Determination of the age distribution of sea ice from Lagrangianobservations of ice motion

A procedure for monitoring the local age distribution of the Arctic sea ice cover is presented. The age distribution specifies the area covered by ice in different age classes. In the authors' approach, a regular array of grid points is defined initially on the first image of a long time series, and an ice tracker finds the positions of those points in all subsequent images of the series. These Lagrangian points mark the corners of a set of cells that move and deform with the ice cover. The area of each cell changes with each new image or time step. A positive change indicates that ice in a new age class was formed in the cell. A negative change is assumed to have ridged the youngest ice in the cell, reducing its area. The ice in each cell ages as it progresses through the time series. The area of multiyear ice in each cell is computed using an ice classification algorithm. Any area that is not accounted for by the young ice or multiyear ice is assigned to a category of older first-year ice. The authors thus have a fine age resolution in the young end of the age distribution, and coarse resolution for older ice. The age distribution of the young ice can be converted to a thickness distribution using a simple empirical relation between accumulated freezing-degree days and ice thickness, or using a more complicated thermodynamic model. The authors describe a general scheme for implementing this procedure for the Arctic Ocean from fall freeze-up until the onset of melt in the spring. The concept is illustrated with a time series of five ERS-1 SAR images spanning a period of 12 days. Such a scheme could be implemented with RADARSAT SAR imagery to provide basin-wide ice age and thickness information     

On the utility of SeaWinds/QuikSCAT data for the estimation of the thermodynamic state of first-year sea ice

The thermodynamic state of sea ice is important to accurately and remotely monitor in order to provide improved geophysical variable parameterizations in sea ice thermodynamic models. Operationally, monitoring the thermodynamic state of sea ice can facilitate eased ship navigation routing. SeaWinds/QuikSCAT (QuikSCAT) dual-polarization [i.e., horizontal (HH) and vertical (VV)] active microwave data are available at a sufficiently large spatial scale and high temporal resolution to provide estimates of sea ice thermodynamics. This analysis evaluated the temporal evolution of the backscatter coefficient (/spl sigma//spl deg/) and VV/HH copolarization ratio from QuikSCAT for estimating sea ice thermodynamics. QuikSCAT estimates were compared against RADARSAT-1 synthetic aperture radar (SAR) imagery and the Canadian Ice Service (CIS) prototype operational ice strength algorithm. In situ data from the Collaborative Interdisciplinary Cryospheric Experiment (C-ICE) for 2000, 2001, and 2002 were used as validation. Results indicate that the temporal evolution of /spl sigma//spl deg/ from QuikSCAT is analogous to RADARSAT-1. The QuikSCAT /spl sigma//spl deg/ temporal evolution has the ability to identify winter, snow melt, and ponding thermodynamic states. Moreover, the copolarization VV/HH ratio of QuikSCAT could provide a second estimate of the ponding state in addition to identifying the drainage state that is difficult to detect by single-polarization SAR. QuikSCAT detected thermodynamic states that were found to be in reasonable agreement to that of in situ observations at the C-ICE camp for all years. Operational implications of this analysis suggest QuikSCAT is a more effective and efficient medium for monitoring ice decay compared to RADARSAT-1 and can be utilized to provide more robust modeled ice strength thresholds.     

Determination of oceanic total precipitable water from the SSM/I

Results are presented of calibration/validation studies showing the ability of the Special Sensor Microwave/Imager (SSM/I) to measure total precipitable water in the atmosphere over the ocean. Comparisons between radiosondes and the SSM/I are presented for three different algorithms. The results show the possibility of a distinct improvement in the retrieval of total precipitable water over the ocean. The global, nonlinear algorithm is more sensitive to cloud liquid water content, rain, and sea ice. The additional sensitivity is due to the screening of rain and sea ice from the dependent data and the squared term in the retrieval algorithm. Thus, it will be very important to have good screening procedures for identifying these conditions. The linear algorithm overestimates in the mid-range and underestimates at large values of total precipitable water. The explanation for this effect is probably related to the selection of the center of the water vapor line as the operating frequency of the SSM/I water vapor channel. The line center is most likely to exhibit a saturation effect at large water vapor amounts, and pressure and temperature effects can also be important, depending on the distribution of water vapor in the atmosphere     

Ocean surface wind speed and direction retrievals from the SSM/I

A semiempirical model is developed that retrieves ocean surface wind direction information in addition to improved wind speeds from Special Sensor Microwave/Imager (SSM/I) measurements. Radiative transfer and neural network techniques were combined in the authors' approach. The model was trained and tested using clear sky cases, but atmospheric transmittance is retrieved so that retrieval in other than clear sky conditions is possible. With two SSM/I instruments currently providing operational ocean surface wind speed retrievals, the addition of wind direction information and improved wind speed retrievals will enhance the impact of this data in weather prediction models and marine weather forecasting     

Interpretation of SSM/I measurements over Greenland

Multispectral brightness temperature (T_B) measurements over Greenland are obtained from the Special Sensor Microwave Imager (SSM/I), which are flown aboard the DMSP satellites. This paper examines the different spectral characteristics over Greenland throughout the year. Although snow covers the vast majority of Greenland, the southern regions rarely exhibit the spectral characteristics associated with snowcover (i.e., T_B decreases at higher frequencies). In fact, the SSM/I polarization and frequency measurements over southern Greenland are more indicative of water than a snow-covered surface (i.e., T_B increases at higher frequencies). A simplified physical model is developed to help explain the anomalous measurements over southern Greenland. Model results indicate that high frequency radiation is mainly scattered by snow grains residing above the subsurface ice layers, whereas low frequency radiation is scattered throughout a much greater depth. Since low frequencies are scattered throughout a greater volume, they are depressed relative to high frequencies, and the typical snowcover signature is absent     

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     

Spatial resolution enhancement of SSM/I data

One of the limitations in using Special Sensor Microwave/Imager (SSM/I) data for land and vegetation studies is the relatively low-spatial resolution. To ameliorate this limitation, resolution-enhancement algorithms can be applied to the data. In this paper, the Backus-Gilbert inversion (BGI) technique and the scatterometer image-reconstruction (SIR) algorithm are investigated as possible methods for creating enhanced resolution images from SSM/I data. The two algorithms are compared via both the simulation and the actual SSM/I data. The algorithms offer similar resolution enhancement, though SIR requires significantly less computation. Sample results over two land regions of South America are presented     

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     

Land surface temperature derived from the SSM/I passive microwavebrightness temperatures

Passive microwave brightness temperatures from the Defense Meteorological Space Program Special Sensor Microwave/Imager (SSM/I) were used to determine surface temperature over land areas in the central plains of the United States. A regression analysis comparing all of the SSM/I channels and minimum screen air temperatures (representing the surface temperature) showed good correlations, with root-mean-square errors of 2-3 degC. Pixels containing large amounts of water, snow, and falling rain, as classified with SSM/I brightness temperatures, were excluded from the analysis. The use of independent ground truth data such as soil moisture or land surface type was not required to obtain the correlations between brightness temperatures and surface temperatures     

Global identification of snowcover using SSM/I measurements

Visible satellite sensors have monitored snowcover throughout the Northern Hemisphere for almost thirty years. These sensors can detect snowcover during daylight, cloud-free conditions. The operational procedure developed by NOAA/NESDIS requires an analyst to manually view the images in order to subjectively distinguish between clouds and snowcover. Because this procedure is manually intensive, it is only performed weekly. Since microwave sensors see through nonprecipitating clouds, snowcover can be determined objectively without the intervention of an analyst. Furthermore, microwave sensors can provide daily analysis of snowcover in real-time, which is essential for operational forecast models and regional hydrologic monitoring. Snowcover measurements are obtained from the Special Sensor Microwave Imager (SSM/I), flown aboard the DMSP satellites. A decision tree, containing various filters, is used to separate the scattering signature of snowcover from other scattering signatures. Problem areas are discussed and when possible, a filter is developed to eliminate biases. The finalized decision tree is an objective algorithm to monitor the global distribution of snowcover. Comparisons are made between the SSM/I snowcover product and the NOAA/NESDIS subjectively analyzed weekly product     

基于Multiloss-SAM-ConvLSTM的北极航道独立海冰运动预测

北极航道海冰运动的准确预测对于保证航行安全、评估航道可通行性和动态修正航线具有重要的指导意义。传统的光流法无法满足海冰运动预测任务中“时空预测+语义分割”的要求。为此,基于MERSI-Ⅱ影像制作了海冰运动数据集SeaiceMoving,提出了一种基于Multiloss-SAM-ConvLSTM的海冰运动预测算法,该算法在SAM-ConvLSTM的基础上引入加权的FDWloss,强化了各节点空间语义的获取。针对样本分布不平衡,讨论了后端分割阈值的偏移效应,通过网格搜索确定最佳分割阈值,提高了海冰整体预测结果。实验结果表明,该方法的Kappa系数为0.75,IOU为0.61,Dice系数为0.76,相较于SAM-ConvLSTM,分别提高了0.1、0.12和0.1,对运动后海冰的位置预测和形状提取能力均有提升,减少了海冰“黏连”的情况。此外,该算法对薄云干扰下的海冰运动依然具备良好的预测能力,可以为北极航线的动态规划和航线修正提供较为准确的技术支撑。     

A contrast and comparison of near-sea surface airtemperature/humidity from GMS and SSM/I data with an improved algorithm

With data sets gained from Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I) microwave channels, Geostationary Meteorological Satellite (GMS-5) infrared channels, and ship-measured data, the statistical algorithms to estimate sea surface temperature and near-sea surface air humidity around Taiwan and the South China Sea areas are developed. Then a new, improved method to estimate near-sea surface air temperature based on the algorithm proposed by Konda et al. (1996) is established in this study. The results estimated with SSM/I data show that the root mean square error (RMSE) of SST, near-sea surface air humidity and air temperature over the oceans around Taiwan and the South China Sea are 1.2 K,1.43 g/kg, and 1.6 K, respectively. The results with GMS data are 1.7 K,1.71 g/kg and 1.7 K, respectively. The results also show that the improvements in the algorithm of Konda et al. simplify the computation scheme, improve the accuracy, and match the regional ocean-atmosphere properties in retrieving near-sea surface air temperature. The estimate produced using SSM/I and GMS data also show good consistency between them, both in temporal and spatial variations. Basically, the accuracy of this result implies strong potential for application of satellite data to relative studies and operational work in the ocean-atmosphere interaction     

Metamorphic signature of snow revealed in SSM/I measurements

Brightness temperatures (19, 22, 37, 85 GHz) measured by the special sensor microwave/imager (SSM/I) are analyzed using data from the snow monitoring network within the former Soviet Union during the 1987-1988 winter period. It is shown that in the beginning of winter, the SSM/I measurements display the classical snow scattering signature, i.e., the brightness temperatures decrease with increasing depth, and the largest decrease occurs at the highest frequency. Dramatic deviations from this pattern are observed in the middle of winter, where the brightness temperature approaches a minimum and then begins to increase despite the fact that the snow depth remains constant or continues to grow. The two-stream radiative transfer model is combined with results from dense media theory to help explain the phenomenon. Model results suggest that the increase in brightness temperature is due to a decrease of the single scattering albedo as the snowpack ages. This decrease of the albedo is related to changes in the snow crystalline structure due to metamorphism. Consequences for the interpretation of satellite measurements and development of algorithms for deriving snow water equivalent are discussed     

A technique for enhancing and matching the resolution of microwavemeasurements from the SSM/I instrument

The authors describe and apply a correction technique for matching the resolution of all the frequencies of the Special Sensor Microwave/Imager (SSM/I) to the 25-km spatial resolution of the 37-GHz channel. To accomplish this, it is necessary to increase the spatial resolution of the 19- and 22-GHz channels while degrading that of the 85-GHz channel. It is found that the approach produces adequate enhancement of the spatial resolution to make such a correction worthwhile. Results suggest that this technique decreases brightness temperature differences stemming solely from spatial resolution differences by over 50% for the low resolution channels with only a modest increase in random noise. The correction can also help to better resolve small features which would otherwise be lost due to the lack of resolution     

Arctic-wide operational sea ice drift from enhanced-resolution QuikScat/SeaWinds scatterometry and its validation

The near-real-time enhanced-resolution QuikScat/SeaWinds (QS) scatterometry composite data are used for daily automatic 48-h sea ice tracking by maximum cross-correlation over the entire Arctic. A correlation window of 61 /spl times/ 61 pixels is used for best performance. Both QS polarizations, vertical (VV) and horizontal (HH), are used independently, which permits to filter the final results for erroneous vectors by comparing the two drift vectors, u/spl I.oarr//sub VV/ and u/spl I.oarr//sub HH/, respectively. Additional filtering is performed by setting a minimum correlation coefficient and by considering the spatial consistency of the motion field. The algorithm has been validated with winter 2002/2003 buoy data from the International Arctic Buoy Program showing error standard deviations in the 48-h displacement of 3.1 and 3.2 km in the latitude and longitude direction, respectively. This corresponds to an error standard deviation in ice drift speed of just 2.6 cm/s. Errors are largest in dynamic regions with lower ice concentrations as for example the southern Fram Strait. The enhanced-resolution data improve previous drift results by about 25%, but are still blurred by the necessary 36-h period to produce the composites.     

Determination of cloud liquid water content using the SSM/I

As part of a calibration/validation effort for the Special Sensor Microwave/Imager (SSM/I), coincident observations of SSM/I brightness temperatures and surface-based observations of cloud liquid water were obtained. These observations were used to validate initial algorithms and to derive an improved algorithm. The initial algorithms were divided into latitudinal-, seasonal-, and surface-type zones. It was found that these initial algorithms, which were of the D-matrix type, did not yield sufficiently accurate results. The surface-based measurements of channels were investigated; however, the 85 V channel was excluded because of excessive noise. It was found that there is no significant correlation between the SSM/I brightness temperatures and the surface-based cloud liquid water determination when the background surface is land or snow. A high correlation was found between brightness temperatures and ground-based measurements over the ocean     

风云三号卫星MERSI影像提取北极海冰参数的方法

风云三号卫星MERSI影像的空间分辨率达到250 m,不仅可以长期宏观监测海冰整体分布变化,而且能够观测分析破碎冰块的面积、圆度等细节形态特征.从海冰光谱特征和冰块灰度分布特征出发,给出MERSI影像提取海冰整体分布参数和冰块形态参数方法,包括海冰识别、冰块分离提取以及海冰参数提取,利用这些方法成功提取了2014年夏季弗拉姆海峡海冰边缘区的海冰细节信息.给出的方法可为研究分析北极海冰变化及海气相互作用提供方法支持和信息保障.     

Surface roughness characterizations of sea ice and ice sheets: case studies with MISR data

This work is an examination of potential uses of multiangular remote sensing imagery for mapping and characterizing sea ice and ice sheet surfaces based on surface roughness properties. We use data from the Multi-angle Imaging SpectroRadiometer (MISR) to demonstrate that ice sheet and sea ice surfaces have characteristic angular signatures and that these angular signatures may be used in much the same way as spectral signatures are used in multispectral classification. Three case studies are examined: sea ice in the Beaufort Sea off the north coast of Alaska, the Jakobshavn Glacier on the western edge of the Greenland ice sheet, and a region in Antarctica south of McMurdo station containing glaciers and blue-ice areas. The MISR sea ice image appears to delineate different first-year ice types and, to some extent, the transition from first-year to multiyear ice. The MISR image shows good agreement with sea ice types that are evident in concurrent synthetic aperture radar (SAR) imagery and ice analysis charts from the National Ice Center. Over the Jakobshavn Glacier, surface roughness data from airborne laser altimeter transects correlate well with MISR-derived estimates of surface roughness. In Antarctica, ablation-related blue-ice areas, which are difficult to distinguish from bare ice exposed by crevasses, are easily detected using multiangular data.     

Spatial resolution enhancement of terrestrial features usingdeconvolved SSM/I microwave brightness temperatures

A method for enhancing the 19-, 22-, and 37-GHz measurements of the SSM/I (Special Sensor Microwave/Imager) to the spatial resolution and sampling density of the high-resolution 85-GHz channel is presented. An objective technique for specifying the tuning parameter, which balances the tradeoff between resolution and noise, is developed in terms of maximizing cross-channel correlations. Various validation procedures are performed to demonstrate the effectiveness of the method, which, it is hoped, will provide researchers with a valuable tool in multispectral applications of satellite radiometer data     

Ocean surface wind speed measurements of the Special SensorMicrowave/Imager (SSM/I)

An operational wind speed algorithm was developed. This algorithm is based on the D-matrix approach, which seeks a linear relationship between measured SSM/I brightness temperatures and environmental parameters. D-matrix performance in the low-to-medium wind speed range was validated by comparing algorithm-derived wind speeds with near-simultaneous and colocated measurements made by the anemometers of offshore ocean buoys. Results indicate that for approximately 85% of the time, the D-matrix-retrieved winds will have an accuracy better than the Defense Meteorological Space Program goal of ±2 m/s. For the remaining 15% of the time, the scene will be rain-flagged and retrieval accuracies will be worse than ±2 m/s