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In this paper, we consider the development of algorithms for the automatic detection of buried threats using ground penetrating radar (GPR) measurements. GPR is one of the most studied and successful modalities for automatic buried threat detection (BTD), and a large variety of BTD algorithms have been proposed for it. Despite this, large-scale comparisons of GPR-based BTD algorithms are rare in the literature. In this paper, we report the results of a multi-institutional effort to develop advanced BTD algorithms for a real-world GPR BTD system. The effort involved five institutions with substantial experience with the development of GPR-based BTD algorithms. In this paper, we report the technical details of the advanced algorithms submitted by each institution, representing their latest technical advances, and many state-of-the-art GPR-based BTD algorithms. We also report the results of evaluating the algorithms from each institution on the large experimental data set used for development. The experimental data set comprised 120 000 m2 of GPR data using surface area, from 13 different lanes across two U.S. test sites. The data were collected using a vehicle-mounted GPR system, the variants of which have supplied data for numerous publications. Using these results, we identify the most successful and common processing strategies among the submitted algorithms, and make recommendations for GPR-based BTD algorithm design. [ABSTRACT FROM AUTHOR]

The accurate estimation of the terrestrial latent heat flux (LE) from satellite observations at high spatial and temporal scales plays an important role in the assessment of the water and heat exchange between the earth’s surface and the atmosphere. Although a variety of data fusion methods have been proposed to merge different LE products for more reliable estimates, most of them have ignored the spatiotemporal consistency of LE products across different resolutions. In this paper, we apply the multiresolution tree (MRT) method to improve the accuracy and reduce the inconsistency between the Moderate Resolution Imaging Spectroradiometer (MODIS) LE (MOD16) product and the Landsat-based LE product at different resolutions. Eddy covariance (EC) ground measurements at five sites, MODIS and Landsat images from January 2005 to December 2005 in the north central USA, are used to evaluate the performance of the MRT method. The results show that the MRT method can improve the accuracy of the original LE products (MOD16 and Landsat), and it has the potential to significantly reduce the uncertainty and inconsistency of these products. The bias decreased by 38.3% on average, and the root-mean-square error (RMSE) decreased by approximately 49.2% after the MRT was applied at each scale. Further studies are still required to make the MRT method more universal on a variety of land cover types for long-time periods. [ABSTRACT FROM AUTHOR]

In Part I of this paper, an operational fully automated Landsat-like image spectral rule-based decision-tree classifier (LSRC), suitable for mapping radiometrically calibrated sevenband Landsat-4/-5 Thematic Mapper (TM) and Landsat-7 Enhanced TM+ (ETM+) spaceborne images [eventually synthesized from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and theModerate Resolution Imaging Spectroradiometer (MODIS) imaging sensor] into a discrete and finite set of spectral categories, has been downscaled to properly deal with spaceborne multispectral imaging sensors whose spectral resolution overlaps with, but is inferior to Landsat's, namely: 1) Satellite Pour l'Observation de la Terre (SPOT)-4/-5, Indian Remote Sensing Satellite (IRS)-1C/-1D/-P6 Linear Imaging Self-Scanner (LISS)-III, and IRS-P6 Advanced Wide Field Sensor (AWiFS); 2) National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) and Meteosat Second Generation (MSG); 3) Environmental Satellite (ENVISAT) Advanced Along-Track Scanning Radiometer (AATSR); 4) GeoEye-1, IKONOS-2, QuickBird-2, OrbView-3, TopSat, KOrean MultiPurpose SATellite (KOMPSAT)-2, FORMOsa SATellite (FORMOSAT)-2, Advanced Land Observing Satellite (ALOS) Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2), RapidEye, WorldView-2, PLEIADES-1/-2, and SPOT-6/-7; and 5) Disaster Monitoring Constellation (DMC), IRS-P6 LISS-IV, and SPOT-1/-2. LSRC, together with its five downscaled versions, identified, respectively, as the four-band SPOT-like SRC (SSRC), the four-band AVHRR-like SRC (AVSRC), the five-band AATSR-like SRC (AASRC), the four-band IKONOS-like SRC (ISRC), and the three-band DMClike SRC (DSRC), form the so-called integrated SRC system of systems. In this paper, first, the classification accuracy and robustness to changes in the input data set of SSRC, AVSRC, AASRC, ISRC, and DSRC are assessed, both qualitatively and quantitatively, in comparison with LSRC's. Next, ongoing and future SRC applications are presented and discussed. They encompass: 1) the implementation of operational two-stage stratified hierarchical Remote Sensing (RS) image understanding systems discussed in Part I of this paper; 2) the integration of near real-time satellite mapping services with Internet map servers; and 3) the development of a new approach to semantic querying of large-scale multisensor image databases. These experimental results and application examples prove that the integrated SRC system of systems is operational, namely, it is effective, near real-time, automatic, and robust to changes in the input data set. Therefore, SRC appears eligible for use in operational satellite-based measurement systems such as those envisaged by the ongoing international Global Earth Observation System of Systems (GEOSS) Programme and the Global Monitoring for Environment and Security (GMES) system project. [ABSTRACT FROM AUTHOR]

Combining data sets from multiple satellite sensors is a powerful method for studying Earth-atmosphere problems. By fusing data, we can utilize the strengths of the individual sensors that may not be otherwise possible. In this paper, we provide the framework for combining level 2 data products, using data from three sensors aboard the National Aeronautics and Space Administration (NASA)'s Terra satellite. These data include top-of-the-atmosphere (TOA) radiative energy fluxes obtained from the Clouds and the Earth's Radiant Energy System (CERES), aerosol optical thickness from the multispectral Moderate Resolution Imaging Spectroradiometer (MODIS), and aerosol properties from the Multi-angle Imaging SpectroRadiometer (MISR). The CERES Single Scanner Footprint (SSF) contains the pixel level CERES TOA fluxes and the level 2 MODIS aerosol data. We specifically focus upon fusing the CERES SSF with the MISR aerosol products. Although this project was undertaken specifically to address aerosol research, the methods employed for fusing data products can be used for other problems requiring synergistic data sets. We present selected case studies over different aerosol regimes and indicate that multisensor information provides value-added information for aerosol research that is not available from a single sensor. [ABSTRACT FROM AUTHOR]

Probabilistic flood mapping offers flood managers, decision makers, insurance agencies, and humanitarian relief organizations a useful characterization of uncertainty in flood mapping delineation. Probabilistic flood maps are also of high interest for data assimilation into numerical models. The direct assimilation of probabilistic flood maps into hydrodynamic models would be beneficial because it would eliminate the intermediate step of having to extract water levels first. This paper introduces a probabilistic flood mapping procedure based on synthetic aperture radar (SAR) data. Given a SAR image of backscatter values, we construct a total histogram of backscatter values and decompose this histogram into probability distribution functions of backscatter values associated with flooded (open water) and non-flooded pixels, respectively. These distributions are then used to estimate, for each pixel, its probability of being flooded. The new approach improves on binary SAR-based flood mapping procedures, which do not inform on the uncertainty in the pixel state. The proposed approach is tested using four SAR images from two floodplains, i.e., the Severn River (U.K.) and the Red River (U.S.). In all four test cases, reliability diagrams, with error values ranging from 0.04 to 0.23, indicate a good agreement between the SAR-derived probabilistic flood map and an independently available validation map, which is obtained from aerial photography. [ABSTRACT FROM PUBLISHER]

Precipitation classification is a critical module in the retrieval algorithm set, for the dual-frequency precipitation radar (DPR) that will be onboard the global precipitation measurement (GPM) core satellite. Precipitation type classification namely stratiform, convective, and other rain type classification is an important part of the classification module. Characteristics of measured dual-frequency ratio ( DFRm), defined as the difference between measured reflectivity at two frequency channels (Ku- and Ka- band), were studied for different rain types. This paper shows that DFRm can be used to separate stratiform and convective rain. In this paper, a precipitation type classification model is developed for DPR profile classification using characteristics of DFRm. Data collected by the airborne PR (ARP-2) in NASA African Monsoon Multidisciplinary Analysis, Genesis and Rapid Intensification Processes, and Wakasa Bay campaigns are employed in model validation. The performance of the precipitation classification method for the GPM-DPR resolution is evaluated and is shown to be applicable to the GPM resolution. [ABSTRACT FROM PUBLISHER]

The authors have recently proposed a model, based on Monte Carlo methods, to simulate light interaction with sand. In this paper, principal component analysis and regression techniques are applied to yield a compact analytical representation of the spectral reflectance signatures produced by the model. This analytical formulation compares well with the original model and is appropriate for applications demanding interactive rates. Examples are provided comparing the original model to the proposed formulation for three hypothetical sand samples. The effect of water content on reflectance is demonstrated for these samples. Additionally, examples are provided comparing the original and proposed models for three sand samples from the U.S. Army Topographic Engineering Center spectral database. [ABSTRACT FROM AUTHOR]

In this paper, laboratory-based bidirectional reflectance distribution function (BRDF) analysis of vegetation leaves, soil, and leaf-litter samples is presented. The leaf litter and soil samples, numbered 1 and 2, were obtained from a site located in the savanna biome of South Africa (Skukuza: 25.0° S, 31.5° E). A third soil sample, number 3, was obtained from Etosha Pan, Namibia (19.20° S, 15.93° E, altitude of 1100 m). In addition, BRDF of local fresh and dry leaves from tulip polar tree (Liriodendron tulipifera) and black locust tree (Robinia pseudoacacia) were studied. It is shown how the BRDF depends on the incident and scatter angles, sample size (i.e., crushed versus whole leaf), soil samples fraction size, sample status (i.e., fresh versus dry leaves), vegetation species (i.e., poplar versus locust), and the vegetation's biochemical composition. As a demonstration of the application of the results of this paper, airborne BRDF measurements acquired with NASA's Cloud Absorption Radiometer over the same general site where the soil and leaf-litter samples were obtained are compared to the laboratory results. Good agreement between laboratory and airborne-measured BRDF is reported. [ABSTRACT FROM AUTHOR]

The Multispectral Thermal Imager (MTI) satellite is a research and development project sponsored by the U.S. Department of Energy. The primary mission is to demonstrate advanced multispectral and thermal imaging from a satellite, Including new technologies, data processing, and analysis techniques and validation by reference to ground truth. The MTI builds on the efforts of a number of earlier efforts, Including Landsat, National Aeronautics and Space Administration remote sensing missions, and others, but the MTI incorporates a unique combination of attributes designed to advance the state of the art. The MTI satellite was launched on March 12,2000 into a 580 km × 610 km, sun-syn-chronous orbit with nominal 1 A.M. and 1 P.M. equatorial crossing times. The Air Force Space Test Program provided the Orbital Sciences Taurus launch. The satellite-based sensors obtain radiance data that are subsequently processed into measurements of atmospheric and surface properties such as column water vapor, atmospheric aerosol loading, surface temperatures, material composition, and others. This paper provides an overview of the MTI research objectives, design, operations, data products, and data processing and analysis. Several other papers provide greater detail on selected topics. [ABSTRACT FROM AUTHOR]

A comprehensive system that is used to invert the geophysical products from microwave measurements has recently been developed. This system, known as the Microwave Integrated Retrieval System (MiRS), ensures that the final solution is consistent with the measurements and, when used as input to the forward operator, fits them to within the instrument noise levels. In the presence of precipitation, this variational algorithm retrieves a set of hydrometeor products consisting of cloud liquid water, ice water, and rain water content profiles. This paper presents the development and assessment of the MiRS rainfall rate that is derived based on a predetermined relationship of the rainfall with these hydrometeor products. Since this relationship relies on the geophysical products retrieved by the MiRS as inputs and not on sensor-dependent parameters, the technique is suitable for all microwave sensors to which the MiRS is applied. This precipitation technique has been designed to facilitate its transition from research to operations when applied to current and future satellite-based sensors. Currently, the MiRS rainfall rate technique has been implemented operationally at the U.S. National Oceanic and Atmospheric Administration (NOAA) for the NOAA-18, NOAA-19, Metop-A Advanced Microwave Sounding Unit, and Microwave Humidity Sensor, as well as for the Defense Meteorological Satellite Program (DMSP)-F16 and DMSP-F18 Special Sensor Microwave Imager/Sounder microwave satellite sensors. For the validation of the MiRS rainfall rate technique, extensive comparisons with state-of-the-art precipitation products derived from rain gauge, ground-based radar, and satellite-based microwave observations are presented for different regions and seasons, and over land and ocean. The MiRS rainfall rate technique is shown to estimate precipitation, with a skill comparable to other satellite-based microwave precipitation algorithms, including the MSPPS, 3B40RT, and MWCOMB, while showing no discontinuities at coasts. This is a relevant result, considering that the MiRS is a system not merely designed to retrieve the rainfall rate but to consistently estimate a comprehensive set of atmospheric and surface parameters from microwave measurements. [ABSTRACT FROM AUTHOR]

As part of the NASA International Polar Year activities, a Ka-band cross-track interferometric synthetic aperture radar (SAR) recently demonstrated high-precision elevation swath mapping capability. This proof-of-concept instrument was achieved by interfacing two Ka-band slotted-waveguide antennas in a cross-track geometry and Ka-band electronics with the Jet Propulsion Laboratory's L-band uninhabited aerial vehicle SAR. Deployed on the NASA Gulfstream III, initial engineering flights in March and April 2009 marked the first airborne demonstration of single-pass cross-track interferometry at Ka-band. Results of a preliminary interferometric assessment indicate height precisions that, for a 3 m \times 3 m posting, range from 30 cm in the near range to 3 m in the far range and greater than 5 km of swath over the urban areas imaged. The engineering flights were followed by a comprehensive campaign to Greenland in May 2009 for ice-surface topography mapping assessment. Toward that end, coordinated flights with the NASA Wallops Airborne Topographic Mapper lidar were conducted in addition to establishing ground calibration sites at both the Summit Station of the National Science Foundation and the Swiss Camp of the Cooperative Institute for Research in the Environmental Sciences. Comparisons of the radar-derived elevation measurements with both in situ and lidar data are planned for a subsequent paper; however, at this stage, a single data example over rugged ice cover produced a swath up to 7 km with the desired height precision as estimated from interferometric correlation data. While a systematic calibration, including assessment and modeling of biases, due to penetration of the electromagnetic waves into the snow cover has not yet been addressed, these initial results indicate that we will exceed our system requirements. [ABSTRACT FROM AUTHOR]

Data merging with interpolation is a method of combining near-coincident satellite observations to provide complete global or regional maps for comparison with models and ground station observations. We investigate various methods and limitations of data merging (or data fusion), with and without interpolation, as a first step toward merging data sets archived in the National Aeronautics and Space Administration Goddard Earth Sciences Data and Information Services Center and made public through the Goddard Interactive Online Visualization and ANalysis Infrastructure (Giovanni) data portals. As a prototype for the data-merging algorithm, this paper uses daily global observations of aerosol optical thickness (AOT), as measured by the MODerate resolution Imaging Spectroradiometer onboard the Terra and Aqua satellites. The goal is to develop a very fast and accurate online method of data merging for implementation into Giovanni. We demonstrate three different methods for pure merging (without interpolation): simple arithmetic averaging (SAA), maximum likelihood estimate (MLE), and weighting by pixel counts. All three methods are roughly comparable, with the MLE (SAA) being slightly preferable when validating with respect to the AOT standard deviations (AOT means). To evaluate the merged product, we introduce two confidence functions, which characterize the percentage of the merged AOT pixels as a function of the relative deviation of the merged AOT from the initial Terra and Aqua AOTs. Eight combinations of merging–interpolation are applied to scenes with regular and irregular data gap patterns. Our results show that the merging–interpolation procedure can produce complete spatial fields with acceptable errors. [ABSTRACT FROM AUTHOR]

From the Landsat program's inception in 1972 to the present, the Earth science user community has been benefiting from a historical record of remotely sensed data. The multispectral data from the Landsat 5 (L5) Thematic Mapper (TM) sensor provide the backbone for this extensive archive. Historically, the radiometric calibration procedure for the L5 TM imagery used the detectors' response to the internal calibrator (IC) on a scene-by-scene basis to determine the gain and offset for each detector. The IC system degraded with time, causing radiometric calibration errors up to 20%. In May 2003, the L5 TM data processed and distributed by the U.S. Geological Survey (USGS) Earth Resources Observation and Science Center through the National Landsat Archive Production System (NLAPS) were updated to use a lifetime lookup-table (LUT) gain model to radiometrically calibrate TM data instead of using scene-specific IC gains. Further modification of the gain model was performed in 2007. The L5 TM data processed using IC prior to the calibration update do not benefit from the recent calibration revisions. A procedure has been developed to give users the ability to recalibrate their existing level-1 products. The best recalibration results are obtained if the work-order report that was included in the original standard data product delivery is available. However, if users do not have the original work-order report, the IC trends can be used for recalibration. The IC trends were generated using the radiometric gain trends recorded in the NLAPS database. This paper provides the details of the recalibration procedure for the following: 1) data processed using IC where users have the work-order file; 2) data processed using IC where users do not have the work-order file; 3) data processed using prelaunch calibration parameters; and 4) data processed using the previous version of the LUT (e.g., LUT03) that was released before April 2, 2007. [ABSTRACT FROM AUTHOR]

This paper presents a new method for NOAA's (National Ocean and Atmospheric Administration) orbital drift correction. This method is pixel-based, and in opposition with most methods previously developed, does not need explicit knowledge of land cover. This method is applied to AVHRR (Advanced Very High Resolution Radiometer) channel information, and relies only on the additional knowledge of solar zenithal angle (SZA) and acquisition date information. In a first step, anomalies in SZA and channel time series are retrieved, and screened out for anomalous values. Then, the part of the parameter anomaly which is explained by SZA anomaly is removed from the data, to estimate new parameter anomalies, and this iteratively until the influence of SZA anomalies is totally removed from the parameter data. This correction has been applied to bimonthly AVHRR data provided by the GIMMS group (Global Inventory Modeling and Mapping Studies), covering Africa from November 2000 to December 2006. NDVI and LST (Land Surface Temperature) have been estimated from raw and corrected data, and averaged over homogeneous vegetation classes. Differences between raw and corrected averaged parameters show an improvement in the quality of the data. In order to validate this method, a whole week (10 to 17 July 2004) of METEOSAT SEVIRI (Spinning Enhanced Visible and InfraRed Imager) data have been used, from which LST have been estimated using a similar method to the one used to retrieve LST from AVHRR data. The comparison between both platforms at the same time of acquisition shows good concordance. [ABSTRACT FROM AUTHOR]

Ice concentration retrieved from spaceborne passive-microwave observations is a prime input to operational sea-ice-monitoring programs, numerical weather prediction models, and global climate models. Atmospheric Environment Service (AES)-York and the Enhanced National Aeronautics and Space Administration Team (NT2) are two algorithms that calculate ice concentration from Special Sensor Microwave/Imager observations. This paper furnishes a comparison between ice concentrations (total, thin, and thick types) output from NT2 and AES-York algorithms against the corresponding estimates from the operational analysis of Radarsat images in the Canadian Ice Service (CIS). A new data fusion technique, which incorporates the actual sensor's footprint, was developed to facilitate this study. Results have shown that the NT2 and AES-York algorithms underestimate total ice concentration by 18.35% and 9.66% concentration counts on average, with 16.8% and 15.35% standard deviation, respectively. However, the retrieved concentrations of thin and thick ice are in much more discrepancy with the operational CIS estimates when either one of these two types dominates the viewing area. This is more likely to occur when the total ice concentration approaches 100 %. If thin and thick ice types coexist in comparable concentrations, the algorithms' estimates agree with CIS's estimates. In terms of ice concentration retrieval, thin ice is more problematic than thick ice. The concept of using a single tie point to represent a thin ice surface is not realistic and provides the largest error source for retrieval accuracy. While AES-York provides total ice concentration in slightly more agreement with CIS's estimates, NT2 provides better agreement in retrieving thin and thick ice concentrations. [ABSTRACT FROM AUTHOR]

The Ozone Monitoring Instrument (OMI) was launched on July 15, 2004 on the National Aeronautics and Space Administration's Earth Observing System Aura satellite. OMI is an ultraviolet-visible imaging spectrograph providing daily global coverage with high spatial resolution. This paper discusses the ground data processing software used for Level 0 to Level lb processing of OMI data. In addition, the OMI operations scenario is described together with the data processing concept. This paper is intended to serve as a reference guide for users of OMI (Level 1b) data. [ABSTRACT FROM AUTHOR]

Transmissions from ground-based systems in C- and X-bands present a significant challenge to the use of these bands for passive microwave remote sensing from aircraft and satellites. Because future missions plan to continue to use these frequencies, it is important to characterize and understand the nature of interference in as much of the candidate spectrum as possible. This paper presents a statistical analysis of interference observed in the continental U.S. using six months of data collected from the C- and X-band channels of the WindSat microwave radiometer. Our findings are consistent with those of previous studies by Li et a!. and Njoku et aL, which are based on data obtained from the Advanced Microwave Scanning Radiometer-EOS using somewhat similar center frequencies and bandwidths. Results show significant radio-frequency interference (RFI) at C-band, including brightnesses in horizontal and vertical polarizations in excess of 330 K, while X-band RFI is less obvious through direct examination of measured linearly polarized brightnesses. Evidence of lower levels of RFI is provided through use of the spectral and polarization indexes of Li et a!., which reveal likely RFI contributions at X-band as well. Further confirmation of X-band RFI is obtained through analysis of the polarimetric channels, which are shown to provide direct evidence of RFI in contrast to the linearly polarized channels. A temporal analysis of the largest C-band RFI sources is also provided in an attempt to further understand their properties. [ABSTRACT FROM AUTHOR]

Radio-frequency interference (RFI) is an increasingly serious problem for passive and active microwave sensing of the Earth. To satisfy their measurement objectives, many spaceborne passive sensors must operate in unprotected bands, and future sensors may also need to operate in unprotected bands. Data from these sensors are likely to be increasingly contaminated by RFI as the spectrum becomes more crowded. In a previous paper we reported on a preliminary investigation of RFI observed over the United States in the 6.9-GHz channels of the Advanced Microwave Scanning Radiometer (AMSR-E) on the Earth Observing System Aqua satellite. Here, we extend the analysis to an investigation of RFI in the 6.9- and 10.7-GHz AMSR-E channels over the global land domain and for a one-year observation period. The spatial and temporal characteristics of the RFI are examined by the use of spectral indices. The observed RI?! at 6.9 GHz is most densely concentrated in the United States, Japan, and the Middle East, and is sparser in Europe, while at 10.7 GHz the RFI is concentrated mostly in England, Italy, and Japan. Classification of RFI using means and standard deviations of the spectral indices is effective in identifying strong RFI. In many cases, however, it is difficult, using these indices, to distinguish weak RFI from natural geophysical variability. Geophysical retrievals using RFI-filtered data may therefore contain residual errors due to weak RFI. More robust radiometer designs and continued efforts to protect spectrum allocations will be needed in future to ensure the viability of spaceborne passive microwave sensing. [ABSTRACT FROM AUTHOR]

In a recent paper, Leroux compared three satellite soil moisture data sets (SMOS, AMSR-E, and ASCAT) and ECMWF forecast soil moisture data to in situ measurements over four watersheds located in the United States. Their conclusions stated that SMOS soil moisture retrievals represent “an improvement [in RMSE] by a factor of 2-3 compared with the other products” and that the ASCAT soil moisture data are “very noisy and unstable.” In this clarification, the analysis of Leroux is repeated using a newer version of the ASCAT data and additional metrics are provided. It is shown that the ASCAT retrievals are skillful, although they show some unexpected behavior during summer for two of the watersheds. It is also noted that the improvement of SMOS by a factor of 2-3 mentioned by Leroux is driven by differences in bias and only applies relative to AMSR-E and the ECWMF data in the now obsolete version investigated by Leroux et al. [ABSTRACT FROM PUBLISHER]

For more than a decade, the U.S. government has been developing laser-based sensors for detecting, locating, and classifying aerosols in the atmosphere at safe standoff ranges. The motivation for this work is the need to discriminate aerosols of biological origin from interferent materials such as smoke and dust using the backscatter from multiple wavelengths in the long wave infrared (LWIR) spectral region. Through previous work, algorithms have been developed for estimating the aerosol spectral dependence and concentration range dependence from these data. The range dependence is required for locating and tracking the aerosol plumes, and the backscatter spectral dependence is used for discrimination by a support vector machine classifier. Substantial progress has been made in these algorithms for the case of a single aerosol present in the lidar line-of-sight (LOS). Often, however, mixtures of aerosols are present along the same LOS overlapped in range and time. Analysis of these mixtures of aerosols presents a difficult inverse problem that cannot be successfully treated by the methods used for single aerosols. Fortunately, recent advances have been made in the analysis of inverse problems using shrinkage-based L1-regularization techniques. Of the several L1-regularization methods currently known, the split Bregman algorithm is straightforward to implement, converges rapidly, and is applicable to a broad range of inverse problems including our aerosol unmixing. In this paper, we show how the split Bregman algorithm can successfully resolve LWIR lidar data containing mixtures of bioaerosol simulants and interferents into their separate components. The individual components then can be classified as bio- or nonbioaerosol by our SVM classifier. We illustrate the approach through data collected in field tests over the past several years using the U.S. Army FAL sensor in testing at Dugway Proving Ground, UT. [ABSTRACT FROM PUBLISHER]

Estimation of soil moisture at large scale has been performed using several satellite-based passive microwave sensors and a variety of retrieval methods over the past two decades. The most recent source of soil moisture is the European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission. A thorough validation must be conducted to insure product quality that will, in turn, support the widespread utilization of the data. This is especially important since SMOS utilizes a new sensor technology and is the first passive L-band system in routine operation. In this paper, we contribute to the validation of SMOS using a set of four in situ soil moisture networks located in the U.S. These ground-based observations are combined with retrievals based on another satellite sensor, the Advanced Microwave Scanning Radiometer (AMSR-E). The watershed sites are highly reliable and address scaling with replicate sampling. Results of the validation analysis indicate that the SMOS soil moisture estimates are approaching the level of performance anticipated, based on comparisons with the in situ data and AMSR-E retrievals. The overall root-mean-square error of the SMOS soil moisture estimates is 0.043 m3/m3 for the watershed networks (ascending). There are bias issues at some sites that need to be addressed, as well as some outlier responses. Additional statistical metrics were also considered. Analyses indicated that active or recent rainfall can contribute to interpretation problems when assessing algorithm performance, which is related to the contributing depth of the satellite sensor. Using a precipitation flag can improve the performance. An investigation of the vegetation optical depth (tau) retrievals provided by the SMOS algorithm indicated that, for the watershed sites, these are not a reliable source of information about the vegetation canopy. The SMOS algorithms will continue to be refined as feedback from validation is evaluated, and it is expected that the SMOS estimates will improve. [ABSTRACT FROM AUTHOR]

A 1-D variational system has been developed to process spaceborne measurements. It is an iterative physical inversion system that finds a consistent geophysical solution to fit all radiometric measurements simultaneously. One of the particularities of the system is its applicability in cloudy and precipitating conditions. Although valid, in principle, for all sensors for which the radiative transfer model applies, it has only been tested for passive microwave sensors to date. The Microwave Integrated Retrieval System (MiRS) inverts the radiative transfer equation by finding radiometrically appropriate profiles of temperature, moisture, liquid cloud, and hydrometeors, as well as the surface emissivity spectrum and skin temperature. The inclusion of the emissivity spectrum in the state vector makes the system applicable globally, with the only differences between land, ocean, sea ice, and snow backgrounds residing in the covariance matrix chosen to spectrally constrain the emissivity. Similarly, the inclusion of the cloud and hydrometeor parameters within the inverted state vector makes the assimilation/inversion of cloudy and rainy radiances possible, and therefore, it provides an all-weather capability to the system. Furthermore, MiRS is highly flexible, and it could be used as a retrieval tool (independent of numerical weather prediction) or as an assimilation system when combined with a forecast field used as a first guess and/or background. In the MiRS, the fundamental products are inverted first and then are interpreted into secondary or derived products such as sea ice concentration, snow water equivalent (based on the retrieved emissivity) rainfall rate, total precipitable water, integrated cloud liquid amount, and ice water path (based on the retrieved atmospheric and hydrometeor products). The MiRS system was implemented operationally at the U.S. National Oceanic and Atmospheric Administration (NOAA) in 2007 for the NOAA-18 satellite. Since then, it has been extended to run for NOAA-19, Metop-A, and DMSP-F16 and F18 SSMI/S. This paper gives an overview of the system and presents brief results of the assessment effort for all fundamental and derived products. [ABSTRACT FROM AUTHOR]

This paper investigates the improvements to the quality of ocean color data using an appropriate choice of ancillary data for National Oceanic and Atmospheric Administration (NOAA) operational ocean color data processing, which requires routine ocean color product production in near real time. The ancillary data, such as the total column ozone amount, sea surface wind speed, atmospheric pressure, and total column water-vapor amount, are required for satellite ocean color data processing for deriving ocean color products, e.g., normalized water-leaving radiance spectra data, chlorophyll-a concentration, water diffuse attenuation coefficient, etc. Currently, NOAA's CoastWatch program uses the climatology ancillary data for the near-real-time ocean color data processing. Alternative ancillary data sets that can replace the climatology data for the near-real-time ocean color data processing have been investigated and studied. The studies were carried out from four selected NOAA CoastWatch regions covering the U.S. coastal and Hawaii regions and for four months (January, April, July, and October), representing the four seasons. Based on the evaluation results, we propose to use the ancillary data produced from the Global Forecast System (GFS) model for the NOAA operational ocean color data processing, as well as for any other near-real-time data processing that requires ancillary data inputs. The effects of using the GFS model data on the accuracy of the derived ocean color products are also investigated and discussed. [ABSTRACT FROM AUTHOR]

A tropospheric ozone Differential Absorption Lidar system, developed jointly by The University of Alabama in Huntsville and the National Aeronautics and Space Administration, is making regular observations of ozone vertical distributions between 1 and 8 km with two receivers under both daytime and nighttime conditions using lasers at 285 and 291 nm. This paper describes the lidar system and analysis technique with some measurement examples. An iterative aerosol correction procedure reduces the retrieval error arising from differential aerosol backscatter in the lower troposphere. Lidar observations with coincident ozonesonde flights demonstrate that the retrieval accuracy ranges from better than 10% below 4 km to better than 20% below 8 km with 750-m vertical resolution and 10-min temporal integration. [ABSTRACT FROM AUTHOR]

Validation is an important and particularly challenging task for remote sensing of soil moisture. A key issue in the validation of soil moisture products is the disparity in spatial scales between satellite and in situ observations. Conventional measurements of soil moisture are made at a point, whereas satellite sensors provide an integrated area/volume value for a much larger spatial extent. In this paper, four soil moisture networks were developed and used as part of the Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E) validation program. Each network is located in a different climatic region of the U.S., and provides estimates of the average soil moisture over highly instrumented experimental watersheds and surrounding areas that approximate the size of the AMSR-E footprint. Soil moisture measurements have been made at these validation sites on a continuous basis since 2002, which provided a seven-year period of record for this analysis. The National Aeronautics and Space Administration (NASA) and Japan Aerospace Exploration Agency (JAXA) standard soil moisture products were compared to the network observations, along with two alternative soil moisture products developed using the single-channel algorithm (SCA) and the land parameter retrieval model (LPRM). The metric used for validation is the root-mean-square error (rmse) of the soil moisture estimate as compared to the in situ data. The mission requirement for accuracy defined by the space agencies is 0.06 \m^3/\m^3. The statistical results indicate that each algorithm performs differently at each site. Neither the NASA nor the JAXA standard products provide reliable estimates for all the conditions represented by the four watershed sites. The JAXA algorithm performs better than the NASA algorithm under light-vegetation conditions, but the NASA algorithm is more reliable for moderate vegetation. However, both algorithms have a moderate to large bias in all cases. The SCA had the lowest overall rmse with a small bias. The LPRM had a very large overestimation bias and retrieval errors. When site-specific corrections were applied, all algorithms had approximately the same error level and correlation. These results clearly show that there is much room for improvement in the algorithms currently in use by JAXA and NASA. They also illustrate the potential pitfalls in using the products without a careful evaluation. [ABSTRACT FROM AUTHOR]

Physically based passive microwave precipitation retrievals are difficult to develop over land because high nonuniform land emissivity values are difficult to distinguish from those of clouds. This paper uses an empirical approach to determine the covariance of emissivity at different microwave window channels and relies on this covariance to estimate the portion of the observed brightness temperatures that may be attributable to rainfall. One year (2006) of global cloud-free surface emissivity values were retrieved using data sets from multiple instruments on NASA's Aqua satellite. Correlations between the emissivities at different channels were developed for use in an empirical model within an optimal estimation retrieval. The optimal estimation retrieves surface temperature, total column water vapor, cloud water, and the emissivity at the 10.7-GHz horizontally polarized channel. From this retrieval and the covariance of emissivities, the 89.0-GHz brightness temperature at both polarizations can be estimated. Significant differences between the observed and retrieved high-resolution brightness temperatures are used to screen for precipitation, and results are compared to ground-based radar data for several study regions representing a variety of land surface types in the U.S. The Heidke Skill Score is used to determine the robustness of this methodology and, in all cases, demonstrates at least some increase in skill relative to random chance. [ABSTRACT FROM AUTHOR]

Global Positioning System (GPS) radio occultation (RO) sounding, with its high vertical resolution temperature and humidity profiling capability, is revolutionizing atmospheric science, particularly through assimilation in numerical weather prediction (NWP) models. Currently, the observations are derived from GPS receivers onboard low Earth orbiting satellites. However, with the current number of satellites, it is difficult to provide dense sounding measurements in a specific region within a limited time period. With a GPS receiver onboard an airplane, the GPS RO technique offers such an opportunity while retaining the high vertical resolution sounding capability. The GNSS Instrument System for Multistatic and Occultation Sensing is currently under development for the National Science Foundation's High-performance Instrumented Airborne Platform for Environmental Research (HIAPER) aircraft. This paper presents a sensitivity analysis of the airborne occultation technique that will be used for the HIAPER system. The results demonstrate an anticipated overall accuracy of better than 0.5% for the retrieved refractivity from the surface to about 1 km below the airplane, where the expected airplane velocity errors of up to 5 mm/s limit the accuracy. The effects on the retrievals due to horizontal variations in atmospheric refractivity are significant, and retrieval errors may reach several percent inside frontal systems when the front is perpendicular to the ray paths and within 200 km of the tangent point. In general, the airborne GPS RO system provides a promising new data source for NWP and targeted observational studies. [ABSTRACT FROM AUTHOR]

The seasonality of forest leaf area index and photosynthetic activity is an important element of ecosystem function. The ecohydrological models representing water and carbon fluxes in tropical forests need regional-scale spatial information on forest leaf phenology. We aimed to evaluate the utility of the National Oceanic and Atmospheric Administration (NOAA)/Advanced Very High Resolution Radiometer (AVHRR) imagery for monitoring evergreen and deciduous forest phenology in lowland Cambodia. The temporal sequences of the normalized difference vegetation index (NDVI) data were constructed from ten-day composite imagery acquired from May 2001 to April 2002. A local maximum fitting (LMF) technique that combined time-series filtering and local curve-fitting techniques was used to extract cloud-free data. With this approach, different phenology between evergreen and deciduous forests was clearly observed during the dry season: Deciduous forests exhibited marked and spatially uniform loss and (later) gain in new leaf area, whereas evergreen forests showed lesser and spatially/temporally heterogeneous changes in leaf phenology. A significant finding was that about 30% of evergreen forests shed detectable quantities of leaves at two times during a dry season (i.e., in the early, and late, dry season). In summary, the capability of composite NOAA/AVHRR NDVI data sets processed with the LMF technique was established as a viable approach for monitoring heterogeneous forest phenology at regional scales during the tropical dry season, although it is apparent that further improvements in cloud clearing are required if the approach is to be useful also in the wet season. [ABSTRACT FROM AUTHOR]

Measurements of water vapor and clouds in the polar regions are difficult, because conventional instruments show little sensitivity (∼1.3 K/mm) to low amounts. On the other hand, millimeter- and submillimeter-wavelength radiometry offers greatly enhanced sensitivity (up to 51.4 K/mm, depending upon frequency). For this reason, the National Oceanic and Atmospheric Administration's Physical Science Division designed a new instrument, the Ground-Based Scanning Radiometer (GSR), for continuous and unattended observations at millimeter and submillimeter wavelengths (50-380 GHz). The GSR was deployed for the first time during the Arctic winter radiometric experiment in March-April 2004. In this paper, we discuss the GSR calibration procedure, which allows for accurate measurements during clear and cloudy skies. Error-budget analysis and comparison with independent measurements show an absolute accuracy on the order of 1-2 K. Examples of multifrequency and multiangle GSR observations are illustrated, representing a valuable new data set for the study of water vapor, clouds, and atmospheric absorption models in the Arctic. [ABSTRACT FROM AUTHOR]

Mapping shallow-water bathymetry with acoustic techniques is complicated and expensive. The environmental parameters in shallow-water (< 2 m) areas become more variable and greatly impact the depth extraction from the survey measurements. Current airborne light detection and ranging (LIDAR) bathymetry surveying in shallow-water depths uses green-channel waveforms to measure the water depth. Unfortunately, due to difficulties in distinguishing between the surface and bottom return of the water column, the timing of the bottom return is often ambiguous. Furthermore, the water often becomes optically ‘dirty’ due to turbulence at these shallow depths. Therefore, it is common to find coastal areas that lack any measured depths. This paper presents a novel approach for measuring water depths in these shallow coastal waters with airborne LIDAR. Observations of the red-channel waveforms show that the waveforms are divided into two groups, namely: 1) waveforms in deep waters (> 2 m) whose shape is invariant with respect to the water depth and 2) waveforms in shallow-water depths that show a change in shape as a function of the depth in the water column. The data for this study are from the US Geological Survey LIDAR surveys of Lake Tahoe, CA, and Lake Michigan, using a SHOALS-400 LIDAR system. [ABSTRACT FROM AUTHOR]

The Moderate Resolution Imaging Spectroradiometer (MODIS) protoflight model onboard the National Aeronautics and Space Administration's Earth Observing System Terra spacecraft has been in operation for over five years since its launch in December 1999. It makes measurements using 36 spectral bands with wavelengths from 0.41 to 14.5 μm. Bands 1–19 and 26 with wavelengths below 2.2 μm, the reflective solar bands (RSBs), collect daytime reflected solar radiance at three nadir spatial resolutions: 0.25 km (bands 1–2), 0.5 km (bands 3–7), and 1 km (bands 8–19 and 26). Bands 20–25 and 27–36, the thermal emissive bands, collect both daytime and nighttime thermal emissions, at 1-km nadir spatial resolution. The MODIS spectral characterization was performed prelaunch at the system level. One of the MODIS onboard calibrators, the Spectroradiometric Calibration Assembly (SRCA), was designed to perform on-orbit spectral characterization of the MODIS RSB. This paper provides a brief overview of MODIS prelaunch spectral characterization, but focuses primarily on the algorithms and results of using the SRCA for on-orbit spectral characterization. Discussions are provided on the RSB center wavelength measurements and their relative spectral response retrievals, comparisons of on-orbit results with those from prelaunch measurements, and the dependence of center wavelength shifts on instrument temperature. For Terra MODIS, the center wavelength shifts over the past five years are less than 0.5 nm for most RSBs, indicating excellent stability of the instrument's spectral characteristics. Similar spectral performance has also been obtained from the Aqua MODIS (launched in May 2002) SRCA measurements. [ABSTRACT FROM AUTHOR]

The Tropospheric Emission Spectrometer (TES) is one of four instruments onboard the Earth Observing System Aura satellite. The TES Science Investigator-led Processing System (SIPS) Facility performs production processing of all TES science data. When the TES project was proposed in 1988, its science algorithms were still evolving, but it was already understood to be at least two orders of magnitude more complex than its NASA predecessor, the Atmospheric Trace Molecule Spectroscopy (ATMOS) instrument. In addition, the expected data volume of the TES instrument would be more than 1000 times greater than that of ATMOS. Development of the TES SIPS faced a number of technical challenges. It also would have been impractical and prohibitively costly to develop the TES SIPS facility without carefully deploying the computing technologies that have recently become available. This paper describes how the challenges were met in the development of the facility by making use of evolving hardware technology and software refinement. The process revealed that the architecture of the hardware implemented was highly dependent upon the processing algorithm, and a stable algorithm was needed early in the hardware development process for performance analysis and benchmarking. [ABSTRACT FROM AUTHOR]

The Earth Observing System (EOS) Microwave Limb Sounder (MLS) is an atmospheric remote sensing experiment led by the Jet Propulsion Laboratory of the California Institute of Technology. The objectives of the EOS MLS are to learn more about stratospheric chemistry and causes of ozone changes, processes affecting climate variability, and pollution in the upper troposphere. The EOS MLS is one of four instruments on the National Aeronautics and Space Administration (NASA) EOS Aura spacecraft launched on July 15, 2004, with an operational period extending at least 5 years after launch. This paper describes the architecture and capabilities of the Science Data Processing System (SDPS) for the EOS MLS. The SDPS consists of two major component—the Science Computing Facility and the Science Investigator-led Processing System. The Science Computing Facility provides the facilities for the EOS MLS Science Team to perform the functions of scientific algorithm development, processing software development, quality control of data products, and scientific analyses. The Science Investigator-led Processing System processes and reprocesses the science data for the entire mission and delivers the data products to the Science Computing Facility and to the Goddard Space Flight Center Earth Science Distributed Active Archive Center, which archives and distributes the standard science products. The Science Investigator-led Processing System is developed and operated by Raytheon Information Technology and Scientific Services of Pasadena under contract with Jet Propulsion Laboratory. [ABSTRACT FROM AUTHOR]

The Ozone Monitoring Instrument (OMI) on EOS/Aura offers unprecedented spatial and spectral resolution, coupled with global coverage, for space-based UV measurements of sulfur dioxide (SO2). This paper describes an OMI SO2 algorithm (the band residual difference) that uses calibrated residuals at SO2 absorption band centers produced by the NASA operational ozone algorithm (OMTO3). By using optimum wavelengths for retrieval of SO2, the retrieval sensitivity is improved over NASA predecessor Total Ozone Mapping Spectrometer (TOMS) by factors of 10 to 20, depending on location. The ground footprint of OMI is eight times smaller than TOMS. These factors produce two orders of magnitude improvement in the minimum detectable mass of SO2. Thus, the diffuse boundaries of volcanic clouds can be imaged better and the clouds can be tracked longer. More significantly, the improved sensitivity now permits daily global measurement of passive volcanic degassing of SO2 and of heavy anthropogenic SO2 pollution to provide new information on the relative importance of these sources for climate studies. [ABSTRACT FROM AUTHOR]

The Ozone Monitoring Instrument (OMI) was launched on July 15, 2004 on the National Aeronautics and Space Administration's Earth Observing System Aura satellite. The OMI instrument is an ultraviolet-visible imaging spectrograph that uses two-dimensional charge-coupled device detectors to register both the spectrum and the swath perpendicular to the flight direction with a 115° wide swath, which enables global daily ground coverage with high spatial resolution. This paper presents the OMI design and discusses the main performance and calibration features and results. [ABSTRACT FROM AUTHOR]

The Ozone Monitoring Instrument (OMI) flies on NASA's Earth Observing System AURA satellite, launched in July 2004. OMI is an ultraviolet/visible (UV/VIS) nadir solar backscatter spectrometer, which provides nearly global coverage in one day, with a spatial resolution of 13 km × 24 km. Trace gases measured include O3, NO2, SO2, HCHO, BrO, and OCIO. In addition OMI measures aerosol characteristics, cloud top heights and cloud coverage, and UV irradiance at the surface. OMI's unique capabilities for measuring important trace gases with daily global coverage and a small footprint will make a major contribution to our understanding of stratospheric and tropospheric chemistry and climate change along with Aura's other three instruments. OMI's high spatial resolution enables detection of air pollution at urban scales. Total Ozone Mapping Spectrometer and differential optical absorption spectroscopy heritage algorithms, as well as new ones developed by the international (Dutch, Finnish, and U.S.) OMI science team, are used to derive OMI's advanced backscatter data products. In addition to providing data for Aura's prime objectives, OMI will provide near-real-time data for operational agencies in Europe and the U.S. Examples of OMI's unique capabilities are presented in this paper. [ABSTRACT FROM AUTHOR]

This paper describes the optics design and field-of-view (FOV) calibration for five radiometers covering 114–660 GHz which share a common antenna in the Microwave Limb Sounder instrument on the National Aeronautics and Space Administration's Aura satellite. Details of near-field pattern measurements are presented. Estimated systematic scaling uncertainties (3σ) on calibrated limb emissions, due to FOV calibration uncertainties, are below 0.4%. 3σ uncertainties in beamwidth and relative pointing of radiometer boresights are 0.006° and 0.003°, respectively. The uncertainty in modeled instrument response, due to the scan dependence of FOV patterns, is less than ±0.24 K equivalent blackbody temperature. Refinements to the calibration using in-flight data are presented. [ABSTRACT FROM AUTHOR]

This paper describes radiometric performance and prelaunch radiometric and spectral calibrations of the GHz component of the Microwave Limb Sounder (MLS) experiment on NASA's Aura spacecraft. Estimated systematic scaling uncertainties (3σ) on limb port radiances are ∼0.5% from radiometric calibration and ∼0.5% to ∼1% from spectral calibrations. Operational noise performance is consistent with prelaunch expectations, and in-orbit measurements to date indicate no changes in noise characteristics, and no observable calibration drifts. Spectral baseline has remained stable to ∼20 mK since launch. Refinements to calibrations based on in-flight data are discussed, and radiometric calibration algorithms are described. [ABSTRACT FROM AUTHOR]

The Ozone Monitoring Instrument (OMI) flies on the National Aeronautics and Space Adminsitration's Earth Observing System Aura satellite launched in July 2004. OMI is a ultraviolet/visible (UV/VIS) nadir solar backscatter spectrometer, which provides nearly global coverage in one day with a spatial resolution of 13 km × 24 km. Trace gases measured include O3, NO2, SO2, HCHO, BrO, and OClO. In addition, OMI will measure aerosol characteristics, cloud top heights, and UV irradiance at the surface. OMI's unique capabilities for measuring important trace gases with a small footprint and daily global coverage will be a major contribution to our understanding of stratospheric and tropospheric chemistry and climate change. OMI's high spatial resolution is unprecedented and will enable detection of air pollution on urban scale resolution. In this paper, the instrument and its performance will be discussed. [ABSTRACT FROM AUTHOR]

Aura, the last of the large Earth Observing System observatories, was launched on July 15, 2004. Aura is designed to make comprehensive stratospheric and tropospheric composition measurements from its four instruments, the High Resolution Dynamics Limb Sounder (HIRDLS), the Microwave Limb Sounder (MLS), the Ozone Monitoring Instrument (OMI), and the Tropospheric Emission Spectrometer (TES). With the exception of HIRDLS, all of the instruments are performing as expected, and HIRDLS will likely be able to deliver most of their planned data products. We summarize the mission, instruments, and synergies in this paper. [ABSTRACT FROM AUTHOR]

Technology for the 14- and 35-GHz Second-Generation Precipitation Radar (PR-2) is currently being developed by the National Aeronautics and Space Administration Jet Propulsion Laboratory to support the development of future spaceborne radar missions. PR-2 will rely on high-performance onboard processing techniques in order to improve the observation capabilities (swath width, spatial resolution, and precision) of a low-earth orbiting rainfall radar. Using field-programmable gate arrays (FPGAs), we have developed a prototype spaceborne processor and controller module that will support advanced capabilities in the PR-2 such as autotargeting of rain and compression of rainfall science data. In this paper, we describe the new technology components designed for the onboard processor, including an FPGA-based 40 × 109 op/s pulse-compression receiver/filter with a range sidelobe performance of -72 dB, and an adaptive scanning controller which yields a six-fold increase in the number of radar looks over areas of precipitation. [ABSTRACT FROM AUTHOR]

This paper studies an uneven two-class unsupervised classification problem of satellite imagery, i.e., the mapping of U.S. Department of Agriculture's (USDA) Conservation Reserve Pro- gram (CRP) tracts. CRP is a nationwide program that encourages farmers to plant long-term, resource conserving coven to improve soil, water, and wildlife resources. With recent payments of nearly US $1.6 billion for new enrollments (2002 signup), it is imperative to obtain accurate digital CRP maps for management and evaluation purposes. CRP mapping is a complex classification problem where both CRP and non-CRP areas are composed of various cover types. Two nonparametric machine learning approaches, i.e., decision tree classifier (DTC) and support vector machine (SVM5) are implemented in this work. Specifically, considering the importance of CRP classification sensitivity, a new DTC pruning method is proposed to increase recall. We also study two SVM relaxation approaches to increase recall. Moreover, a localized and parallel framework is suggested in order to efficiently deal with the large-scale CRP mapping need. Simulation results validate the applicability of the suggested framework and proposed techniques. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Deals with a study that described an ultrawideband, imaging radar (UWBIR) designed and constructed under the Mine Surface Effects (MISE) program in North Carolina. Discussion on the system and its associated signal processing; MISE deployment of the UWBIR.

Statistics of radio frequency interference (RFI) observed in the band 1398–1422 MHz during an airborne campaign in the United States are reported for use in analysis and forecasting of L-band RFI for microwave radiometry. The observations were conducted from September to October 2008, and included approximately 92 h of flight time, of which approximately 20 h of “transit” or dedicated RFI observing flights are used in compiling the statistics presented. The observations used include outbound and return flights from Colorado to Maryland, as well as RFI surveys over large cities. The Passive Active L-Band Sensor (PALS) radiometer of NASA Jet Propulsion Laboratory augmented by three dedicated RFI observing systems was used in these observations. The complete system as well as the associated RFI characterization approaches are described, along with the resulting RFI statistical information and examinations of specific RFI sources. The results show that RFI in the protected L-band spectrum is common over North America, although the resulting interference when extrapolated to satellite observations will appear as “low-level” corruption that will be difficult to detect for traditional radiometer systems. [ABSTRACT FROM AUTHOR]

A microwave land emissivity library has been developed from the Advanced Microwave Sounding Unit (AMSU) data for improving satellite data assimilation. Over the desert, surface emissivity is classified according to soil type into several spectra. For sand, loamy sand, and sandy loam, which contain some large mineral particles, the emissivity spectra generally decrease with frequency. For other desert types whose compositions are dominated by mineral particles smaller than a few hundred micrometers, the emissivity values are almost constant or slightly increasing with frequency. These emissivity features are consistent with those from the land emissivity data set developed at Météo-France. Moreover, both the emissivity library and the Météo-France data set are applied to the assimilation of the AMSU-A data in the National Centers for Environmental Prediction Global Forecast System (GFS). In comparison with the microwave land emissivity model previously developed by Weng , both the emissivity library and the Météo-France data set improve the utilization of the AMSU-A data in the GFS. The increased use of the AMSU-A data through the emissivity library or the data set results in positive impacts on the global medium-range forecasts over either the Southern or Northern Hemispheres. [ABSTRACT FROM AUTHOR]

To evaluate sea ice concentrations (SICs) from the Special Sensor Microwave/Imager (SSM/I) and Advanced Microwave Scanning Radiometer-EOS (AMSR-E), we observed sea ice with the 6-m-resolution panchromatic electronic optical camera (EOC) sensor onboard the Korea Multi-Purpose Satellite-1 (KOMPSAT-1). A total of 68 cloud-free EOC images were obtained across the Antarctic continental edges from September to November 2005. Sea ice types in the EOC images were classified into white ice (W), gray ice (G), and dark-gray ice (D) and then compared with SSM/I and AMSR-E SICs. Spatiotemporal standard deviation of passive microwave SIC proved useful in selecting temporally stable and spatially homogeneous SICs to overcome the diurnal variation of sea ice in the analysis of data from multiple satellites. In the Antarctic spring, the EOC SIC of W + G showed the best fit to SSM/I SIC calculated by the NASA Team (NT) algorithm (mean difference of -2.3% and rmse of 3.2%), whereas that of W + G + D showed the best fit to AMSR-E SIC calculated by the NT2 algorithm (mean difference of 0.3% and rmse of 1.4%). It is concluded that the SSM/I NT algorithm responds to young ice in addition to the ice types A and B, whereas the AMSR-E NT2 algorithm detects ice type C and thin ice as well. The 4.7% difference of SICs between AMSR-E and SSM/I was attributed to the enhanced detection of ice type C (2.1%) and thin ice (2.6%) of the AMSR-E NT2 algorithm. [ABSTRACT FROM AUTHOR]

Multispectral thermal imagery acquired from low Earth orbit was used to develop a method of cloud-height determination that applies image brightness temperature histograms and region-of-interest (ROI) image segmentation as a processing step prior to stereo-height retrieval. The National Aeronautics and Space Administration's Infrared Spectral Imaging Radiometer (ISIR) acquired all imagery during the August 1997 STS-85 mission of the Space Shuttle Discovery. ISIR, the first Earth-observing spectroradiometer to employ an uncooled large-format microbolometer-array focal plane, provided continuous coverage in four spectral bands along track in an 80-km-wide swath. ROI segmentation created binary cloud masks ranging over brightness temperatures in the imagery for which the parallax was determined by a two-dimensional correlation method, allowing stereo heights to be determined using the standard parallax equations. A subpixel parallax algorithm allowed stereo heights to be determined with a precision of roughly ±0.39 km for clouds in the observed altitude range of 0.5–10 km. [ABSTRACT FROM AUTHOR]

A credit assignment approach to decision-based classifier fusion is developed and applied to the problem-of land-cover classification from multiseason airborne hyperspectral imagery. For each input sample, the new method uses a smoothed estimated reliability measure (SERM) in the output domain of the classifiers. SERM requires no additional training beyond that needed to optimize the constituent classifiers in the pool, and its generalization (test) accuracy exceeds that of a number of other extant methods for classifier fusion. Hyperspectral imagery from HyMAP and PROBE2 acquired at three points in the growing season over Smith Island, VA, a barrier island in the Nature Conservancy's Virginia Coast Reserve, serves as the basis for comparing SERM with other approaches. [ABSTRACT FROM AUTHOR]