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.     

Joint experiments programme in remote sensing of marine fish resources

A joint experiments programme was undertaken between three government agencies to pool their resources and expertise to develop techniques for ocean-colour sensing. During October, November and December 1981 a remote-sensing experiment was carried out using vessel-based observations and aircraft underflights synchronous to NIMBUS-7 overpasses in coastal waters off Cochin. The area is well known for the abundance of surface schools of oil sardine (Sardinella hngiceps Val) and mackerel (Rastrelliger kanagurta). An aircraft sensor (Ocean Colour Radiometer) with spectral channels similar to that of the Coastal Zone Colour Scanner (CZCS) on board the NIMBUS-7 satellite was flown to collect data at different altitudes to examine the aspects of atmospheric correction and developing a pigment algorithm. From the analysis of the data it was found that (i) vessel-based measurement of upwelling/downwelling irradiance and the derivation of the spectral diffuse attenuation coefficient and reflectance factor is useful in understanding the optical properties of ocean water, (ii) upwelled radiance data derived from the aircraft sensor could be suitably used in the understanding of atmospheric effects and in developing a pigment algorithm and (iii) a non-linear relationship is observed between fish catch and pigment concentration.     

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.     

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%.     

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.     

Remote sensing of phytoplankton An attempt from the Landsat Thematic Mapper

Abstract

An atmospheric correction scheme using Landsat Thematic Mapper (TM) bands 1, 2 and 4 was developed and the data corrected for atmospheric effects due to Rayleigh scattering and aerosols. Secondly phytoplankton pigment mapping was achieved through substitution of corrected radiances in TM bands 1 and 2 into a bio-optical algorithm developed for the study area off Azhikal in the Arabian Sea. A C map showing near-surface concentrations of phytoplankton pigments (mgm_?3)and a K map showing the pigment distribution (m?1) in one attenuation length were generated.     

Studies of the Antarctic sea ice edge and ice extent from satellite and ship observations

Passive microwave-derived ice edge locations in the Antarctic are assessed against in situ observations from ships between 1989 and 2000. During the growth season (March-October), the ship data agrees with satellite data very well, with r² values of 0.99 and 0.97 for the Bootstrap and Team algorithms, respectively. During the melt season (November-February), the agreement is not so good with the passive microwave ice edge typically 1-2° of latitude south of the observations. This is due to the low concentration and saturated nature of the ice, and the r² values for this period are 0.92 and 0.80 for the Bootstrap and Team algorithms, respectively. Sensitivity studies show that such an offset in the summer ice edge location can cause significant errors in trend studies of the extent of sea ice cover in the Southern Ocean. The passive microwave ice concentration at the ice edge observed by ship varies greatly, averaging 14% for the Team algorithm and 19% for the Bootstrap. Comparisons between passive microwave data and SAR, Landsat and OLS data during the ice growth season show that while small-scale details in ice edge location are lost, the passive microwave data generally provide good and consistent representation of the higher resolution imagery.     

Regional Classes of Sea Ice Cover in the East Antarctic Pack Observed from Satellite and In Situ Data during a Winter Time Period

Ice concentration data alone are often of limited use in many process, and modeling studies as different ice règimes of approximately 100% concentration can have significantly different heat flux, albedo, and other surface properties. Current ice concentration algorithms perform poorly in regions of predominantly thin or highly fragmented ice, which constitute a significant proportion of the pack in East Antarctica. The impact of the sea ice cover on high latitude air–sea interactions and marine ecology depends not only on ice extent and concentration but also on the ice-type composition of the pack. An unsupervised ice classification scheme, using data from four channels of the SSM/I, is presented and tested as a means of gaining important additional, complementary information on surface type. Class interpretation is by comparison with AVHRR, ERS-1 SAR, and near-coincident digital aerial photography and in situ data. The classification does a reasonable job at consistently differentiating the large-scale constituent règimes, including the outer marginal ice zone, the interior pack, and a transition zone separating the two. Given the short period of data analyzed, the cluster maps appear to be generally coherent and consistent through time as the pack changes in response to synoptic-scale atmospheric forcing, although the robustness of the technique needs further testing over longer time periods. An observed crossover in the meridional brightness temperature profiles is a dominant and consistent feature which marks the transition from unconsolidated and wet ice in the marginal ice zone to more consolidated ice with a thicker and drier snow cover in the interior pack. Ambiguities occur at the boundaries of some of these règimes due to sensor resolution limitations and the mixing of different ice types and open water. Also, some ice classes (like brash ice) cannot be distinguished from forming pancake ice. Furthermore, although the signature of the inner pack is usually distinct from that of the outer pack, we observed one extraordinary swell propagation event which led to ice fracturing and surface wetting, and significantly altered the surface classification. The results of this multiparameter study underline the importance of using multisensor systems synergistically to improve interpretation of passive microwave data and better characterize the complex Antarctic pack.     

Analysis of atmosphere-sea ice interactions in the Arctic Basin using ESMR microwave data†

Abstract

Data from the Electrically Scanning Microwave Radiometer (ESMR) on NIMBUS-5 are used to determine the spatial and temporal patterns of change in microwave brightness signatures of Arctic sea ice during a full annual cycle (1973/74). Interactions of ice conditions with the atmosphere are examined using grid point data for surface air temperature and atmospheric pressure. Principal components analysis is used to examine the major elements present in the microwave and atmospheric data. Component scores from these analyses are then used in a canonical correlation analysis to determine interassociations present between the ice and atmosphere in the Beaufort Sea and the European sectors on a synoptic time scale. The synoptic weather conditions associated with the pattern of snow melt on the ice in spring 1974 are described, and a clarification of possible alternative interpretations of features identified as polynyi occurring at 80-85° N. during the late summer 1974 is presented.     

Aerosol optical depth retrieval over snow using AATSR data

Aerosol observations over the Arctic are important because of the effects of aerosols on Arctic climate, such as their direct and indirect effects on the Earth's radiation balance and on snow albedo. Although information on aerosol properties is available from ground-based measurements, passive remote sensing using satellite measurements would offer the advantage of large spatial coverage with good temporal resolution, even though, due to light limitations, this is only available during the Arctic summer. However, aerosol optical depth (AOD) retrieval over the Arctic region is a great challenge due to the high reflectance of snow and ice and due to the high solar zenith angle. In this article, we describe a retrieval algorithm using Advanced Along-Track Scanning Radiometer (AATSR) data, a radiometer flying on the European Space Agency (ESA) Environmental Satellite (ENVISAT), which offers two views (near nadir and at 55° forward) at seven wavelengths in the visible thermal-infrared (VIS-TIR). The main idea of the Dual-View Multi-Spectral (DVMS) approach is to use the dual view to separate contributions to reflectance measured at the top of the atmosphere (TOA) due to atmospheric aerosol and the underlying surface. The algorithm uses an analytical snow bidirectional reflectance distribution function (BRDF) model for the estimation of the ratio of snow reflectances in the nadir and forward views, as well as an estimate of the atmospheric contribution to TOA reflectance obtained using the dark pixel method over the adjacent ocean surface, assuming that this value applies over nearby land surfaces in the absence of significant sources across the coastline. An iteration involving all four AATSR wavebands in the visible near-infrared (VIS-NIR) is used to retrieve the relevant information. The method is illustrated for AATSR overpasses over Greenland with clear sky in April 2009. Comparison of the retrieved AOD with AErosol Robotic Network (AERONET) data shows a correlation coefficient of 0.75. The AODs retrieved from AATSR using the DVMS approach and those obtained from AERONET data show similar temporal trends, but the AERONET results are more variable and the highest AOD values are mostly missed by the DVMS approach. Limitations of the DVMS method are discussed. The pure-snow BRDF model needs further correction in order to obtain a better estimation for mixtures of snow and ice.     

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.     

Satellite observations of optical and biological properties in the Korean dump site of the Yellow Sea

With the standard near-infrared (NIR) atmospheric correction algorithm for ocean color data processing, a high chlorophyll-a concentration patch was consistently observed from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua platform in the middle of the Yellow Sea during the spring (end of March to early May). This prominent patch was not observed in the historical ocean color satellite imageries in late 1970s to early 1980s, and a location corresponding to this patch has been used as a Korean dump site since 1988. At the same time, MODIS chlorophyll-a concentrations derived using the shortwave infrared (SWIR) atmospheric correction algorithm developed for the ocean color satellite data in turbid coastal or high-productive ocean waters were significantly reduced.Comparison between in situ and MODIS chlorophyll-a measurements shows that the chlorophyll-a from the MODIS-Aqua products using the standard-NIR atmospheric correction algorithm is significantly overestimated. The images of the MODIS-derived normalized water-leaving radiance spectra and water diffuse attenuation coefficient data using the NIR-SWIR-based atmospheric correction approach show that absorption and scattering by organic and inorganic matter dumped in the Korean dump site have strongly influenced the satellite-derived chlorophyll-a data. Therefore, the biased high chlorophyll-a patch in the region is in fact an overestimation of chlorophyll-a values due to large errors from the standard-NIR atmospheric correction algorithm. Using the NIR-SWIR algorithm for MODIS-Aqua ocean color data processing, ocean color products from 2002 to 2008 for the Korean dump site region have been generated and used for characterizing the ocean optical and biological properties. Results show that there have been some important changes in the seasonal and interannual variations of phytoplankton biomass and other water optical and biological properties induced by colored dissolved organic matters, as well as suspended sediments.     

暗原色先验单幅图像去雾改进算法

目的为解决传统基于暗原色先验的单幅图像去雾算法实现效率低以及恢复雾化图像在天空、白云等明亮区域颜色失真的不足,提出一种改进算法。方法通过分块思想,完成透射率的空间自适应估计;通过判断大气光强度和暗通道差值绝对值大小来判断雾图中是否含有明亮区域。结果该算法不仅降低了传统算法的时间复杂度,而且弥补了传统算法在明亮区域透射率估计的不足。结论实验结果表明该改进算法可行、有效。     

Algorithm for atmospheric corrections of aircraft and satellite imagery

Abstract

An algorithm is described for making fast atmospheric corrections. The required radiation parameters are stored in a lookup table. The procedure is to enter the lookup table with the measured radiance, wavelength, view and illumination directions, heights of observation and surface, and the aerosol and gaseous absorption optical thicknesses. The surface radiance, the irradiance incident on a surface, and surface reflectance are computed then. Alternately, the program will compute the upward radiances at specific altitudes for a given surface reflctance, view and illumination directions, and aerosol and gaseous absorption optical thicknesses.     

Interpretation of Seasat radar-altimeter data over sea ice using near-simultaneous SAR imagery

Abstract

The backscatter properties of Seasat altimeter data in the Beaufort Sea on the 3 October 1978 show distinct zones, which arc interpreted in terms of geophysical characteristics. An overlapping and near-simultaneous synthetic-aperture radar image shows regions of open water, new ice and multi-year sea ice, which correspond to the different zones. It is found that the altimeter signal is sensitive to the ocean-ice boundary and that it indicates the ice type. The pulse-echo waveforms also suggest that several scattering components are present in the returned power over sea ice.     

Remote sensing of aerosols over the oceans using AVHRR data Theory,practice and applications

Abstract

The basic features of single- and dual-channel aerosol retrieval algorithms based on matching radiances measured in AVHRR channels -1 (～0.58-0.68 μm) and -2( ～0.73-1.10μm) with model computations will be described. The use of the NOAA/NESDIS single-channel algorithm will be illustrated with examples of detection and mapping of enhanced atmospheric turbidity over the oceans. The effects of variations in the physical and radiative properties of atmospheric aerosols, and in atmospheric ozone and water vapour, will be briefly discussed in the light of model sensitivity analyses.     

Technical Note Simulation of MEIS-II data on 5S code to quantify algal concentration in an intertidal area

An atmospheric correction algorithm due to Deschamps et al. (1981) has been applied to MEIS-II data. Some atmospheric conditions such as continental and maritime aerosol models have been used in this work. Simulation on 5S code was made with Thematic Mapper Band 4 and Spot band 3. All these pieces of the puzzle permitted the qualification of algal concentration in an intertidal area, which was the aim of this work. Continental or maritime aerosol models gave a similar result due probably to the specific area, located between land and nearshore. Apparent radiance is smaller than corrected radiance because absorption process takes place in this part of the spectrum (0.8 to 0.9μm). When apparent radiance is higher than 30 Wm^?2 sr^?1 μm^?1 algal concentration is overestimated by up to 60 per cent.     

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。     

Satellite remote sensing of atmospheric water vapour

Abstract

The Advanced Very High Resolution Radiometer (AVHRR-2) has two channels in the 10-13/mi window region. These channels are used for remote sensing of the sea surface temperature corrected for atmospheric absorption. The brightness temperature difference between the channels can be directly related to the atmospheric absorption due to water vapour. The problem of water-vapour retrieval from satellite data is examined in detail. The best evaluation of the water-vapour content is obtained from the spectrometric data of the Infrared Interferometer Spectrometer (IRIS), with an error of about + 3kg/m². It is, however, feasible to obtain the water-vapour content from AVHRR data with an algorithm derived from radiative transfer model simulations. The retrieved water vapour has an error of ±5kg/m² when compared with ship data. It is possible to use the remotely sensed water vapour for the inference of the boundary-layer structure. The information is, however, limited for water vapour contained near the surface.