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.     

SAR observations of Arctic freeze-up compared to SSM/I during ARCTIC'91

Ice concentration in the Arctic derived from ERS-1 Synthetic Aperture Radar (SAR) and Special Scanning Microwave/Imager (SSM/I) images are compared. The satellite data are compared to video images and in situ measurements. The data were acquired during the freeze-up period of the ARCTIC'91 expedition. The studied areas were characterized by melting conditions and new ice formation with frost flowers. The ERS-1 SAR images are classified by a local averaging method and a segmentation method. Parameters for the methods are derived from the backscattering distributions. Temporal sequences and meteorological information are used for consistent results. Ice concentration derived from SAR are compared with the SSM/I ice concentration (NASA team algorithm) and ship observations. SSM/I may underestimate the ice concentration by 20 per cent due to thin ice formation and melting conditions while SAR may overestimate. However, by using the SAR estimate of the different ice classes we believe it is possible to increase the accuracy of the NASA team algorithm. We conclude that it is important to compare results from different sensors and methods.     

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.     

Comparison of aerial video and Landsat 7 data over ponded sea ice

The development of melt ponds on Arctic sea ice during spring and summer is of great importance to the Arctic climate system as it accelerates the decay of the sea ice and greatly reduces the albedo. Both melt pond development and its spatial distribution are needed to understand the surface energy balance in summer. Previously, a technique was developed for classifying summer sea ice characteristics, including the amount of open water, white (snow-covered) ice, wet ice, and melt ponds using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) spectral information. In this paper, we refine this technique through the use of airborne video data coincident with Landsat ETM+ imagery obtained over Baffin Bay on June 27, 2000. The video images, having a resolution of about 1.5 m at an aircraft altitude of 1.4 km, are classified into open water, ponded or wet ice, and unponded sea ice. Comparison of the video and Landsat imagery shows that many of the melt ponds are too small to cover an entire Landsat pixel (resolution of 30 m) so that the Landsat classification scheme would underestimate melt pond fraction. Thirteen high-resolution video images are classified to develop a method to calculate fractions of open water, ponded or wet ice, and unponded ice from Landsat 7 data. A comparison between these classified video images and Landsat retrievals yields a correlation coefficient of 0.95 with rms errors of less than 9% for the two ice types and 2% for open water. Comparisons of Landsat and video analyses not used in the development of the algorithm yield correlation coefficients of 0.87 for open water, 0.68 for ponded ice, and 0.78 for unponded ice. The rms differences are 10%, 8%, and 11%, respectively.     

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.     

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.     

Multi-year sea ice mapping by thermal infrared radiometry

Marine operations in polar and subpolar regions rely on accurate sea ice information for operational planning purposes. Before venturing into operations, however, mapping of the prevailing sea ice conditions are important to feasibility analyses and planning of the operation. Multi-year sea ice information is often derived from passive microwave radiometers such as the Special Sensor Microwave Imager (SSM/I) on board the U.S. Defense Meteorological Satellite Program (DMSP) satellites, or the Scanning Multichannel Microwave Radiometer (SMMR) on board the Nimbus-7 satellite. These sources provide wide aerial coverage and all weather capability, but offer only low spatial resolution, 30 km. In contrast, the thermal infrared channel of the Advanced Very High Resolution Radiometer (AVHRR) on board the NOAA satellites, provides a 1 1km spatial resolution at nadir with a reasonable cost. A technique for extraction of multi-year sea ice information from thermal infrared AVHRR data was thus created. It relies on surface temperature differences between first-year and multi-year sea ice. Adjustments of the absolute concentration levels were made based on a regression relation between AVHRR and SMMR based multi-year sea ice concentrations. The technique is inapplicable during periods of dark, cloud cover, or melting conditions.     

The physical,radiative and microwave scattering characteristics of melt ponds on Arctic landfast sea ice

Melt ponds are an important characteristic of Arctic sea ice because of their control on the surface radiation balance. Little is known about the physical nature of these features and to date there is no operational method for detection of their formation or estimation of their aerial fraction. Coincident in situ observations, aerial surveys and synthetic aperture radar data from a field site in Arctic Canada are compared in an evaluation of the physical, radiative and electrical properties of melt ponds on first-year and multiyear sea ice. Results show that the interrelationships between the thermal diffusivity and conductivity of the snow cover control the mechanisms of snow ablation. Aerial fractions of snow patches, and light and dark coloured melt ponds, show considerable variation both as a function of proximity to land and due to ice type. First-year sea ice is shown to have a water background with discrete snow patches distributed throughout. Multiyear sea ice consists of discrete 'particles' within a snow background. Morphological measurements indicate that snow patches range in size with average areas of from 5 to 20m2 . Pond sizes over multiyear sea ice are also highly variable with averages ranging from 15 to 20m2. The integrated shortwave albedo was measured in the field and averaged to: snow patches (0.64 0.07); light melt ponds (0.29 0.04); and dark melt ponds (0.14 0.03). Snow patch size statistics explained a statistically significant proportion of the surface shortwave albedo. We found that microwave scattering could be used to obtain a measure of the onset ofmelt and had utility in detecting subtle details ofthe thermodynamic transition from winter through early melt into pond formation. We formalized a statistical relationship between microwave scattering and surface climatological albedo (sigma-alpha relationship). We found the relationship valid only for landfast firstyear sea ice under windy conditions. We conclude with a discussion of the role of surface wind stress and diurnal cycling in specification of the sigma-alpha relationship.     

Inter‐annual variability of chlorophyll and sea‐ice in the Antarctic Divergence region: an attempt to derive their quantitative relationship

Inter‐annual variability in chlorophyll a (chl a) and sea‐ice concentration in the Antarctic Divergence (AD) region near 140°?E was examined using satellite sensor data in order to derive a quantitative relationship between the two. Sea‐viewing Wide Field‐of‐view Sensor (SeaWiFS) derived chl a and Special Sensor Microwave/Imager (SSM/I) derived sea‐ice concentration data during 1997–2003 were analysed. Mean chl a concentration south of 64°?S during the period between September and April (from austral spring to autumn) of each year was calculated. The mean chl a in 2001/2002 was extremely high (1.08?mg?m^?3) within the six periods between 1997/1998 and 2002/2003. Simply integrated indices on sea‐ice amount, such as the maximum sea‐ice extent in the winter season, annual integration of coverage area over ice edge to coast, were not able to explain inter‐annual variation of chl a, especially the highest chl a value in 2001/2002. We found a high correlation between the chl a south of 64°?S and the sea‐ice index, which might be due to the surface meltwater of the sea‐ice from the AD zone as a result of eddies. The quantitative relationship might have contributed to the prediction of phytoplankton blooms in this coastal region and demonstrated the impact of the sea‐ice extent on the Antarctic marine ecosystem.     

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.     

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.     

A first try at identification of sea ice using the three beam scatterometer of ERS-1

Regional-scale behaviour of backscattering at C-band is investigated using data of the ERS-1 three-beam scatterometer, the AMI-WIND, during November 1991, over the Arctic ocean. The normalized radar cross-section appears as a linear function of incidence angle, whose two parameters vary considerably between zones of first-year and multi-year sea ice. Once determined the slope parameter of the AMI-WIND data, measured normalized radar cross-sections are corrected to bring them to a single reference incidence angle. False-colour mapping of this variable displays first-year and multi-year sea ice zones as determined previously from several passive micro-wave sensors (ESMR. SSM,/I)     

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 surface features in Arctic summer 2008: Aerial observations

Eight helicopter flights were conducted, and more than 9000 aerial images were obtained during the Third Chinese National Arctic Research Expedition in 2008 in the Pacific Arctic Sector (PAS). Along the cruise tracks between 77°N and 86°N, area fractions of open water and ice cover varied from 0.96 to 0.12 and from 0.03 to 0.81, respectively, while the melt pond fraction varied between 0 and 0.2. The ice concentrations derived from aerial images and the AMSR-E/ASI products were comparable to each other, especially in the range of 50-90%. However, the satellite-derived data overestimated the aerial observations by 14 ± 9% in areas with large ice concentrations (> 90%), and nearly ignored those with very low ice concentrations (< 20%). In addition, a significantly higher amount of melt ponds was observed in the PAS in the summer of 2008 as compared to five years ago. The areally averaged albedo increased from 0.09 in the marginal ice zone at 77°N to 0.63 in the far north zone at 86°N, where the ice concentration was 90%. The albedo was significantly smaller than those reported in earlier studies in the PAS for the same region because of an overall decrease in ice concentration. Compared with 2007 data, the lower ice concentration in 2008 may yield a smaller total ice-covered area, although the Arctic ice extent in 2008 was slightly larger than the record minimum in 2007.     

Atmospheric signatures in sea-ice concentration estimates from passive microwaves: Modelled and observed

The effects of weather systems on sea-ice concentration retrieval are investigated using an advanced radiative transfer model with input data from 155radiosonde ascents together with satellite and ground based observations in the Weddell Sea in 1992. The results of the model study indicate that, using the SSM/I NASA Team algorithm, cloud liquid water increases estimates of total sea-ice concentration by the same magnitude as water vapour, i.e., up to 10 per cent, depending on surface type (open ocean, first-year ice, multiyear ice), and actual concentration. Estimates of the multiyear ice concentration are reduced by up to 80 per cent by cloud liquid water whereas the water-vapour effect is smaller (up to 6 per cent). The combined effect is less than the sum of the two. Calculations using the SMMR sea-ice algorithm were made for comparison with previous estimates by Pedersen and Maslanik. In this case study, estimates of the multiyear fraction show a smaller reduction by water vapour and a larger reduction by cloud liquid water, whereas the total concentration change is in between the two previous results.The algorithm for the SSM/I radiometer exhibits stronger effects on total ice concentration due to water vapour and cloud liquid water than that for SMMR, and atmospheric effects using the future MIMR radiometer sea-ice algorithm will be in between those from SMMR and SSM/I. Different calculated ice-concentration changes for the SSM/I due to different sets of tiepoints (emissivities) can be of the same order of magnitude as the atmospheric effect of cloud liquid water. Comparison between these modelled effects and satellite-derived concentrations from SSM/I shows good geographical and quantitative agreement in areas with extensive frontal water clouds.     

An improved look-up table technique for geophysical parameters from SSM/I

An improved look-up table technique is developed to calculate meteorological parameters from Special Sensor Microwave/Imager (SSM/I) measurements. The method, which is based on a look-up table and an extrapolation and interpolation technique used in the weather prediction model, gives results comparable to or better than the regression method for the total precipitable water (TPW), surface wind speed (V), and cloud liquid water path (LWP). Applied to a noise-free data set (dependent test) TPW, V and LWP are retrieved with a rms. accuracy of 0.26 kg m^-2, 0.28 m s^-1 and 0.002 kg m^-2, respectively. If the random noise of the SSM/I radiometer is taken into account in the retrieval, the r.m.s. increases to 0.84 kg m^-2, 1.08 m s^-1 and 0.013 kg m^-2, respectively. The method is applied to a set of over 520 SSM/I measurements from the DMSP-F8 satellite for which collocated radiosondes launched from ships are available. The rms. (bias) of TPW and V was 2.91 (-0.61) kg m^-2 and 2.75 (-0.13) m s^-1, respectively. We use the improved look-up table technique to calculate the monthly mean global distribution of surface wind for August 1989 and compare the results with the Comprehensive Ocean-Atmosphere Data Set (COADS) for the same month. The rms. error and mean differences for the monthly mean values of sea surface wind speed between the retrievals and COADS data are 1.01 m s^-1 and 0.03 m s^-1, respectively. We also calculate LWP for October 1987 and compare it with the LWP derived from cloud optical thicknesses of International Satellite Cloud Climatology Project (ISCCP) dataset. Good agreement is obtained. The extension of the method to calculate cloud water and water vapour profiles is discussed.     

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.     

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。     

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.