Passive microwave signature observations of the Baltic Sea ice

Passive microwave signatures of various Baltic Sea ice types and open water leads were measured in the spring of 1995 and in March 1997 with airborne non‐imaging microwave radiometers (MWR) operating in the frequency range from 6.8 to 36.5?GHz. The MWR datasets were assigned by video imagery into open water leads and various ice type categories. The ground data provided further classification into dry, moist and wet snow sub‐categories. The datasets were used to study the behaviour of the brightness temperature and polarization ratio as a function of frequency and the degree of ice deformation; additionally, the dimensionality of multichannel datasets, classification of surface types, and suitability of the SSM/I and AMSR‐E data and NASA Team and Bootstrap ice concentration algorithms for the mapping of the Baltic Sea ice were examined. The results indicate that open water leads can be distinguished from sea ice regardless of the snow cover wetness, using even single‐channel MWR data. Classification of ice types is possible only under dry snow condition. Determination of the ice type concentrations from the coarse‐resolution space‐borne MWR data is not feasible, because the mean signatures for various ice types are very close to each other. The results also suggest that the SSM/I and AMSR‐E data and the NASA Team and Bootstrap algorithms can be used to map total ice concentration after modifications of open water and sea ice reference signatures.     

The modelling of sea ice melt-water ponds for the High Arctic using an Airborne line scan camera,and applied to the Satellite Special Sensor Microwave/Imager (SSM/I)

Melt-water ponds on sea ice in the Northeast Water Polynya (77-82 N, 1-18 W) were mapped using a line scan camera (LSC) mounted on a helicopter. Passive microwave satellite data from the Special Sensor Microwave/ Imager (SSM/I) were employed to analyse the temporal trend of radiances of shorefast ice for 1993 and sea ice during sixteen flights of the LSC (June-July). A simple, linear algorithm tailored to accommodate the summer ice regime, was developed. The LSC measurements of ice (50.9 12.5%), water and melt-water pond fractions compared very well with the SSM/I derived mean ice concentrations (50.9 12.8%). The comparison resulted in a correlation coefficient of 0.953. Combining the LSC melt-water pond fraction data with other data available from the literature provided the basis to construct a second degree polynomial function of a melt-water empirical model to correct the under estimation of SSM/I derived sea ice concentration due to the effect of melt-water ponds.     

Estimation of thin ice thickness from AMSR-E data in the Chukchi Sea

In this study, we have developed an algorithm for estimating thin ice thickness in the Chukchi Sea of the Arctic Ocean using Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E) data. The algorithm is based on comparisons between the polarization ratio (PR) of AMSR-E brightness temperatures from the 89 and 36 GHz channels (PR₈₉ and PR₃₆) and the thermal ice thickness. The thermal ice thickness is estimated from a heat budget calculation using the ice surface temperature from clear-sky Moderate-Resolution Imaging Spectroradiometer (MODIS) infrared data. Whereas coastal polynyas have been the main target of previous algorithms, this algorithm is also applicable for marginal ice zones. AMSR-E has twice the spatial resolution of Special Sensor Microwave/Imager (SSM/I) data and can therefore resolve polynyas at a smaller scale. Although the spatial resolution of the 89 GHz data (6.25 km) is twice that of the 36 GHz data (12.5 km), the 89 GHz data can be contaminated by atmospheric water vapour. We propose an exclusion method of data affected by water vapour to resolve this issue. A combined algorithm of thin ice and ice concentration is also discussed, in which the ice thickness can be estimated independently from the open water fraction in grid cells with less than 100% ice concentration. The PR–thickness relationship in this study is somewhat different from previous studies, which is likely due to the difference in prevailing ice types caused by background environmental conditions.     

遥感技术监测海冰密集度

概要评述可见近红外、主/ 被动微波遥感技术监测海冰密集度的基本原理、算法及其优缺点。着重介绍和讨论被动微波传感器SMMR 和SMM/ I 遥感图像混合像元内海冰总密集度, 一年海冰及多年海冰密集度的NASA 算法及其天气滤波器。     

A comparison between SSM/I passive microwave melt onset dates and satellite-derived albedo melt onset dates in the Arctic

Arctic sea ice undergoes a very strong annual cycle. This study sets out to look at the transition when the Arctic sea ice starts to melt using satellite-obtained passive microwave brightness temperatures and satellite-derived albedo data for 13 points within the Arctic, including both first-year and multiyear ice locations, for 1995–2000. Special sensor microwave imager (SSM/I) brightness temperature differences are used to determine melt onset dates once surface temperatures approach freezing. Independently, satellite-derived albedo data are obtained and a melt onset date is derived. Generally, the two methods produce the same date for melt onset with optimum conditions. However, in most cases there are clouds present, which for the albedo data restrict observations and generate melt dates that are several days later than the passive microwave melt onset which is not affected by cloud cover. Melt onset dates, determined from the passive microwave brightness temperatures, are compared to those from the albedo observation to determine differences between the two methods. For first-year ice (FYI) locations, the average differences in melt onset dates for the study locations between the passive microwave and albedo-derived methods are +/?3 days. The average difference for multiyear ice (MYI) locations melt onset dates is around 8 days, slightly longer than the (FYI) locations, however, this is due to more cloudy conditions. The results indicate that the passive microwave-derived melt onset dates and albedo-derived dates are very close and either method could be used to determine melt. The advantage of using microwave data would be the independence of having to have cloud free conditions.     

Monitoring sandstorms and desertification in northern China using SSM/I data and Getis statistics

A massive sandstorm enveloped most of northern China during Spring 2002. Monitoring the evolution of sandstorm and desertification has become one of the most serious problems for China's environment. Since 1989, one of the most advanced and operational passive microwave sensors is the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave Imager (SSM/I) operated at seven channels (19, 37, 85?GHz with vertical and horizontal polarization and 22?GHz with vertical polarization only). In this paper, the sandstorm and desertification indexes, SDI and DI, are derived from the radiative transfer equation, and are employed with multi-channel measurements of the DMSP SSM/I for monitoring the sandstorm and desertification in northern China. Some SSM/I data in 1997 and 2001 are employed. The algorithm of the Getis statistics is developed to categorize the spatial correlation and its evolution during these days. It is demonstrated that the SSM/I indexes, SDI and DI, and its Getis statistics are well applicable for monitoring the sandstorm and desertification.     

Simultaneous MSMR and SSM/I observations and analysis of sea‐ice characteristics over the Antarctic region

Polar sea ice has been monitored quasi‐continuously over the last 30 years using passive microwave radiometers onboard three satellites in polar orbit, namely Nimbus‐5, Nimbus‐7 and Defense Meteorological Satellite Program (DMSP) series. A short overlap between Scanning Multichannel Microwave Radiometer (SMMR) on Nimbus‐7 and Special Sensor Microwave Imager (SSM/I) onboard DMSP allowed inter‐calibration of the two sensors leading to a consistent series of long‐term sea‐ice measurements since 1978. With the launch of Multifrequency Scanning Microwave Radiometer (MSMR) onboard OCEANSAT‐1 in the polar sun‐synchronous orbit during 1999, India developed the capability to monitor the polar sea ice on a regular basis. The concurrent availability of SSM/I and MSMR over the last few years presents a valuable opportunity to attempt an inter‐comparison of MSMR with SSM/I measurements and derived sea‐ice parameters.

In this paper, we present an indirect validation of the brightness temperatures (T _b) observed by MSMR with near‐simultaneous measurements from SSM/I over the Antarctic and Southern Polar Ocean regions. Simultaneous MSMR and SSM/I data from two contrasting seasons—summer and winter—for the 1999–2000 period have been used. Analysis includes a comparison of T _b scatterograms to achieve confidence in the quantitative use of the T _b data to derive various geophysical parameters, e.g. sea‐ice concentration and extent. Additionally, the T _b images produced by the two sensors are compared to establish the capability of MSMR in reliable two‐dimensional portrayal of all the sea and continental ice features over the Antarctic Region. Based on a regression analysis between MSMR observed T _b in different frequency channels and polarizations, and SSM/I‐derived sea‐ice concentration (SIC) values, we have developed algorithms to estimate SIC over the Southern Polar Ocean from MSMR data. The MSMR algorithms allow estimation of SIC with better than 10% rms error. MSMR SIC images faithfully capture the observed distribution of sea ice in all the sectors of the Southern Ocean both during summer and winter periods. Using the MSMR‐derived SIC, we have also derived monthly sea‐ice extent (SIE) estimates for a period extending for about 20 months from the beginning of the launch of MSMR. These estimates show excellent agreement with values derived from SSM/I. These analyses bring out the very high level of compatibility in the measurements and derived sea‐ice parameters produced by the two sensors.     

SMMR与SSM/I被动微波亮度温度数据交叉定标

过去30 a星载微波辐射计（SMMR和SSM/I）长时间序列的被动微波亮度温度数据,在陆地表层系统科学以及气候变化研究中起到了非常重要的作用。由于卫星及其携带的微波辐射计的更新,不同传感器所测得的同一地物在同一时间的亮温存在不同程度的偏差,通过分析相邻传感器重复观测时期同一地表18/19GHz和37GHz水平和垂直极化的亮度温度,并以DMSP的F13卫星上的SSM/I传感器为标准,建立了4个通道的交叉定标系数。      

On sea ice drift from the wavelet analysis of the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave Imager (SSM/I) data

Wavelet analysis of DMSP SSM/I 85GHz radiance data is used to obtain daily sea ice drift information for both the northern and southern polar regions. This technique provides improved spatial coverage over the existing array of Arctic Ocean buoys and better temporal resolution over techniques utilizing data from satellite synthetic aperture radars. Examples of derived ice-drift maps for both hemispheres illustrate large-scale circulation reversals over a period of one month. Comparisons with ice displacements derived from buoys give good quantitative agreement.     

Simulation of a multi-layer model of dense scatterers for anomalous scattering signatures from SSM/I snow data

Numerical simulation of coupled vector radiative transfer equations for a multi-layer model of dense scatterers is developed. The effect from ice layer formed during melting is studied to take account of anomalous spectrum of brightness temperature at the SSM/I channels. This approach is applied to analysis of SSM/I data observed over the southern and central Greenland snow, which can show distinctly different spectral characteristics, T B19 > T B85 or T B19 < T B85. Ice layers produced in melting and ice grain sizes can significantly affect functions of thermal emission at SSM/I channels.     

Sea ice small-scale surface roughness estimation based on AMSR-E observations

In this article, the retrieval of a sea ice small-scale surface roughness parameter using a proposed model is investigated at several Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E) channels (6.9, 10.7, and 89 GHz) over the Arctic oceans. The AMSR-E 89 GHz observations with a spatial resolution of approximately 6 km?× 4 km, nearly three times the resolution of the currently operational radiometer SSM/I 85 GHz (15 km?× 13 km), are fully exploited to retrieve the total and multiyear (MY) ice concentrations through the utilization of the ARTIST sea ice (ASI) and polarization corrected temperature (PCT) algorithms, respectively. To improve the accuracy of the retrieved ice concentration, a tie-point adaption scheme was used to obtain daily adaptable tie-points for the two ice concentration algorithms. A sea ice small-scale roughness parameter was then calculated with the model proposed by Hong for the above-mentioned three frequencies. At lower frequencies, such as 6.9 and 10.7 GHz, roughness estimates are available for all ice types. However, estimates at 89 GHz are physically illegitimate over the wintertime MY ice cover. The model estimates at the two low frequencies were further studied over a protracted period (2003–2010). The annual time series of the averaged estimate over the Arctic sea ice were found to exhibit a slightly decreasing trend (?2.1 × 10^?3 and??1.9 × 10^?3 cm year^?1 for 6.9 and 10.7 GHz, respectively). Meanwhile, the winter time series showed an increasing trend whereas the summer time series showed a remarkably decreasing trend, which indicates more serious melting activity occurring over the Arctic ice.     

Effects of atmospheric water and surface wind on passive microwave retrievals of sea ice concentration: a simulation study

The atmospheric effects on the retrieval of sea ice concentration from passive microwave sensors were examined using simulated data typical for the Arctic summer. The simulation includes atmospheric contributions of cloud liquid water (CLW), water vapour (WV) and surface wind on the microwave signatures. A plane parallel radiative transfer model was used to compute brightness temperatures at SSM/I frequencies over surfaces that contained open water, first‐year (FY) ice, multi‐year (MY) ice and their combinations. Synthetic retrievals were performed using the NASA Team (NT) algorithm for the estimation of sea ice concentrations. Our results show that if the satellite sensor's field of view is filled with only FY ice, the retrieval is hardly affected by the atmospheric conditions because of the high contrast between emission signals from the FY ice surface and the atmosphere. Pure MY ice concentration is generally underestimated because of the low MY ice surface emissivity, which results in the enhancement of emission signals from the atmosphere. In marginal ice areas, the atmospheric and surface effects tend to degrade the accuracy at low sea ice concentrations. FY ice concentration is overestimated and MY ice concentration is underestimated in the presence of atmospheric water and surface wind at low ice concentration. Moreover, strong surface wind appears to be more important than atmospheric water in contributing to the retrieval errors of total ice concentrations over marginal ice zones.     

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.     

Remotely-sensed and simulated variability of Arctic sea-ice concentrations in response to atmospheric synoptic systems

Responses of the Beaufort Sea and Canada Basin icc pack to the passage of synoptic-scale weather systems are studied using Synthetic Aperture Radar (SAR), Advanced Very High Resolution Radiometer (AVHRR) and Special Sensor Microwave/Imager (SSM/I) data combined with ice model simulations. Changes within the consolidated ice pack are examined in detail for October 1991. The analysis is then extended to consider general conditions from October 1991 to June 1992. The SAR, SSM/I, and modelled concentrations concur generally, showing a 1–5 per cent decrease in ice fraction during the passage of low-pressure systems through the study area. The AVHRR imagery indicates a greater proportion of thin ice within the pack, but comparable decreases in concentration. While changes in SSM/I-derived open-water fractions are similar to changes in the other data sets, the SSM/I data suggest substantial increases in first-year ice concentration, indicative of the formation of refreezing open water areas. Sensible heat fluxes calculated using open-water and ice-type fractions from the SAR and SSM/I imagery point out the sensitivity of heat transfer estimates to, the data types and classification method used to derive ice information.     

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.     

Artificial neural network-based techniques for the retrieval of SWE and snow depth from SSM/I data

The retrieval of snow water equivalent (SWE) and snow depth is performed by inverting Special Sensor Microwave Imager (SSM/I) brightness temperatures at 19 and 37 GHz using artificial neural network ANN-based techniques. The SSM/I used data, which consist of Pathfinder Daily EASE-Grid brightness temperatures, were supplied by the National Snow and Ice Data Centre (NSIDC). They were gathered during the period of time included between the beginning of 1996 and the end of 1999 all over Finland. A ground snow data set based on observations of the Finnish Environment Institute (SYKE) and the Finnish Meteorological Institute (FMI) was used to estimate the performances of the technique. The ANN results were confronted with those obtained using the spectral polarization difference (SPD) algorithm, the HUT model-based iterative inversion and the Chang algorithm, by comparing the RMSE, the R², and the regression coefficients. In general, it was observed that the results obtained through ANN-based technique are better than, or comparable to, those obtained through other approaches, when trained with simulated data. Performances were very good when the ANN were trained with experimental data.     

Comments on 'Monitoring of brightness temperature over India and adjoining regions using SSM/I data' by Kumar et al. (1999)

Kumar et al. (1999) present seasonally averaged 85 GHz horizontal polarization brightness temperature (T_b ) maps of the Indian region, based on data from the passive microwave imager SSM/I onboard DMSP satellites. They describe spatial variations of observed T _b during three different seasons. Their attempt to relate these variations, more or less directly, in terms of the 'physiographic' and 'geological' properties of the Indian landmass, and in terms of the influence of inflow of water from land to ocean, without properly accounting for atmospheric contributions, is misleading.     

A comparison of 18 winter seasons of in situ and passive microwave-derived snow water equivalent estimates in Western Canada

The Meteorological Service of Canada (MSC) has developed an operational snow water equivalent (SWE) retrieval algorithm suite for western Canada that can be applied to both Scanning Multichannel Microwave Radiometer (SMMR) and Special Sensor Microwave/Imager (SSM/I) data. Separate algorithms derive SWE for open environments, deciduous, coniferous, and sparse forest cover. A final SWE value represents the area-weighted average based on the proportional land cover within each pixel. The combined SSM/I and SMMR time series of dual polarized, multichannel, spaceborne passive microwave brightness temperatures extends back to 1978, providing a lengthy time series for algorithm assessment. In this study, 5-day average (pentad) passive microwave-derived SWE imagery for 18 winter seasons (December, January, February 1978/79 through 1995/96) was compared to SWE estimates taken from a distributed network of surface measurements throughout western Canada.Results indicated both vegetative and snowpack controls on the performance of MSC algorithms. In regions of open and low-density forest cover, the in situ and passive microwave SWE data exhibited both strong agreement and similar levels of interannual variability. In locations where winter season SWE typically exceeded 75 mm, and/or dense vegetative cover was present, dataset agreement weakened appreciably, with little interannual variability in the passive microwave SWE retrievals. These results have important implications for extending the SWE monitoring capability of the MSC algorithm suite to northern regions such as the Mackenzie River basin.     

Atmospheric effects on SMMR and SSM/1 37 GHz polarization difference over the Sahel

Abstract

The difference of vertically and horizontally polarized brightness temperatures (referred to here as the polarization difference, δT) observed at 37GHz frequency of the scanning multi-channel microwave radiometer (SMMR) on board the Nimbus-7 satellite and special sensor microwave imager (SSM/I) on board the DMSP-F8 satellite could provide useful information about land surface change within the span of these global observations, November 1978 to August 1987 for SMMR and July 1987 to present for SSM/I. The atmospheric effects on the δT are studied over two 2-5° by 2-5° regions within the Sahel and Sudan zones or Africa from January 1985 to December 1986 through radiative transfer analysis using surface temperature, atmospheric water vapour and cloud optical thickness developed under the International Satellite Cloud Climatology Project (ISCCP). The atmospheric effects are also studied using surface observations of air temperature and vapour pressure at Niamey (13-5° N, 2-2° E) for the period January 1979 to December 1990. It is found that atmospheric effects alone cannot explain the observed temporal variation of δT, although the atmosphere introduces important modulations on the observed seasonal variations of δT due to rather significant seasonal variation of precipitable water vapour. Therefore, these δT data should be corrected for atmospheric effects before any quantitative analysis of land surface change over the Sahel and Sudan zones. The entire global data set from December 1978 to December 1990 has been archived for unrestricted distribution and use.     

Investigation of effect of heterogeneities in vegetation and rainfall on simulated SSM/I brightness temperatures

Land surface characteristics: soil and vegetation and rainfall inputs are distributed in nature. Representation of land surface characteristics and inputs in models is lumped at spatial scales corresponding to the grid size or observation density. Complete distributed representation of these characteristics or inputs is infeasible due to excessive computational costs or costs associated with maintaining dense observational networks. The measurements of microwave brightness temperatures by the SSM/I (Special Sensor Microwave Imager) are at resolutions of the order of 56km 56km for 19 GHz and 33 km 33 km for 37 GHz. At these resolutions, soil moisture and vegetation are not homogeneous over the measurement area. The experiments carried out in this study determine the effect of heterogeneities in vegetation (leaf area index) and input rainfall on simulated soil moisture and brightness temperatures and the inversion of brightness temperatures to obtain soil moisture estimates. This study would help us to understand the implications of using the SSM/I microwave brightness temperatures for soil moisture estimation. The consequences of treating rainfall inputs and vegetation over large land surface areas in a lumped fashion is examined. Simpler methods based on dividing the leaf area index or input rainfall into classes rather than explicit representation for representing heterogeneities in leaf area index and spatial distribution of rainfall is tested. It is seen that soil moisture is affected by the representation (lumped vs distributed) of rainfall and not leaf area index. The effect of spatially distributed soil moisture on the inversion of observed SSM/I brightness temperatures to obtain soil moisture estimates is investigated. The inversion process does not exhibit biases in the retrieval of soil moisture. The methodology presented in this paper can be used for any satellite sensor for purposes of analysis and evaluation.