Antarctic ice sheet and sea ice regional albedo and temperature change, 1981-2000, from AVHRR Polar Pathfinder data

Spring-summer (November, December, January) ice sheet and sea ice regional surface albedo, surface temperature, sea ice concentration and sea ice extent averages and trends from 1981 to 2000 have been calculated for the Antarctic area. In this research the AVHRR Polar Pathfinder 5-km EASE-Grid Composites and the combined SMMR and SSMI data sets from the National Snow and Ice Data Center (NSIDC), Boulder, Colorado have been employed. A regional analysis has been made for five longitudinal sectors around Antarctica: the Weddell Sea (WS), the Indian Ocean (IO), the Pacific Ocean (PO), the Ross Sea (RS) and the Bellingshausen-Amundsen Sea (BS). The IO and PO sectors show ice sheet albedos of 0.85 and temperatures of − 25 °C. The corresponding values in the RS and BS sectors are 0.80 and − 16 °C respectively. The sea ice albedo is about 0.60 in the RS, BS and WS sectors and 0.55 in the IO and PO sectors. The average sea ice temperature varies around − 12 °C. All the sectors show slight increasing spring-summer albedo trends and decreasing spring-summer temperature trends and similar interannual variability in albedo and surface temperature. The steepest ice sheet albedo trend of 0.0019 ± 0.0009/yr is found in the RS sector. The steepest sea ice albedo trend of 0.0044 ± 0.0017 /yr occurs in the PO sector. The steepest temperature trends for both the ice sheet and sea ice occur in the BS sector, having values of − 0.075 ± 0.040 °C/yr and − 0.107 ± 0.027 °C/yr respectively. The sea ice concentration shows slight increasing trends, the highest being in the PO sector (0.3 ± 0.12%/yr), whereas the sea ice extent trends are near zero with the exception of the RS sector (14,700 ±8600 km²/yr) and the BS sector (− 13,000 ± 6400 km²/yr).     

Improved retrieval of sea ice total concentration from spaceborne passive microwave observations using numerical weather prediction model fields: An intercomparison of nine algorithms

The accuracy of the sea ice concentration estimates in polar regions is reduced by the effects of atmospheric emission and absorption. A method is presented where a fast atmospheric radiative transfer model and Numerical Weather Prediction (NWP) model fields are used to correct brightness temperatures before they enter the sea ice concentration algorithm. The skill of the method is a function of the errors in the NWP model fields modulated by the sensitivities of the sea ice concentration algorithm used. The NWP model fields representing the most significant atmospheric parameters, i.e. water vapour, cloud liquid water, surface temperature and wind speed over open water are evaluated using remote sensing data. For wind speed and total water vapour, it is found that the standard deviation of the difference is less than the RMS error quoted for the remote sensing algorithms. The best consistency is found for water vapour followed by wind speed. The NWP model cloud liquid water displays standard deviations much higher than the RMS error of the remote sensing algorithm and close to the total average content. Nine sea ice concentration algorithms are further evaluated in a sensitivity study to the above-mentioned atmospheric constituents using a detailed atmospheric radiative transfer model. The result shows that the class of algorithms based solely on the 19 and 37 GHz vertically polarised channels display the smallest sensitivity to all three atmospheric parameters: total water vapour, wind speed and cloud liquid water. Finally, it is demonstrated that this method overcomes many problems associated with conventional weather filtering over mixed ice-water and new-ice pixels and allows the retrieval of sea ice concentrations below 10%.     

Derivation of melt pond coverage on Arctic sea ice using MODIS observations

The formation of meltponds on the surface of sea ice during summer is one of the main factors affecting variability in surface albedo over the ice cover. However, observations of the spatial extent of ponding are rare. To address this, a MODIS surface reflectance product is used to derive the daily melt pond cover over sea ice in the Beaufort/Chukchi Sea region through the summer of 2004. For this region, the estimated pond cover increased rapidly during the first 20 days of melt from 10% to 40%. Fluctuations in pond cover occurred through summer, followed by a more gradual decrease through late August to 10%. The rapid initial increase in pond cover occurred later as latitude increased and melt progressed northward.

A surface campaign at Barrow in June 2004 provided pond and ice spectral reflectance needed by the MODIS algorithm to deduce pond coverage. Although individual pond and ice reflectance varies within the comparatively small region of measurement, the mean values used within the algorithm ensured that relevant values (i.e. concurrent with satellite observations) were being applied.

Aerosonde unpiloted aerial vehicles (UAVs) were deployed in June 2004 from Barrow, Alaska, to photograph the sea ice so melt pond cover could be estimated. Although the agreement between derived pond cover from UAV photos and estimates from MODIS varies, the mean estimates and distribution of pond coverages are similar, suggesting that the MODIS technique is useful for estimating pond coverage throughout the region. It is recommended that this technique be applied to the entire Arctic through the melt season.     

Correcting for the influence of frozen lakes in satellite microwave radiometer observations through application of a microwave emission model

The spatial resolution of passive microwave observations from space is of the order of tens of kilometers with currently available instruments, such as the Special Sensor Microwave/Imager (SSM/I) and Advanced Microwave Scanning Radiometer (AMSR-E). The large field of view of these instruments dictates that the observed brightness temperature can originate from heterogeneous land cover, with different vegetation and surface properties.In this study, we assess the influence of freshwater lakes on the observed brightness temperature of AMSR-E in winter conditions. The study focuses on the geographic region of Finland, where lakes account for 10% of the total terrestrial area. We present a method to mitigate for the influence of lakes through forward modeling of snow covered lakes, as a part of a microwave emission simulation scheme of space-borne observations. We apply a forward model to predict brightness temperatures of snow covered sceneries over several winter seasons, using available data on snow cover, vegetation and lake ice cover to set the forward model input parameters. Comparison of model estimates with space-borne observations shows that the modeling accuracy improves in the majority of examined cases when lakes are accounted for, with respect to the case where lakes are not included in the simulation. Moreover, we present a method for applying the correction to the retrieval of Snow Water Equivalent (SWE) in lake-rich areas, using a numerical inversion method of the forward model. In a comparison to available independent validation data on SWE, also the retrieval accuracy is seen to improve when applying the influence of snow covered lakes in the emission model.     

Semi-automated feature-tracking of East Antarctic sea ice from Envisat ASAR imagery

While feature tracking of sea ice using cross-correlation methods on pairs of satellite Synthetic Aperture Radar (SAR) images has been extensively carried out in the Arctic, this is not the case in the Antarctic. This is due to the dynamic nature of Antarctic pack ice, its microwave signature, the tendency for SAR swath paths to be poorly aligned with the often narrow sea ice zone around the continent and inadequate satellite sampling. A semi-automated system, known as IPADS (IMCORR [IMageCORRelation] Processing, Analysis and Display System), has been developed to map fast ice and pack ice in Antarctica using multiple pairs of SAR images. The software processing pipeline uses overlapping image pairs which are geocoded and roughly registered using only data contained in the image headers. Next, fast ice maps are rapidly generated using zero motion features located within ocean regions. This also provides precise image registration. Finally, the same image pairs are re-examined for pack ice motion in a slower off-line batch process. The pack and fast ice are identified using a cluster-based search method which compares both location and motion information. Each image pair generates a NetCDF file which adds to a growing database of Antarctic sea ice motion and ice roughness. Five image-pair examples are presented to illustrate the methods used as well as their strengths and limitations. Substantial pack ice motion can often be detected in the marginal ice zone on SAR images only a few days apart.     

实测光谱和欧氏距离在GF2卫星海冰提取中的应用

由于卫星受载荷分辨率和混合光谱限制,海冰遥感监测存在很大不确定性。为提高海冰提取精度,本文将实测光谱与高分辨率卫星数据有效结合,利用光谱数据进行GF2卫星等效反射率转换;基于欧式距离确定16种海冰判识指标的可分性;从而提出一种GF2卫星海冰快速提取方法,为中低分辨率卫星大范围的海冰监测提供基准订正数据。结果表明：①经过光谱曲线分析初步确定B2和B4波段是GF2卫星海冰遥感监测的敏感波段;②欧式距离计算结果反映16种判识指标中,B2/B4指标欧氏距离最大,海冰与雪、冰水混合物、海水等相似地物的可分性最强,是海冰最佳判识指标③根据判识指标采用阈值法能快速提取海冰分布信息,实现海冰与其他相似地物的分离;④比较本文提出的新指标（B2/B4）与其他4种指标海冰提取的精度,在冰水两种地物分类中,B2/B4判识指标略好;在多种地物分类中,B2/B4判识指标总体分类精度为96.48%,Kappa系数大于0.5,比其他指标高出3.89～4.85%。     

Generation of high-resolution East Antarctic landfast sea-ice maps from cloud-free MODIS satellite composite imagery

A method to generate high spatio-temporal resolution maps of landfast sea ice from cloud-free MODIS composite imagery is presented. Visible (summertime) and thermal infrared (wintertime) cloud-free 20-day MODIS composite images are used as the basis for these maps, augmented by AMSR-E ASI sea-ice concentration composite images (when MODIS composite image quality is insufficient). The success of this technique is dependent upon efficient cloud removal during the compositing process. Example wintertime maximum (~ 374,000 km²) and summertime minimum (~ 112,000 km²) fast-ice maps for the entire East Antarctic coast are presented. The summertime minimum map provides the first high-resolution indication of multi-year fast-ice extent, which may be used to help assess changes in Antarctic sea-ice volume. The 2σ errors in fast-ice extent are estimated to be ± 2.98% when ≥ 90% of the fast-ice pixels in a 20-day period are classified using the MODIS composite, or ± 8.76 otherwise (when augmenting AMSR-E or the previous/next MODIS composite image is used to classify > 10% of the fast ice). Imperfect composite image quality, caused by persistent cloud, inaccurate cloud masking or a highly dynamic fast-ice edge, was the biggest impediment to automating the fast-ice detection procedure.     

Detection of small-scale roughness and refractive index of sea ice in passive satellite microwave remote sensing

Polar ice masses and sheets are sensitive indicators of climate change. Small-scale surface roughness significantly impacts the microwave emission of the sea ice/snow surface; however, published results of surface roughness measurements of sea ice are rare. Knowing the refractive index is important to discriminate between objects. In this study, the small-scale roughness and refractive index over sea ice are estimated with AMSR-E observations and a unique method. Consequently, the small-scale surface roughness of 0.25 cm to 0.5 cm at AMSR-E 6.9 GHz shows reasonable agreement with the results of known observations, ranging from 0.2 cm to 0.6 cm for the sea ice in the Antarctic and Arctic regions. The refractive indexes are retrieved from 1.6 to 1.8 for winter, from 1.2 to 1.4 for summer in the Arctic and the Antarctic, which are similar to those of the sea ice and results from previous studies. This research shows the physical characteristics of the sea ice edges and melting process. Accordingly, this investigation provides an effective procedure for retrieving the small-scale roughness and refractive index of sea ice and snow. Another advantage of this study is the ability to distinguish sea ice from the sea surface by their relative small-scale roughness.     

Observations of Lake Baikal ice from satellite altimetry and radiometry

We demonstrate the potential of combining satellite altimetry and radiometry for lake ice studies using the example of Lake Baikal in Siberia. We show the synergy using active and passive microwave observations available from the recent satellite altimetry missions (TOPEX/Poseidon, Jason-1, ENVISAT and Geosat Follow-On), complemented by the SSM/I passive data. We assess the applicability of altimetric and radiometric data for ice/water discrimination, and discuss the drawbacks and benefits of each type of sensor. An ice discrimination method, based on the combined use of the data from the four altimetric missions and SSM/I, is proposed and validated using available in situ observations and MODIS imagery. The method is applied to the entire satellite data set and used to define specific dates of ice events (first appearance of ice, formation of stable ice cover, first appearance of open water, complete disappearance of ice) and associated uncertainties. Using these satellite-derived estimates, we can extend the existing time series of ice events in the Southern Baikal up to 2004 and provide new information on the Middle and Northern Baikal, regions where no recent in situ ice cover observations are available. Our data show that over the last 10-15 years, trends towards earlier ice formation and later ice break-up result in a tendency for longer fast ice duration over the whole Lake Baikal. The methods proposed here have been tested for Lake Baikal, but they are applicable for other lakes and water bodies with seasonal ice cover.     

On the use of the dual-frequency ENVISAT altimeter to determine snowpack properties of the Antarctic ice sheet

The primary purpose of ice-sheet altimetry is to monitor the changes in ice-sheet topography which may impact on global sea-level. However, the altimetric signal is sensitive to different properties of the snowpack, and therefore can also be used to determine these properties. The radar altimeter onboard the European Space Agency's ENVISAT satellite provides a dual-frequency dataset at Ku (13.6 GHz) and S band (3.2 GHz). In this paper, these signals are studied over the Antarctic ice-sheet during the 4 first years of the mission (2002-2006), in order to retrieve snowpack properties.The altimeter signal can be described by 4 classical waveform parameters. The 4 year time-series of all these parameters are decomposed into a linear and a seasonal time component. The linear component is almost constant. The distribution of the mean parameters over the Antarctic ice-sheet shows that the altimeter signal is sensitive to small-scale (mm) surface roughness.For the first time, the amplitudes and phases of the seasonal variations are characterized. The S band amplitudes are greater than the Ku band, and the phase varies over the entire ice-sheet. Previous studies suggested that the seasonal variations of the altitude from the altimeter are created by a decrease of the snowpack height through compaction. The dual-frequency observations shown here suggest that this hypothesis is too simple. Instead, the altitude variations observed in the altimetric signal are not created by the snowpack height change, but are more likely caused by the seasonal change of the snow properties, which cause a different response between the S and Ku bands. Therefore, both the linear and the seasonal variations of the altimetric signal can be used to retrieve snowpack properties.Here, we compare the dual-frequency ENVISAT signal with a model of the altimetric echo over the Antarctic ice-sheet. The model combines a surface model with a sub-surface model, for both the S and Ku bands. The Brown model [Brown G. S. (1977). The average impulse response of a rough surface and its applications. IEEE Transactions on Antennas and Propagation, 25, 1.] is used to describe the interaction of the radar wave with the snow surface. The backscatter coefficient of the surface is derived using the IEM method [Fung, A. K. (1994). Microwave scattering and emission models and their applications, Boston, MA: Artech House.]. The sub-surface signal takes into account both the layering effects and the scattering caused by the homogeneous media which is composed of small snow grains. The model is tested in two areas of the Antarctic plateau which present very different waveform parameters. The sensitivity of the radar signal to the different snowpack properties is investigated. The analysis of the waveform behaviours shows that the sub-surface signal can be completely masked by the small-scale surface roughness signal.Finally, the temperature and surface density effects are investigated in order to explain the seasonal variations of the altimetric signal. Both the temperature and the compaction rate of the snow change seasonally. Temperature is shown to impact on the Ku band signal. Furthermore, the compaction rate of the snow surface can explain all of the seasonal variation characteristics observed at both the S and Ku bands. The seasonal change of compaction rate in the snow creates a change in the waveform shape that can bias the altitude. In particular, the snow compaction can induce a bias in the retrieved altimetric altitude of more than 80 cm for the Ku band and 1.5 m for the S band. This work underlines that the altitude time-series needs to be corrected for the shape of the altimetric echo over ice-sheets.     

Development of a winter snow water equivalent algorithm using in situ passive microwave radiometry over snow-covered first-year sea ice

A snow water equivalent (SWE) algorithm has been developed for thin and thick snow using both in situ microwave measurements and snow thermophysical properties, collected over landfast snow covered first-year sea ice during the Canadian Arctic Shelf Exchange Study (CASES) overwintering mission from December 2003 to May 2004. Results showed that the behavior of brightness temperatures (T_bs) in thin snow covers was very different from those in a thick snowpack. Microwave SWE retrievals using the combination of T_b 19 GHz and air temperature (multiple regression) over thick snow are quite accurate, and showed very good agreement with the physical data (R² = 0.94) especially during the cooling period (i.e., from freeze up to the minimum air temperature recorded) where the snow is dry and cold. Thin snow SWE predictions also showed fairly good agreement with field data (R² = 0.70) during the cold season. The differences between retrieved and in situ SWE for both thin and thick snow cover are mainly attributable to the variations in air temperature, snow wetness and spatial heterogeneity in snow thickness.     

Mapping sediment-laden sea ice in the Arctic using AVHRR remote-sensing data: Atmospheric correction and determination of reflectances as a function of ice type and sediment load

Exploiting the fact that the spectral characteristics of light backscattered from sediment-laden ice differ substantially from those of clean ice and that sediment tends to accumulate at the ice surface during the first melt season, remote-sensing techniques provide a valuable tool for mapping the extent of particle-laden ice in the Arctic basin and assessing its particulate loading. This study considers two fundamental problems that still need to be addressed in order to make full use of satellite observations for this type of assessment: (i) the effects of the atmosphere on surface reflectances derived from radiances measured by the satellite sensor need to be quantified and ultimately corrected for, and (ii) the spectral reflectance of the ice surface as a function of particle loading and sub-pixel distribution needs to be determined in order to derive quantitative estimates from the at-sensor satellite signal. Here, spectral albedos have been computed for different ice surfaces of variable sediment load with a radiative transfer model for sea ice coupled with an optical model for particulates included in sea ice. In a second step, the role of the atmosphere in modulating the surface reflectance signal is assessed with the aid of an atmospheric radiative transfer model applied to a “standard” Arctic atmosphere and surface boundary conditions as prescribed by the sea ice radiative transfer model. A series of sensitivity studies helps assess differences between top-of-the-atmosphere and true surface reflectance and has been utilized to derive a look-up table for atmospheric correction of Advanced Very High Resolution Radiometer (AVHRR) data over sediment-laden sea ice surfaces. In particular, the effects of solar elevation, viewing geometry, and atmospheric properties are considered. The atmospheric corrections are necessary for certain geometries and surface types. Large discrepancies between raw and corrected data are particularly evident in the derived coverage of clean ice and ice with small sediment loading.     

Development of a water clear of sea ice detection algorithm from enhanced SeaWinds/QuikSCAT and AMSR-E measurements

We develop and evaluate water clear of sea ice (open water following ice cover) detection algorithms that make use of Scatterometer Image Reconstruction (SIR) SeaWinds/QuikSCAT (QuikSCAT) backscatter (σ°) and Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) brightness temperature (T_B) measurements. Algorithm validation was performed within Canadian Arctic waters using the Canadian Ice Service Digital Archive (CISDA) ice charts, NASATeam ice concentration estimates, extended AVHRR Polar Pathfinder (APP-x) albedo data, RADARSAT-1 imagery, and MODIS imagery. Results indicate that the temporal evolution of QuikSCAT σ°, AMSR-E polarization ratio (PR18), and AMSR-E vertical spectral gradient ratio (GR3618) can detect water clear of sea ice events, however mean differences due to frequency dependent characteristics of the data (spatial resolution; sensitivity to open water) were apparent. All water clear of sea ice algorithms are in good agreement with the timing and clearing patterns given by the CISDA. The QuikSCAT algorithm provided a more representative ice edge and more details on the ice clearing process due to higher spatial resolution, however, transient clearing events were better represented by the AMSR-E PR(18) or (GR3618) algorithm. By exploiting the strengths of each sensor, we found that a QuikSCAT and AMSR-E fused algorithm provide improved open water area estimates by as much as 11%. The fusion of QuikSCAT and AMSR-E PR(18) yielded in the most spatially representative open water detection. The residual surface of the water clear of sea ice algorithms was found to provide another measure of the average September minimum pan-Arctic sea ice extent within 6% of the NASATeam algorithm estimates.     

Impact of sea ice on the retrieval of water-leaving reflectance, chlorophyll a concentration and inherent optical properties from satellite ocean color data

Two physical phenomena by which satellite remotely sensed ocean color data are contaminated by sea ice at high latitudes are described through simulations and observations: (1) the adjacency effect that occurs along sea ice margins and (2) the sub-pixel contamination by a small amount of sea ice within an ocean pixel. The signal at the top of the atmosphere (TOA) was simulated using the 6S radiative transfer code that allows modeling of the adjacency effect for various types of sea ice surrounding an open water area. In situ sea ice reflectance spectra used in the simulations were measured prior to and during the melt period as part of the 2004 Canadian Arctic Shelf Exchange Study (CASES). For sub-pixel contamination, the TOA signal was simulated for various surface reflectances obtained by linear mixture of both sea ice and water-leaving reflectances (ρ_w). The standard atmospheric correction algorithm was then applied to the simulated TOA spectra to retrieve ρ_w spectra from which chlorophyll a concentrations (CHL) and inherent optical properties (IOPs) were derived. The adjacency effect was associated with large errors (> 0.002) in the retrieval of ρ_w as far as 24 km from an ice edge in the blue part of the spectrum (443 nm). Therefore, for moderate to high CHL (> 0.5 mg m^− 3), any pixel located within a distance of ∼ 10-20 km from the ice edge were unreliable. It was also found necessary to consider the adjacency effect when the total absorption coefficient (a_t) was to be retrieved using a semi-analytical algorithm. a_t(443) was underestimated by more than 35% at a distance of 20 km from an ice edge for CHL > 0.5 mg m^− 3. The effect on the retrieval of the particle backscattering coefficient (b_bp) was important only for clear waters (CHL ∼ 0.05 mg m^− 3). In contrast, sub-pixel contamination by a small amount of sea ice produced systematic underestimation of ρ_w in the blue because of incorrect interpretation of enhanced reflectance in the near infrared that is attributed to higher concentrations of atmospheric aerosols. In general, sub-pixel contamination was found to result in overestimations of CHL and a_t, and underestimations of b_bp. A simple method was proposed to flag pixels contaminated by adjacency effect.     

Comparison of Envisat radar and airborne laser altimeter measurements over Arctic sea ice

Sea ice thickness is a crucial, but very undersampled cryospheric parameter of fundamental importance for climate modeling. Advances in satellite altimetry have enabled the measurement of sea ice freeboard using satellite microwave altimeters. Unfortunately, validation of these new techniques has suffered from a lack of ground truth measurements. Therefore, an airborne campaign was carried out in March 2006 using laser altimetry and photo imagery to validate sea ice elevation measurements derived from the Envisat/RA-2 microwave altimeter.We present a comparative analysis of Envisat/RA-2 sea ice elevation processing with collocated airborne measurements collected north of the Canadian Archipelago. Consistent overall relationships between block-averaged airborne laser and Envisat elevations are found, over both leads and floes, along the full 1300 km aircraft track. The fine resolution of the airborne laser altimeter data is exploited to evaluate elevation variability within the RA-2 ground footprint. Our analysis shows good agreement between RA-2 derived sea ice elevations and those measured by airborne laser altimetry, particularly over refrozen leads where the overall mean difference is about 1 cm. Notwithstanding this small 1 cm mean difference, we identify a larger elevation uncertainty (of order 10 cm) associated with the uncertain location of dominant radar targets within the particular RA-2 footprint. Sources of measurement uncertainty or ambiguity are identified, and include snow accumulation, tracking noise, and the limited coverage of airborne measurements.     

南极渔海况基础数据服务系统

针对南大洋的地理特点和南极渔业的发展,结合建立基础数据服务系统的需求,利用ExtJS技术,以Visual Studi0 2008为开发平台,提出基于B/S架构来构建系统,设计了系统的框架、功能和数据库,并且探讨了系统实现过程中的关键技术.最后对南极渔海况基础数据服务系统进行了展示,验证了系统设计方案实施的可行性,结果表明系统具有高度的可用性.     

Evaluation of the HUT modified snow emission model over lake ice using airborne passive microwave measurements

The algorithms designed to estimate snow water equivalent (SWE) using passive microwave measurements falter in lake-rich high-latitude environments due to the emission properties of ice covered lakes on low frequency measurements. Microwave emission models have been used to simulate brightness temperatures (Tbs) for snowpack characteristics in terrestrial environments but cannot be applied to snow on lakes because of the differing subsurface emissivities and scattering matrices present in ice. This paper examines the performance of a modified version of the Helsinki University of Technology (HUT) snow emission model that incorporates microwave emission from lake ice and sub-ice water. Inputs to the HUT model include measurements collected over brackish and freshwater lakes north of Inuvik, Northwest Territories, Canada in April 2008, consisting of snowpack (depth, density, and snow water equivalent) and lake ice (thickness and ice type). Coincident airborne radiometer measurements at a resolution of 80 × 100 m were used as ground-truth to evaluate the simulations.The results indicate that subsurface media are simulated best when utilizing a modeled effective grain size and a 1 mm RMS surface roughness at the ice/water interface compared to using measured grain size and a flat Fresnel reflective surface as input. Simulations at 37 GHz (vertical polarization) produce the best results compared to airborne Tbs, with a Root Mean Square Error (RMSE) of 6.2 K and 7.9 K, as well as Mean Bias Errors (MBEs) of −8.4 K and −8.8 K for brackish and freshwater sites respectively. Freshwater simulations at 6.9 and 19 GHz H exhibited low RMSE (10.53 and 6.15 K respectively) and MBE (−5.37 and 8.36 K respectively) but did not accurately simulate Tb variability (R = −0.15 and 0.01 respectively). Over brackish water, 6.9 GHz simulations had poor agreement with airborne Tbs, while 19 GHz V exhibited a low RMSE (6.15 K), MBE (−4.52 K) and improved relative agreement to airborne measurements (R = 0.47). Salinity considerations reduced 6.9 GHz errors substantially, with a drop in RMSE from 51.48 K and 57.18 K for H and V polarizations respectively, to 26.2 K and 31.6 K, although Tb variability was not well simulated. With best results at 37 GHz, HUT simulations exhibit the potential to track Tb evolution, and therefore SWE through the winter season.     

混合本征模型的多视SAR影像海冰密度检测

目的 SAR影像中像素光谱测度的空间相关性蕴含着海洋表面和海冰更加丰富的空间特性及其变化信息,因此合理建模这种相关性是高分辨率SAR影像海冰精准解译的关键。提出一种利用随机模型及空间统计学测度刻画海冰空间结构的方法。方法 本文首先,在空间统计学框架下,SAR影像被表示为多值Gamma模型和泊松线Mosaic模型线性加权构建的混合模型,其中多值Gamma模型用于描述海洋表面雷达信号背向散射变化的连续性,而泊松线Mosaic模型则用于表征不同类型海冰表面雷达信号背向散射变化的区域性。利用上述混合模型的一阶、二阶变异函数,建模蕴含在SAR影像中海冰空间结构的变化。结果 对RADARSAT-1影像海冰结构建模并反演其密度。实验区域真实海冰密度分别为20%,80%等,运用本文方法反演所得海冰密度与真实海冰密度误差正负不超过10%。结论 本文提出混合本征模型用以刻画SAR强度影像中海冰像素强度变化的空间关系,能够较好地反演Ungava湾海冰密度分布。为利用遥感影像检测空间机构提供一种全新的方法。     

Some active and passive microwave signatures of Antarctic sea ice from mid-winter to spring 1991

Antarctic sea ice is often covered by a deep snow layer which acts as an emitter and a scatterer to microwave radiation leading to possible misinterpretations of ice signatures, particularly at high frequencies. The algorithms for ice identification, based on the observations of the Special Sensor Microwave Imager, at 19GHz (vertical and horizontal polarizations) and 37Ghz (vertical polarization), have proven to be inefficient for distinguishing new and old ice over the Antarctic Ocean. At an equivalent resolution and analysed on a weekly basis, complementary information can be obtained from active microwave measurements provided, at 5·3GHz (vertical polarization), by the Active Microwave Instrument, the scatterometer of ERS–1. Based on data obtained from the end of August to the end of November 1991, during the austral winter and spring radar backscatter is analysed as a function of the incidence angle. At low incidence angles, the derivative of the backscatter is closely related to the water concentration as derived from passive radiometry, whereas, at high incidence angles, the backscatter is mainly due to ice, as the water contribution is strongly reduced. During the whole period, stable features are apparent on the images obtained from the backscattering coefficients at 50°. On those images, higher values characterize the marginal ice zone, the polynya areas and the advected ice within the Ross Sea. At high incidence angles, the strong signatures of deformed/ rough ice depart significantly from the information classically extracted from the radiometers, the brightness temperatures as well as the derived products, polarization, spectral gradient ratios and concentration. It is therefore possible to classify the Antarctic ice cover into geographical clusters where the active microwave signatures can be attributed. to a peculiar ice type. Though those clusters are not totally identified, their stability and the coherence of their patterns show that they are related to geophysical structures. Four backscatter curves, simulating distinct behaviours over the Antarctic region, are proposed for sea water, marginal ice, first-year ice of the inner part of the pack and multi-year ice.