Showing papers in "IEEE Transactions on Geoscience and Remote Sensing in 2003"
TL;DR: The various algorithms being used for the remote sensing of cloud properties from MODIS data with an emphasis on the pixel-level retrievals (referred to as Level-2 products), with 1-km or 5-km spatial resolution at nadir are described.
Abstract: The Moderate Resolution Imaging Spectroradiometer (MODIS) is one of five instruments aboard the Terra Earth Observing System (EOS) platform launched in December 1999. After achieving final orbit, MODIS began Earth observations in late February 2000 and has been acquiring data since that time. The instrument is also being flown on the Aqua spacecraft, launched in May 2002. A comprehensive set of remote sensing algorithms for cloud detection and the retrieval of cloud physical and optical properties have been developed by members of the MODIS atmosphere science team. The archived products from these algorithms have applications in climate change studies, climate modeling, numerical weather prediction, as well as fundamental atmospheric research. In addition to an extensive cloud mask, products include cloud-top properties (temperature, pressure, effective emissivity), cloud thermodynamic phase, cloud optical and microphysical parameters (optical thickness, effective particle radius, water path), as well as derived statistics. We will describe the various algorithms being used for the remote sensing of cloud properties from MODIS data with an emphasis on the pixel-level retrievals (referred to as Level-2 products), with 1-km or 5-km spatial resolution at nadir. An example of each Level-2 cloud product from a common data granule (5 min of data) off the coast of South America will be discussed. Future efforts will also be mentioned. Relevant points related to the global gridded statistics products (Level-3) are highlighted though additional details are given in an accompanying paper in this issue.
1,636 citations
California Institute of Technology1, University of California, Santa Barbara2, National Oceanic and Atmospheric Administration3, University of Maryland, College Park4, University of Wisconsin-Madison5, Massachusetts Institute of Technology6, Langley Research Center7, University of Maryland, Baltimore County8, Goddard Space Flight Center9
TL;DR: Based on the excellent radiometric and spectral performance demonstrated by AIRS during prelaunch testing, it is expected the assimilation of AIRS data into the numerical weather forecast to result in significant forecast range and reliability improvements.
Abstract: The Atmospheric Infrared Sounder (AIRS), the Advanced Microwave Sounding Unit (AMSU), and the Humidity Sounder for Brazil (HSB) form an integrated cross-track scanning temperature and humidity sounding system on the Aqua satellite of the Earth Observing System (EOS). AIRS is an infrared spectrometer/radiometer that covers the 3.7-15.4-/spl mu/m spectral range with 2378 spectral channels. AMSU is a 15-channel microwave radiometer operating between 23 and 89 GHz. HSB is a four-channel microwave radiometer that makes measurements between 150 and 190 GHz. In addition to supporting the National Aeronautics and Space Administration's interest in process study and climate research, AIRS is the first hyperspectral infrared radiometer designed to support the operational requirements for medium-range weather forecasting of the National Ocean and Atmospheric Administration's National Centers for Environmental Prediction (NCEP) and other numerical weather forecasting centers. AIRS, together with the AMSU and HSB microwave radiometers, will achieve global retrieval accuracy of better than 1 K in the lower troposphere under clear and partly cloudy conditions. This paper presents an overview of the science objectives, AIRS/AMSU/HSB data products, retrieval algorithms, and the ground-data processing concepts. The EOS Aqua was launched on May 4, 2002 from Vandenberg AFB, CA, into a 705-km-high, sun-synchronous orbit. Based on the excellent radiometric and spectral performance demonstrated by AIRS during prelaunch testing, which has by now been verified during on-orbit testing, we expect the assimilation of AIRS data into the numerical weather forecast to result in significant forecast range and reliability improvements.
1,413 citations
TL;DR: The AMSR-E sensor calibration and extent of radio frequency interference are currently being assessed, to be followed by quantitative assessments of the soil moisture retrievals, which will provide evaluations of the retrieved soil moisture and enable improved hydrologic applications of the data.
Abstract: The Advanced Microwave Scanning Radiometer (AMSR-E) on the Earth Observing System (EOS) Aqua satellite was launched on May 4, 2002. The AMSR-E instrument provides a potentially improved soil moisture sensing capability over previous spaceborne radiometers such as the Scanning Multichannel Microwave Radiometer and Special Sensor Microwave/Imager due to its combination of low frequency and higher spatial resolution (approximately 60 km at 6.9 GHz). The AMSR-E soil moisture retrieval approach and its implementation are described in this paper. A postlaunch validation program is in progress that will provide evaluations of the retrieved soil moisture and enable improved hydrologic applications of the data. Key aspects of the validation program include assessments of the effects on retrieved soil moisture of variability in vegetation water content, surface temperature, and spatial heterogeneity. Examples of AMSR-E brightness temperature observations over land are shown from the first few months of instrument operation, indicating general features of global vegetation and soil moisture variability. The AMSR-E sensor calibration and extent of radio frequency interference are currently being assessed, to be followed by quantitative assessments of the soil moisture retrievals.
1,387 citations
TL;DR: Landsat-5 (L5) Thematic Mapper data processed and distributed by the U.S. Geological Survey (USGS) Earth Resources Observation System (EROS) Data Center (EDC) will be radiometrically calibrated using a new procedure and revised calibration parameters to improve absolute calibration accuracy, consistency over time, and consistency with Landsat-7 (L7) Enhanced Thematic mapper Plus (ETM+) data.
Abstract: Effective May 5, 2003, Landsat-5 (L5) Thematic Mapper (TM) data processed and distributed by the U.S. Geological Survey (USGS) Earth Resources Observation System (EROS) Data Center (EDC) will be radiometrically calibrated using a new procedure and revised calibration parameters. This change will improve absolute calibration accuracy, consistency over time, and consistency with Landsat-7 (L7) Enhanced Thematic Mapper Plus (ETM+) data. Users will need to use new parameters to convert the calibrated data products to radiance. The new procedure for the reflective bands (1-5,7) is based on a lifetime radiometric calibration curve for the instrument derived from the instrument's internal calibrator, cross-calibration with the ETM+, and vicarious measurements. The thermal band will continue to be calibrated using the internal calibrator. Further updates to improve the relative detector-to-detector calibration and thermal band calibration are being investigated, as is the calibration of the Landsat-4 (L4) TM.
1,108 citations
TL;DR: The Moderate Resolution Imaging Spectroradiometer is an Earth-viewing sensor that flies on the Earth Observing System Terra and Aqua satellites, launched in 1999 and 2002, respectively that provides atmospheric properties such as cloud mask, atmospheric profiles, aerosol properties, total precipitable water, and cloud properties.
Abstract: The Moderate Resolution Imaging Spectroradiometer (MODIS) is an Earth-viewing sensor that flies on the Earth Observing System Terra and Aqua satellites, launched in 1999 and 2002, respectively. MODIS scans a swath width of 2330 km that is sufficiently wide to provide nearly complete global coverage every two days from a polar-orbiting, Sun-synchronous, platform at an altitude of 705 km. MODIS provides images in 36 spectral bands between 0.415 and 14.235 /spl mu/m with spatial resolutions of 250 m (two bands), 500 m (five bands), and 1000 m (29 bands). These bands have been carefully selected to enable advanced studies of land, ocean, and atmospheric properties. Twenty-six bands are used to derive atmospheric properties such as cloud mask, atmospheric profiles, aerosol properties, total precipitable water, and cloud properties. We describe each of these atmospheric data products, including characteristics of each of these products such as file size, spatial resolution used in producing the product, and data availability.
905 citations
TL;DR: A progressive morphological filter was developed to detect nonground LIDAR measurements and shows that the filter can remove most of the nong round points effectively.
Abstract: Recent advances in airborne light detection and ranging (LIDAR) technology allow rapid and inexpensive measurements of topography over large areas. This technology is becoming a primary method for generating high-resolution digital terrain models (DTMs) that are essential to numerous applications such as flood modeling and landslide prediction. Airborne LIDAR systems usually return a three-dimensional cloud of point measurements from reflective objects scanned by the laser beneath the flight path. In order to generate a DTM, measurements from nonground features such as buildings, vehicles, and vegetation have to be classified and removed. In this paper, a progressive morphological filter was developed to detect nonground LIDAR measurements. By gradually increasing the window size of the filter and using elevation difference thresholds, the measurements of vehicles, vegetation, and buildings are removed, while ground data are preserved. Datasets from mountainous and flat urbanized areas were selected to test the progressive morphological filter. The results show that the filter can remove most of the nonground points effectively.
871 citations
TL;DR: It is seen that relatively few features are needed to achieve the same classification accuracies as in the original feature space when classification of panchromatic high-resolution data from urban areas using morphological and neural approaches.
Abstract: Classification of panchromatic high-resolution data from urban areas using morphological and neural approaches is investigated. The proposed approach is based on three steps. First, the composition of geodesic opening and closing operations of different sizes is used in order to build a differential morphological profile that records image structural information. Although, the original panchromatic image only has one data channel, the use of the composition operations will give many additional channels, which may contain redundancies. Therefore, feature extraction or feature selection is applied in the second step. Both discriminant analysis feature extraction and decision boundary feature extraction are investigated in the second step along with a simple feature selection based on picking the largest indexes of the differential morphological profiles. Third, a neural network is used to classify the features from the second step. The proposed approach is applied in experiments on high-resolution Indian Remote Sensing 1C (IRS-1C) and IKONOS remote sensing data from urban areas. In experiments, the proposed method performs well in terms of classification accuracies. It is seen that relatively few features are needed to achieve the same classification accuracies as in the original feature space.
756 citations
TL;DR: New state-of-the-art methodology is described to analyze the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit/Humidity Sounder for Brazil (AIRS/AMSU/HSB) data in the presence of multiple cloud formations, suggesting clear column radiances can be reconstructed under partial cloud cover with an accuracy comparable to single spot channel noise in the temperature and water vapor sounding regions.
Abstract: New state-of-the-art methodology is described to analyze the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit/Humidity Sounder for Brazil (AIRS/AMSU/HSB) data in the presence of multiple cloud formations. The methodology forms the basis for the AIRS Science Team algorithm, which will be used to analyze AIRS/AMSU/HSB data on the Earth Observing System Aqua platform. The cloud-clearing methodology requires no knowledge of the spectral properties of the clouds. The basic retrieval methodology is general and extracts the maximum information from the radiances, consistent with the channel noise covariance matrix. The retrieval methodology minimizes the dependence of the solution on the first-guess field and the first-guess error characteristics. Results are shown for AIRS Science Team simulation studies with multiple cloud formations. These simulation studies imply that clear column radiances can be reconstructed under partial cloud cover with an accuracy comparable to single spot channel noise in the temperature and water vapor sounding regions; temperature soundings can be produced under partial cloud cover with RMS errors on the order of, or better than, 1 K in 1-km-thick layers from the surface to 700 mb, 1-km layers from 700-300 mb, 3-km layers from 300-30 mb, and 5-km layers from 30-1 mb; and moisture profiles can be obtained with an accuracy better than 20% absolute errors in 1-km layers from the surface to nearly 200 mb.
706 citations
TL;DR: An advanced technique for the generation of deformation maps using synthetic aperture radar (SAR) data is presented, which estimates the linear and nonlinear components of the displacement, the error of the digital elevation model (DEM), and the atmospheric artifacts from a reduced set of low spatial resolution interferograms.
Abstract: In this paper, an advanced technique for the generation of deformation maps using synthetic aperture radar (SAR) data is presented. The algorithm estimates the linear and nonlinear components of the displacement, the error of the digital elevation model (DEM) used to cancel the topographic terms, and the atmospheric artifacts from a reduced set of low spatial resolution interferograms. The pixel candidates are selected from those presenting a good coherence level in the whole set of interferograms and the resulting nonuniform mesh tessellated with the Delauney triangulation to establish connections among them. The linear component of movement and DEM error are estimated adjusting a linear model to the data only on the connections. Later on, this information, once unwrapped to retrieve the absolute values, is used to calculate the nonlinear component of movement and atmospheric artifacts with alternate filtering techniques in both the temporal and spatial domains. The method presents high flexibility with respect to the required number of images and the baselines length. However, better results are obtained with large datasets of short baseline interferograms. The technique has been tested with European Remote Sensing SAR data from an area of Catalonia (Spain) and validated with on-field precise leveling measurements.
680 citations
TL;DR: Initial results over several sites with established ground truth and years of airborne hyperspectral data show that Hyperion data from the shortwave infrared spectrometer can be used to produce useful geologic (mineralogic) information, but also indicate that SNR improvements are required for future spaceborne sensors to allow the same level of mapping that is currently possible from airborne sensors such as AVIRIS.
Abstract: Airborne hyperspectral data have been available to researchers since the early 1980s and their use for geologic applications is well documented. The launch of the National Aeronautics and Space Administration Earth Observing 1 Hyperion sensor in November 2000 marked the establishment of a test bed for spaceborne hyperspectral capabilities. Hyperion covers the 0.4-2.5-/spl mu/m range with 242 spectral bands at approximately 10-nm spectral resolution and 30-m spatial resolution. Analytical Imaging and Geophysics LLC and the Commonwealth Scientific and Industrial Research Organisation have been involved in efforts to evaluate, validate, and demonstrate Hyperions's utility for geologic mapping in a variety of sites in the United States and around the world. Initial results over several sites with established ground truth and years of airborne hyperspectral data show that Hyperion data from the shortwave infrared spectrometer can be used to produce useful geologic (mineralogic) information. Minerals mapped include carbonates, chlorite, epidote, kaolinite, alunite, buddingtonite, muscovite, hydrothermal silica, and zeolite. Hyperion data collected under optimum conditions (summer season, bright targets, well-exposed geology) indicate that Hyperion data meet prelaunch specifications and allow subtle distinctions such as determining the difference between calcite and dolomite and mapping solid solution differences in micas caused by substitution in octahedral molecular sites. Comparison of airborne hyperspectral data [from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS)] to the Hyperion data establishes that Hyperion provides similar basic mineralogic information, with the principal limitation being limited mapping of fine spectral detail under less-than-optimum acquisition conditions (winter season, dark targets) based on lower signal-to-noise ratios. Case histories demonstrate the analysis methodologies and level of information available from the Hyperion data. They also show the viability of Hyperion as a means of extending hyperspectral mineral mapping to areas not accessible to aircraft sensors. The analysis results demonstrate that spaceborne hyperspectral sensors can produce useful mineralogic information, but also indicate that SNR improvements are required for future spaceborne sensors to allow the same level of mapping that is currently possible from airborne sensors such as AVIRIS.
659 citations
TL;DR: The results based on the new version (advanced IEM) indicate that significant improvements for emissivity prediction may be obtained for a wide range of roughness scales, in particular in the intermediate roughness regions.
Abstract: This paper presents a model of microwave emissions from rough surfaces. We derive a more complete expression of the single-scattering terms in the integral equation method (IEM) surface scattering model. The complementary components for the scattered fields are rederived, based on the removal of a simplifying assumption in the spectral representation of Green's function. In addition, new but compact expressions for the complementary field coefficients can be obtained after quite lengthy mathematical manipulations. Three-dimensional Monte Carlo simulations of surface emission from Gaussian rough surfaces were used to examine the validity of the model. The results based on the new version (advanced IEM) indicate that significant improvements for emissivity prediction may be obtained for a wide range of roughness scales, in particular in the intermediate roughness regions. It is also shown that the original IEM produces larger errors that lead to tens of Kelvins in brightness temperature, which are unacceptable for passive remote sensing.
TL;DR: The Hyperion Imaging Spectrometer was the first imaging spectrometer to routinely acquire science-grade data from Earth orbit and met or exceeded predictions including continued operation well beyond the planned one-year program.
Abstract: The Hyperion Imaging Spectrometer was the first imaging spectrometer to routinely acquire science-grade data from Earth orbit. Instrument performance was validated and carefully monitored through a combination of calibration approaches: solar, lunar, earth (vicarious) and atmospheric observations complemented by onboard calibration lamps and extensive prelaunch calibration. Innovative techniques for spectral calibration of space-based sensors were also tested and validated. Instrument performance met or exceeded predictions including continued operation well beyond the planned one-year program.
TL;DR: It demonstrates how three polarimetric parameters, namely the scattering entropy, the scattering anisotropy, and the alpha angle may be used in order to decouple surface roughness from moisture content estimation offering the possibility of a straightforward inversion of these two surface parameters.
Abstract: Proposes a new model for the inversion of surface roughness and soil moisture from polarimetric synthetic aperture radar (SAR) data, based on the eigenvalues and eigenvectors of the polarimetric coherency matrix. It demonstrates how three polarimetric parameters, namely the scattering entropy (H), the scattering anisotropy (A), and the alpha angle (/spl alpha/) may be used in order to decouple surface roughness from moisture content estimation offering the possibility of a straightforward inversion of these two surface parameters. The potential of the proposed inversion algorithm is investigated using fully polarimetric laboratory measurements as well as airborne L-band SAR data and ground measurements from two different test sites in Germany, the Elbe-Auen site and the Weiherbach site.
TL;DR: AMSR-E is a modified version of AMSR that was launched December 2002 aboard the Advanced Earth Observing Satellite-II (ADEOS-II), a six-frequency dual-polarized total-power passive microwave radiometer that observes water-related geophysical parameters supporting global change science and monitoring efforts.
Abstract: The Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) was developed and provided to the National Aeronautics and Space Administration's EOS Aqua satellite by the National Space Development Agency of Japan, as one of the indispensable instruments for Aqua's mission. AMSR-E is a modified version of AMSR that was launched December 2002 aboard the Advanced Earth Observing Satellite-II (ADEOS-II). It is a six-frequency dual-polarized total-power passive microwave radiometer that observes water-related geophysical parameters supporting global change science and monitoring efforts. The hardware improvements over existing spaceborne microwave radiometers for Earth imaging include the largest main reflector of its kind and addition of 6.925-GHz channels. These improvements provide finer spatial resolution and the capability to retrieve sea surface temperature and soil moisture information on a global basis. This paper provides an overview of the instrument characteristics, mission objectives, and data products.
TL;DR: A methodologically simple approach to estimate snow depth from spaceborne microwave instruments is described and will be developed further and implemented for use with the Advanced Microwave Scanning Radiometer-Earth Observing System aboard Aqua.
Abstract: A methodologically simple approach to estimate snow depth from spaceborne microwave instruments is described. The scattering signal observed in multifrequency passive microwave data is used to detect snow cover. Wet snow, frozen ground, precipitation, and other anomalous scattering signals are screened using established methods. The results from two different approaches (a simple time and continentwide static approach and a space and time dynamic approach) to estimating snow depth were compared. The static approach, based on radiative transfer calculations, assumes a temporally constant grain size and density. The dynamic approach assumes that snowpack properties are spatially and temporally dynamic and requires two simple empirical models of density and snowpack grain radius evolution, plus a dense media radiative transfer model based on the quasicrystalline approximation and sticky particle theory. To test the approaches, a four-year record of daily snow depth measurements at 71 meteorological stations plus passive microwave data from the Special Sensor Microwave Imager, land cover data and a digital elevation model were used. In addition, testing was performed for a global dataset of over 1000 World Meteorological Organization meteorological stations recording snow depth during the 2000-2001 winter season. When compared with the snow depth data, the new algorithm had an average error of 23 cm for the one-year dataset and 21 cm for the four-year dataset (131% and 94% relative error, respectively). More importantly, the dynamic algorithm tended to underestimate the snow depth less than the static algorithm. This approach will be developed further and implemented for use with the Advanced Microwave Scanning Radiometer-Earth Observing System aboard Aqua.
TL;DR: Environmental noise calculations demonstrate that Hyperion has sufficient sensitivity to detect optical water quality concentrations of colored dissolved organic matter, chlorophyll, and suspended matter in the complex waters of Moreton Bay.
Abstract: The successful launch of Hyperion in November 2000 bridged the gap between the high-resolution (spatial and spectral) airborne remote sensing and the lower resolution satellite remote sensing. Although designed as a technical demonstration for land applications, Hyperion was tested for its capabilities over a range of water targets in Eastern Australia, including Moreton Bay in southern Queensland. Moreton Bay was the only Australian Earth Observing 1 (EO-1) Hyperion coastal site used for calibration/validation activities. This region was selected due to its spatial gradients in optical depth, water quality, bathymetry, and substrate composition. A combination of turbid and humic river inputs, as well as the open ocean flushing, determines the water quality of the bay. The field campaigns were coincident with Hyperion overpasses, retrieved inherent optical properties, apparent optical properties, substrate reflectance spectra, and water quality parameters. Environmental noise calculations demonstrate that Hyperion has sufficient sensitivity to detect optical water quality concentrations of colored dissolved organic matter, chlorophyll, and suspended matter in the complex waters of Moreton Bay. A methodology was developed integrating atmospheric and hydrooptical radiative transfer models (MODTRAN-4, Hydrolight) to estimate the underwater light field. A matrix inversion method was applied to retrieve concentrations of chlorophyll, colored dissolved organic matter, and suspended matter, which were comparable to those estimated in the field on the days of the overpass.
TL;DR: Preprocessing, which includes fixing bad and outlier pixels, local destriping, atmospheric correction, and minimum noise fraction smoothing, provides improved results and it is feasible to develop a consistent and standardized time series of data that is compatible with field-scale and airborne measured indexes.
Abstract: The benefits of Hyperion hyperspectral data to agriculture have been studied at sites in the Coleambally Irrigation Area of Australia. Hyperion can provide effective measures of agricultural performance through the use of established spectral indexes if systematic and random noise is managed. The noise management strategy includes recognition of "bad" pixels, reducing the effects of vertical striping, and compensation for atmospheric effects in the data. It also aims to reduce compounding of these effects by image processing. As the noise structure is different for Hyperion's two spectrometers, noise reduction methods are best applied to each separately. Results show that a local destriping algorithm reduces striping noise without introducing unwanted effects in the image. They also show how data smoothing can clean the data and how careful selection of stable Hyperion bands can minimize residual atmospheric effects following atmospheric correction. Comparing hyperspectral indexes derived from Hyperion with the same indexes derived from ground-measured spectra allowed us to assess some of these impacts on the preprocessing options. It has been concluded that preprocessing, which includes fixing bad and outlier pixels, local destriping, atmospheric correction, and minimum noise fraction smoothing, provides improved results. If these or equivalent preprocessing steps are followed, it is feasible to develop a consistent and standardized time series of data that is compatible with field-scale and airborne measured indexes. Red-edge and leaf chlorophyll indexes based on the preprocessed data are shown to distinguish different levels of stress induced by water restrictions.
TL;DR: Different methods for topographic correction of Landsat Thematic Mapper images have been assessed in the context of mapping vegetation types, with the best results obtained with a variation of the C method, which takes into account the overcorrection of low illuminated slopes by the original C method.
Abstract: Different methods for topographic correction of Landsat Thematic Mapper images have been assessed in the context of mapping vegetation types. The best results were obtained with a variation of the C method, which takes into account the overcorrection of low illuminated slopes by the original C method. The performance of this method was tested using two criteria: the changes in the spectral characteristics of the image and the reduction in standard deviation of each vegetation type after the correction.
TL;DR: A test statistic for equality of two such matrices and an associated asymptotic probability for obtaining a smaller value of the test statistic are derived and applied successfully to change detection in polarimetric SAR data.
Abstract: When working with multilook fully polarimetric synthetic aperture radar (SAR) data, an appropriate way of representing the backscattered signal consists of the so-called covariance matrix. For each pixel, this is a 3/spl times/3 Hermitian positive definite matrix that follows a complex Wishart distribution. Based on this distribution, a test statistic for equality of two such matrices and an associated asymptotic probability for obtaining a smaller value of the test statistic are derived and applied successfully to change detection in polarimetric SAR data. In a case study, EMISAR L-band data from April 17, 1998 and May 20, 1998 covering agricultural fields near Foulum, Denmark are used. Multilook full covariance matrix data, azimuthal symmetric data, covariance matrix diagonal-only data, and horizontal-horizontal (HH), vertical-vertical (VV), or horizontal-vertical (HV) data alone can be used. If applied to HH, VV, or HV data alone, the derived test statistic reduces to the well-known gamma likelihood-ratio test statistic. The derived test statistic and the associated significance value can be applied as a line or edge detector in fully polarimetric SAR data also.
TL;DR: The detection of both time-uniform and seasonal deformation phenomena is addressed, and a first assessment of the precision achievable by means of the PS Technique is discussed.
Abstract: Spaceborne differential radar interferometry has proven a remarkable potential for mapping ground deformation phenomena (e.g., urban subsidence, volcano dynamics, coseismic and postseismic displacements along faults, as well as slope instability). However, a full operational capability has not been achieved yet due to atmospheric disturbances and phase decorrelation phenomena. These drawbacks can often be-at least partially-overcome by carrying out measurements on a subset of image pixels corresponding to natural or artificial stable reflectors [permanent scatterers (PS)] and exploiting long temporal series of interferometric data. This approach allows one to push the measurement precision very close to its theoretical limit (in the order of /spl sim/1 mm for C-band European Remote Sensing (ERS)-like sensors). In this paper, the detection of both time-uniform and seasonal deformation phenomena is addressed, and a first assessment of the precision achievable by means of the PS Technique is discussed. Results highlighting deformation phenomena occurring in two test sites in California are reported (Fremont in the Southern Bay Area and San Jose in the Santa Clara Valley).
TL;DR: A novel Bayesian-based algorithm within the framework of wavelet analysis is proposed, which reduces speckle in SAR images while preserving the structural features and textural information of the scene.
Abstract: Synthetic aperture radar (SAR) images are inherently affected by multiplicative speckle noise, which is due to the coherent nature of the scattering phenomenon. This paper proposes a novel Bayesian-based algorithm within the framework of wavelet analysis, which reduces speckle in SAR images while preserving the structural features and textural information of the scene. First, we show that the subband decompositions of logarithmically transformed SAR images are accurately modeled by alpha-stable distributions, a family of heavy-tailed densities. Consequently, we exploit this a priori information by designing a maximum a posteriori (MAP) estimator. We use the alpha-stable model to develop a blind speckle-suppression processor that performs a nonlinear operation on the data and we relate this nonlinearity to the degree of non-Gaussianity of the data. Finally, we compare our proposed method to current state-of-the-art soft thresholding techniques applied on real SAR imagery and we quantify the achieved performance improvement.
TL;DR: The Aqua satellite carries six distinct Earth-observing instruments to measure numerous aspects of Earth's atmosphere, land, oceans, biosphere, and cryosphere, with a concentration on water in the Earth system.
Abstract: Aqua is a major satellite mission of the Earth Observing System (EOS), an international program centered at the U.S. National Aeronautics and Space Administration (NASA). The Aqua satellite carries six distinct Earth-observing instruments to measure numerous aspects of Earth's atmosphere, land, oceans, biosphere, and cryosphere, with a concentration on water in the Earth system. Launched on May 4, 2002, the satellite is in a Sun-synchronous orbit at an altitude of 705 km, with a track that takes it north across the equator at 1:30 p.m. and south across the equator at 1:30 a.m. All of its Earth-observing instruments are operating, and all have the ability to obtain global measurements within two days. The Aqua data will be archived and available to the research community through four Distributed Active Archive Centers (DAACs).
TL;DR: An object-based approach for urban land cover classification from high-resolution multispectral image data that builds upon a pixel-based fuzzy classification approach is presented and is able to identify buildings, impervious surface, and roads in dense urban areas with 76, 81, and 99% classification accuracies.
Abstract: In this paper, we present an object-based approach for urban land cover classification from high-resolution multispectral image data that builds upon a pixel-based fuzzy classification approach. This combined pixel/object approach is demonstrated using pan-sharpened multispectral IKONOS imagery from dense urban areas. The fuzzy pixel-based classifier utilizes both spectral and spatial information to discriminate between spectrally similar road and building urban land cover classes. After the pixel-based classification, a technique that utilizes both spectral and spatial heterogeneity is used to segment the image to facilitate further object-based classification. An object-based fuzzy logic classifier is then implemented to improve upon the pixel-based classification by identifying one additional class in dense urban areas: nonroad, nonbuilding impervious surface. With the fuzzy pixel-based classification as input, the object-based classifier then uses shape, spectral, and neighborhood features to determine the final classification of the segmented image. Using these techniques, the object-based classifier is able to identify buildings, impervious surface, and roads in dense urban areas with 76%, 81%, and 99% classification accuracies, respectively.
TL;DR: Field spectrometer data and leaf area index (LAI) measurements were collected on the same day as the Earth Observing 1 satellite overpass for a study site in the Patagonia region of Argentina to determine the most effective bands for forest LAI estimation.
Abstract: Field spectrometer data and leaf area index (LAI) measurements were collected on the same day as the Earth Observing 1 satellite overpass for a study site in the Patagonia region of Argentina. We first simulated the total at-sensor radiances using MODTRAN 4 for atmospheric correction. Then ground spectroradiometric measurements were used to improve the retrieved reflectance for each pixel on the Hyperion image. Using the improved pixel-based surface reflectance spectra, 12 two-band "vegetation indices (VIs)" were constructed using all available 168 Hyperion bands. Finally, we evaluated the correlation of each possible vegetation index with LAI measurements to determine the most effective bands for forest LAI estimation. The experimental results indicate that most of the important hyperspectral bands with high R/sup 2/ are related to bands in the shortwave infrared (SWIR) region and some in the near-infrared (NIR) region. The bands are centered near 820, 1040, 1200, 1250, 1650, 2100, and 2260 nm with bandwidths ranging from 10-300 nm. It is notable that the originally defined VIs that use red and NIR bands did not produce higher correlation with LAI than VIs constructed with bands in SWIR and NIR regions.
TL;DR: The EO-1 sensors, Hyperion and ALI, provide data with excellent discrimination for Pacific Northwest forests in comparison to Landsat-7 ETM+.
Abstract: Hyperion (a hyperspectral sensor) and the Advanced Land Imager (ALI) (a multispectral sensor) are carried on the National Aeronautics and Space Administration's Earth Observing 1 (EO-1) satellite. The Evaluation and Validation of EO-1 for Sustainable Development (EVEOSD) is our project supporting the EO-1 mission. With 10% of the world's forests and the second largest country by area in the world, Canada has a natural requirement for effective monitoring of its forests. Eight test sites have been selected for EVEOSD, with seven in Canada and one in the United States. Extensive fieldwork has been conducted at four of these sites. A comparison is made of forest classification results from Hyperion, ALI, and the Enhanced Thematic Mapper Plus (ETM+) of Landsat-7 for the Greater Victoria Watershed. The data have been radiometrically corrected and orthorectified. Feature selection and statistical transforms are used to reduce the Hyperion feature space from 198 channels to 11 features. Classes chosen for discrimination included Douglas-fir, hemlock, western redcedar, lodgepole pine, and red alder. Overall classification accuracies obtained for each sensor were Hyperion 90.0%, ALI 84.8%, and ETM+ 75.0%. Hyperspectral remote sensing provides significant advantages and greater accuracies over ETM+ for forest discrimination. The EO-1 sensors, Hyperion and ALI, provide data with excellent discrimination for Pacific Northwest forests in comparison to Landsat-7 ETM+.
TL;DR: The EO-1 satellite system is reviewed and details of the instruments and their performance as measured during the first year of operation are provided and calibration techniques and tradeoffs between alternative approaches are discussed.
Abstract: The Earth Observing One (EO-1) satellite, a part of National Aeronautics and Space Administration's New Millennium Program, was developed to demonstrate new technologies and strategies for improved Earth observations. It was launched from Vandenburg Air Force Base on November 21, 2000. The EO-1 satellite contains three observing instruments supported by a variety of newly developed space technologies. The Advanced Land Imager (ALI) is a prototype for a new generation of Landsat-7 Thematic Mapper. The Hyperion Imaging Spectrometer is the first high spatial resolution imaging spectrometer to orbit the Earth. The Linear Etalon Imaging Spectral Array (LEISA) Atmospheric Corrector (LAC) is a high spectral resolution wedge imaging spectrometer designed to measure atmospheric water vapor content. Instrument performances are validated and carefully monitored through a combination of radiometric calibration approaches: solar, lunar, stellar, Earth (vicarious), and atmospheric observations complemented by onboard calibration lamps and extensive prelaunch calibration. Techniques for spectral calibration of space-based sensors have been tested and validated with Hyperion. ALI and Hyperion instrument performance continue to meet or exceed predictions well beyond the planned one-year program. This paper reviews the EO-1 satellite system and provides details of the instruments and their performance as measured during the first year of operation. Calibration techniques and tradeoffs between alternative approaches are discussed. An overview of the science applications for instrument performance assessment is presented.
TL;DR: Significant limitations for current multispectral sensors were identified, where the location and broadband character of the spectral bands only marginally resolved the complex spectral characteristics of the urban environment, especially for built surface types.
Abstract: This study evaluated how spectral resolution of high-spatial resolution optical remote sensing data influences detailed mapping of urban land cover. A comprehensive regional spectral library and low altitude data from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) were used to characterize the spectral properties of urban land cover. The Bhattacharyya distance was applied as a measure of spectral separability to determine a most suitable subset of 14 AVIRIS bands for urban mapping. We evaluated the performance of this spectral setting versus common multispectral sensors such as Ikonos by assessing classification accuracy for 26 urban land cover classes. Significant limitations for current multispectral sensors were identified, where the location and broadband character of the spectral bands only marginally resolved the complex spectral characteristics of the urban environment, especially for built surface types. However, the AVIRIS classification accuracy did not exceed 66.6% for 22 urban cover types, primarily due to spectral similarities of specific urban materials and high within-class variability.
TL;DR: The two main elements of the Atmospheric Infrared Sounder radiative transfer algorithm (AIRS-RTA) are described in this paper: the fast parameterization of the atmospheric transmittances that are used to perform the AIRS physical retrievals and the spectroscopy used to generate the parameterized transmittance.
Abstract: The two main elements of the Atmospheric Infrared Sounder radiative transfer algorithm (AIRS-RTA) are described in this paper: 1) the fast parameterization of the atmospheric transmittances that are used to perform the AIRS physical retrievals and 2) the spectroscopy used to generate the parameterized transmittances. We concentrate on those aspects of the spectroscopy that are especially relevant for temperature and water vapor retrievals. The AIRS-RTA is a hybrid model in that it parameterizes most gases on a fixed grid of pressures, while the water optical depths are parameterized on a fixed grid of water amounts. Water vapor, ozone, carbon monoxide, and methane profiles can be varied, in addition to the column abundance of carbon dioxide.
TL;DR: This work investigates the possibility to focus synthetic aperture radar data relative to the same area, neglecting any mutual interaction between the targets, and assuming the propagation in homogeneous media, to achieve three-dimensional tomography reconstruction in presence of volumetric scattering in the elevation direction.
Abstract: Deals with the use of multipass synthetic aperture radar (SAR) data in order to achieve three-dimensional tomography reconstruction in presence of volumetric scattering. Starting from azimuth- and range-focused SAR data relative to the same area, neglecting any mutual interaction between the targets, and assuming the propagation in homogeneous media, we investigate the possibility to focus the data also in the elevation direction. The problem is formulated in the framework of linear inverse problem and the solution makes use of the singular value decomposition of the relevant operator. This allows us to properly take into account nonuniform orbit separation and to exploit a priori knowledge regarding the size of the volume interested by the scattering mechanism, thus leading to superresolution in the elevation direction. Results obtained on simulated data demonstrate the feasibility of the proposed processing technique.
TL;DR: The theoretical basis and initial performance of the algorithms that are used to derive sea ice concentration, ice temperature, and snow depth on sea ice from newly acquired Earth Observing System-Aqua/Advanced Microwave Scanning Radiometer-EOS (AMSR-E) radiances is presented.
Abstract: A summary of the theoretical basis and initial performance of the algorithms that are used to derive sea ice concentration, ice temperature, and snow depth on sea ice from newly acquired Earth Observing System-Aqua/Advanced Microwave Scanning Radiometer-EOS (AMSR-E) radiances is presented. The algorithms have been developed and tested using historical satellite passive microwave data and are expected to provide more accurate products, since they are designed to take advantage of the wider range of frequencies and higher spatial resolution of the AMSR-E microwave instrument. Validation programs involving coordinated satellite, aircraft, and surface measurements to determine the accuracies of these sea ice products and to improve further our capability to monitor global sea ice are currently underway.