A microwave technique for mapping ice temperature in the Arcticseasonal sea ice zone

A technique for deriving ice temperature in the Arctic seasonal sea ice zone from passive microwave radiances has been developed. The algorithm operates on brightness temperatures derived from the Special Sensor Microwave/Imager (SSM/I) and uses ice concentration and type from a previously developed thin ice algorithm to estimate the surface emissivity. Comparisons of the microwave derived temperatures with estimates derived from infrared imagery of the Bering Strait yield a correlation coefficient of 0.93 and an RMS difference of 2.1 K when coastal and cloud contaminated pixels are removed. SSM/I temperatures were also compared with a time series of air temperature observation from Gambell on St. Lawrence Island and from Point Barrow, AK weather stations. These comparisons indicate that the relationship between the air temperature and the ice temperature depends on ice type     

Evaluation of SSM/I and AMSR-E Sea Ice Concentrations in the Antarctic Spring Using KOMPSAT-1 EOC Images

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.     

Fusion of satellite active and passive microwave data for sea icetype concentration estimates

Young first-year sea ice is nearly as important as open water in modulating heat flux between the ocean and atmosphere in the Arctic. Just after the onset of freeze-up, first-year ice is in the early stages of growth and will consist of young first-year and thin ice. The distribution of sea ice in this thickness range impacts heat transfer in the Arctic. Therefore, improving the estimates of ice concentrations in this thickness range is significant. The NASA Team Algorithm (NTA) for passive microwave data inaccurately classifies sea ice during the melt and freeze-up seasons because it misclassifies multiyear ice as first-year ice. We developed a hybrid fusion technique for incorporating multiyear ice information derived from synthetic aperture radar (SAR) images into a passive microwave algorithm to improve ice type concentration estimates. First, we classified SAR images using a dynamic thresholding technique and estimated the multiyear ice concentration. Then we used the SAR-derived multiyear ice concentration to constrain the NTA and obtained an improved first-year ice concentration estimate. We computed multiyear and first-year ice concentration estimates over a region in the eastern-central Arctic in which field observations of ice and in situ radar backscatter measurements were performed. The fused estimates of first-year and multiyear ice concentration appear to be more accurate than NTA, based on ice observations that were logged aboard the US Coast Guard Icebreaker Polar Star in the study area during 1991     

Comparison of passive microwave ice concentration algorithm retrievals with AVHRR imagery in arctic peripheral seas

An accurate representation of sea ice concentration is valuable to operational ice analyses, process studies, model inputs, and detection of long-term climate change. Passive microwave imagery, such as from the Special Sensor Microwave/Imager (SSM/I), are particularly valuable for monitoring of sea ice conditions because of their daily, basin-scale coverage under all sky conditions. SSM/I-derived sea ice concentration estimates using four common algorithms [Bootstrap (BT), Cal/Val (CV), NASA Team (NT), and NASA Team 2 (N2)] are compared with concentrations computed from Advanced Very High Resolution Radiometer (AVHRR) visible and infrared imagery. Comparisons are made over approximately an eight-month period in three regions of the Arctic and focus on areas near the ice edge where differences between the algorithms are likely to be most apparent. The results indicate that CV and N2 have the smallest mean error relative to AVHRR. CV tends to overestimate concentration, while the other three algorithms underestimate concentration. NT has the largest underestimation of nearly 10% on average and much higher in some instances. In most cases, mean errors of the SSM/I algorithm were significantly different from each other at the 95% significance level. The BT algorithm has the lowest error standard deviation, but none of the considered algorithms was found to have statistically significantly different error standard deviations in most cases. This indicates that spatial resolution is likely a limiting factor of SSM/I in regions near the ice edge in that none of the algorithms satisfactorily resolve mixed pixels. Statistical breakdowns by season, region, ice conditions, and AVHRR scene generally agree with the overall results. Representative case studies are presented to illustrate the statistical results.     

Validation of sea ice motion from QuikSCAT with those from SSM/I and buoy

Arctic sea ice motion for the period from October 1999 to March 2000 derived from QuikSCAT and ocean buoy observations.Special Sensor Microwave/Imager (SSM/I) data using the wavelet analysis method agrees well with ocean buoy observations. Results from QuikSCAT and SSM/I are compatible when compared with buoy observations and complement each other. Sea ice drift merged from daily results from QuikSCAT, SSM/I, and buoy data gives more complete coverage of sea ice motion. Based on observations of six months of sea ice motion maps, the sea ice motion maps in the Arctic derived from QuikSCAT data appear to have smoother (less noisy) patterns than those from NSCAT, especially in boundary areas, possibly due to constant radar scanning incidence angle. For late summer, QuikSCAT data can provide good sea ice motion information in the Arctic as early as the beginning of September. For early summer, QuikSCAT can provide at least partial sea ice motion information until mid-June. In the Antarctic, a case study shows that sea ice motion derived from QuikSCAT data is consistent with pressure field contours.     

Automated delineation of dry and melt snow zones in Antarctica using active and passive microwave observations from space

This paper presents the algorithms and analysis results for delineating snow zones using active and passive microwave satellite remote sensing data. With a high-resolution Radarsat synthetic aperture radar (SAR) image mosaic, dry snow zones, percolation zones, wet snow zones, and blue ice patches for the Antarctic continent have been successfully delineated. A competing region growing and merging algorithm is used to initially segment the SAR images into a series of homogeneous regions. Based on the backscatter characteristics and texture property, these image regions are classified into different snow zones. The higher level of knowledge about the areal size of and adjacency relationship between snow zones is incorporated into the algorithms to correct classification errors caused by the SAR image noise and relief-induced radiometric distortions. Mathematical morphology operations and a line-tracing algorithm are designed to extract a vector line representation of snow-zone boundaries. With the daily passive microwave Special Sensor Microwave/Imager (SSM/I) data, dry and melt snow zones were derived using a multiscale wavelet-transform-based method. The analysis results respectively derived from Radarsat SAR and SSM/I data were compared and correlated. The complementary nature and comparative advantages of frequently repeated passive microwave data and spatially detailed radar imagery for detecting and characterizing snow zones were demonstrated.     

Arctic Sea Ice Type and Concentration Mapping Using Passive and Active Microwave Sensors

The mapping of ice type concentrations in the Arctic is important for commercial operations and for climate-related research. Algorithms based on moderate-resolution passive microwave sensors for mapping first-year ice and multiyear ice concentrations suffer from a number of known problems. In this paper, it has been shown that QuikSCAT scatterometer data can add complimentary information to that available from passive microwave, which can assist in separating different ice classes. Specifically, we identify a class of ice that exhibits a passive microwave signature which is characteristic of first-year ice, but has a scatterometer signature which is typical of multiyear ice. We track the evolution and distribution of this new ice class throughout the Arctic during the winter season of 2003-2004 and compare the results against the U.S. National Ice Center (NIC) ice charts. It has been found that the new ice class is predominantly multiyear ice and is especially prevalent in the Fram Strait and the high Arctic regions north of the islands Franz Josef Land and Severnaya Zemlya. A simple algorithm has been proposed that enables a passive microwave-based partial ice concentration algorithm (for example, the NT algorithm based on Special Sensor Microwave/Imager data) to be adapted using QuikSCAT scatterometer data, so that the new ice class is corrected from the first-year ice class to the multiyear ice category. The algorithm performance is measured against the NIC ice charts. We provide a discussion regarding the possible physical causes of the effects that have been observed and described     

The potential of combining SSM/I and SSM/T2 measurements to improvethe identification of snowcover and precipitation

The potential of passive microwave radiometry for classifying snowcover and precipitation using measurements from the Special Sensor Microwave/Imager (SSM/I) and the Special Sensor Microwave Water Vapor Profiler (SSM/T2) is investigated by modelling the radiative transfer for different surface types and atmospheric conditions. The model accounts for various land surfaces and vegetation covers, different snow types as well as wind roughened ocean water. The atmospheric part includes multiple scattering and depolarization by cloud droplets and precipitating water as well as ice spheres. It was found, that the combination of a window channel (91 GHz) and an atmospheric sounding channel (183±7 GHz) can improve the separation of snowcover and precipitation which is difficult by using only SSM/I channels. The 183±7 GHz channel is strongly influenced by the water vapor distribution which makes its use difficult for warm rain cases and low cloud tops. Then, the signature at this frequency is not unique and the above relation gives no further improvement of the classification. However, the identification of rainfall over cold land backgrounds can be significantly improved, which is illustrated by the application of a combined SSM/I-SSM/T2 algorithm to two satellite datasets when compared to the SSM/I algorithm and to operational surface weather maps     

Automatic detection and validity of the sea-ice edge: an application of enhanced-resolution QuikScat/SeaWinds data

Sea-ice edge detection is an essential task at the different national ice services to secure navigation in ice-covered seas. Comparison between the Remund and Long ice mask image from enhanced-resolution QuikScat/SeaWinds (QS) products and the analyzed ice edge from high-resolution RADARSAT synthetic aperture radar has shown that the automatically determined QS ice mask underestimates the Arctic ice extent. QS data was statistically analyzed by colocating the data with ice charts around Greenland and with the National Aeronautics and Space Administration Team's Special Sensor Microwave/Imager (SSM/I) ice concentration algorithm over the whole Arctic region. All variables, i.e., the backscatter in vertical and horizontal polarization, the active polarization ratio (APR) and the daily standard deviation, are sensitive to ice types and are strongly correlated with ice concentration when the relationship is expressed in exponential form. Our study showed that the APR is especially suitable for ice-ocean separation, and a threshold of -0.02 was determined. An ice edge algorithm based on this APR threshold was developed using the other variables with conservative season-dependent thresholds to eliminate additional ocean noise. Also, the history of the ice cover is considered in order to detect single ice fields that are separated from the main Arctic pack ice. Validation with RADARSAT 1 and with the Advanced Very High Resolution Radiometer showed that the new algorithm successfully detects very low ice concentrations of about 10% during the entire year. The validity of the detected ice edge for near-real-time issues is also discussed in relation to the ice motion in the Marginal Ice Zone and the integration time necessary to produce the enhanced-resolution images. The new algorithm improves the automatic global ice edge resolution by a factor of two when compared to SSM/I products and could be used in both model initialization and data assimilation.     

Integrating in situ and multiscale passive microwave data for estimation of subgrid scale snow water equivalent distribution and variability

New multiscale research datasets were acquired in central Saskatchewan, Canada during February 2003 to quantify the effect of spatially heterogeneous land cover and snowpack properties on passive microwave snow water equivalent (SWE) retrievals. Microwave brightness temperature data at various spatial resolutions were acquired from tower and airborne microwave radiometers, complemented by spaceborne Special Sensor Microwave/Imager (SSM/I) data for a 25/spl times/25 km study area centered on the Old Jack Pine tower in the Boreal Ecosystem Research and Monitoring Sites (BERMS). To best address scaling issues, the airborne data were acquired over an intensively spaced grid of north-south and east-west oriented flight lines. A coincident ground sampling program characterized in situ snow cover for all representative land cover types found in the study area. A suite of micrometeorological data from seven sites within the study area was acquired to aid interpretation of the passive microwave brightness temperatures. The in situ data were used to determine variability in SWE, snow depth, and density within and between forest stands and land cover types within the 25/spl times/25 km SSM/I grid cell. Statistically significant subgrid scale SWE variability in this mixed forest environment was controlled by variations in snow depth, not density. Spaceborne passive microwave SWE retrievals derived using the Meteorological Service of Canada land cover sensitive algorithm suite were near the center of the normally distributed in situ measurements, providing a reasonable estimate of the mean grid cell SWE. A realistic level of SWE variability was captured by the high-resolution airborne data, showing that passive microwave retrievals are capable of capturing stand-to-stand SWE variability if the imaging footprint is sufficiently small.     

Spatial resolution improvement of SSM/I data with image restorationtechniques

A space variant image restoration algorithm has been developed with the aim of improving the spatial resolution of SSM/I (Special Sensor Microwave/Imager) passive microwave imagery. Due to the conical scanning of the instrument the relative geometry of the data samples changes over the scan. This change is accounted for by using a space variant point-spread-function in the restoration algorithm. Application of this algorithm to a scene from the Weddell Sea results in an image with enhanced ice edge and coast definition. As a result ice concentration estimates near the edge agree more closely with higher resolution (optical) data from AVHRR     

Seasonal and Regional Variations of Active/Passive Microwave Signatures of Sea Ice

Ku-band (13.3 GHz) scatterometer and K-band (19.4 GHz) radiometer data acquired by the CCRS CV-580 aircraft over the period from 1979 to 1982 in Canadian and Danish (Greenland) coastal waters have been analyzed to determine the seasonal and regional variations of microwave sea-ice signatures. A clustering analysis of the like and cross-polarized scattering cross sections, ?HHo and ?HVo, and the H polarized emissivity ?H, has been used to identify distinct microwave sea-ice signatures for each ice type and to trace the evolution of these signatures with region and season. Ice-type signatures in the high Arctic under cold conditions are quite stable, and major ice classes are readily identified from microwave measurements. Under warmer conditions the signatures change with the structure, moisture content of the snow pack, and with the free water in the surface layers of the underlying ice. An attempt is made to create a consistent picture of the microwave signature transformation by grouping the data into " seaice seasons" (snow and ice surface transformation stages). The separation between microwave ice-class signatures reaches a minimum at the peak of the summer melt.     

A New Algorithm (ECICE) to Estimate Ice Concentration From Remote Sensing Observations: An Application to 85-GHz Passive Microwave Data

A new algorithm, called Environment Canada's Ice Concentration Extractor (ECICE), has been developed to calculate total ice concentration and partial concentration of each ice type from remote-sensing observations. It employs two new concepts. First, it obtains a best estimate of ice concentrations by minimizing the sum of squared difference between observed and estimated radiometric values based on a linear radiometric model for each ice type. Second, instead of employing a single radiometric value (tie point) for each ice type, it utilizes the probability density distribution of the radiometric values for each ice type. Then, in a Monte Carlo simulation, 1000 radiometric values are randomly selected, total and ice-type concentrations are calculated by solving the minimization problem, and finally, median values from the 1000 simulations are chosen. The algorithm was applied to the winter sea ice in the Gulf of St. Lawrence, Canada, using observations from Special Sensor Microwave Imager (SSM/I) 85-GHz channel. Results were evaluated against ice concentration estimates from the operational analysis of Radarsat images at the Canadian Ice Service (CIS). Statistics of the differences between the output concentration and CIS estimates show that ECICE can successfully identify open water and consolidated pack ice pixels better than the Enhanced NASA Team algorithm. However, in areas of ice concentrations between 20% and 70%, the algorithm's performance could not be precisely evaluated because the typical size of the CIS's analysis polygon is much larger than the footprint of the 85-GHz SSM/I channel. Hence, the algorithm captures information at a finer spatial scale. Examples of using one, two, and three radiometric parameters to calculate the concentrations are presented.      

Airborne retrievals of snow and ice surface emissivity atmillimeter wavelengths

Passive microwave radiometers (24-157 GHz) have been flown over Baltic Sea ice and snow sites in April 1995 and on March 15, 1997. Data from these instruments are analyzed with reference to ground measurements of snow and ice conditions, and emissivity spectra are presented for 12 classifications of surface type. A simple model based on dielectric permittivity can accurately represent the microwave spectra of sea ice, but cannot be extended to the behavior of dry snow above 100 GHz without the addition of an extra term to represent volume scattering. The parameterization presented is intended to provide a background for temperature and humidity retrievals from satellite sounders, but the results will be of interest to the snow and ice remote-sensing communities     

军事科技中的微波遥感信息技术

空间微波遥感信息技术的发展与国家安全、军事环境监测、导弹制导、背景中目标识别与跟踪、军事预警、电子对抗等军事高科技有着十分密切的关系。本文讨论我们的微波遥感信息研究在现今军事科技中几个方面的应用。其中包括导弹制导时地表散射杂波的模拟与多谱勒频移的仿真,合成孔径雷达(SAR)图像中船行尾迹对海面舰船的识别,浅海水下地形的反演,低掠角入射海面与舰船的双站散射模拟,从SAR,SSM／I(SpecialSensorMicrowave／Imager)星载微波主被动遥感图像数据反演特定区域的气象等环境特征。     

A comparison of ground-based and satellite-borne microwaveradiometric observations in the Great Plains

The authors compare ground-based and the special sensor microwave/imager (SSM/I) brightness temperatures at 19 and 37 GHz in the Northern and the Southern Great Plains. The comparison was conducted to examine season-related differences in plot-scale and satellite footprint-scale brightness temperatures at these frequencies. The ground-based observations were from the three Radiobrightness Energy Balance Experiments (REBEXs), viz., REBEX-1, REBEX-4, and REBEX-5. REBEX-1 and REBEX-4 were conducted near Sioux Falls, SD, in fall and winter 1992-93, and in summer 1996, respectively. REBEX-5 was conducted near Lamont, OK, during summer 1997 as part of the Southern Great Plains Hydrology Experiment-1997 (SGP'97). The instantaneous fields of view (FOV) of the ground-based radiometers were only a few meters compared to those of the SSM/I, which were several tens of kilometers. The REBEX and the SSM/I brightness temperatures are moderately correlated at both the 19 and 37 GHz. They match well during winter when there was uniform snow cover over the SSM/I footprint. During spring, summer, and fall, REBEX brightness temperatures at the grass-site were on average 18 K higher than the SSM/I brightness temperatures because the SSM/I footprint included nearby agricultural fields in summer and predominantly bare soil in fall and spring. During summer, REBEX-4 brightness temperatures at the bare soil site were on average 10 K cooler than the SSM/I brightness temperatures. In effect, the REBEX grass and bare soil brightness temperatures bracket the SSM/I observations with the SSM/I brightness temperatures lying closest to those of the bare soil     

An Ocean Surface Wind Vector Model Function for a Spaceborne Microwave Radiometer

Surface wind vector measurements over the oceans are vital for scientists and forecasters to understand the Earth's global weather and climate. In the last two decades, operational measurements of global ocean wind speeds were obtained from passive microwave radiometers (Special Sensor Microwave/ Imagers); and over this period, full ocean surface wind vector data were obtained from several National Aeronautics and Space Administration and European Space Agency scatterometry missions. However, since SeaSat-A in 1978, there have not been other combined active and passive wind measurements on the same satellite until the launch of Japan Aerospace Exploration Agency's Advanced Earth Observing Satellite-II in 2002. This mission provided a unique data set of coincident measurements between the SeaWinds scatterometer and the Advanced Microwave Scanning Radiometer (AMSR). The AMSR instrument measured linearly polarized brightness temperatures (TB) over the ocean. Although these measurements contained wind direction information, the overlying atmospheric influence obscured this signal and made wind direction retrievals not feasible. However, for radiometer channels between 10 and 37 GHz, a certain linear combination of vertical and horizontal brightness temperatures causes the atmospheric dependence to cancel and surface parameters such as wind speed and direction and sea surface temperature to dominate the resulting signal. In this paper, an empirical relationship between AMSR T_B's (specifically A ^. T_BV - T_BH) and surface wind vectors (inferred from SeaWinds' retrievals) is established for three microwave frequencies: 10, 18, and 37 GHz. This newly developed wind vector model function for microwave radiometers can serve as a basis for wind vector retrievals either separately or in combination with active scatterometer measurements.     

Estimating terrestrial snow depth with the TOPEX-Poseidon altimeter and radiometer

Active and passive microwave measurements obtained by the dual-frequency TOPEX-Poseidon radar altimeter from the Northern Great Plains of the United States are used to develop a snow pack radar backscatter model. The model results are compared with daily time series of surface snow observations made by the U.S. National Weather Service. The model results show that Ku-band provides more accurate snow depth determinations than does C-band. Comparing the snow depth determinations derived from the TOPEX-Poseidon nadir-looking passive microwave radiometers with the oblique-looking Satellite Sensor Microwave Imager (SSM/I) passive microwave observations and surface observations shows that both instruments accurately portray the temporal characteristics of the snow depth time series. While both retrievals consistently underestimate the actual snow depths, the TOPEX-Poseidon results are more accurate.     

NPOESS Aircraft Sounder Testbed-Microwave (NAST-M): instrumentdescription and initial flight results

The National Polar-Orbiting Operational Environmental Satellite System (NPOESS) Aircraft Sounder Testbed (NAST) has been developed and deployed on the NASA ER-2 high-altitude aircraft. The testbed consists of two co-located cross-track scanning instruments: a Fourier transform interferometer spectrometer (NAST-I) with spectral coverage of 3.7-15.5 μm and a passive microwave spectrometer (NAST-M) with 17 channels near the oxygen absorption lines at 50-57 GHz and 118.75 GHz. The testbed provides the first coregistered imagery from high-resolution microwave and infrared sounders and will provide new data that will help (1) validate meteorological satellite environmental data record feasibility, (2) define future satellite instrument specifications, and (3) demonstrate operational issues in ground validation, data calibration, and retrievals of meteorological parameters. To help validate the performance and potential of NAST-M, imagery was collected from more than 20 overpasses of hurricanes Bonnie and Earl during the Convection and Moisture Experiment (CAMEX-3), Florida, boreal summer 1998. The warm core and convection morphology of Hurricane Bonnie (August, 1998) is clearly revealed both by aircraft-based microwave brightness temperature imagery and temperature retrievals within the eye. Radiance comparisons with the Advanced Microwave Sounding Unit on the NOAA-15 satellite and radiosonde observations yield root mean-squared agreements of approximately 1 K or less     

Land-surface-type classification using microwave brightnesstemperatures from the Special Sensor Microwave/Imager

The use of empirical parameter retrieval algorithms over land requires the prior classification of surface types according to their microwave emission properties. A land-surface-type classification scheme was developed to be used with the Special Sensor Microwave/Imager (SSM/I) algorithm package. The classification rules were based on statistical analysis of SSM/I brightness temperature combinations from several surfaces, including dense vegetation, rangeland and agricultural soils, deserts, snow, precipitation, surface moisture, etc. A set of independent classification rules was derived which should result in increased confidence of parameter retrievals