatial and temporal patterns of NPP and the potential utility of satellite Radar Remote Sensing for regional monitoring of the terrestrial biosphere.
