Search

Sea ice roughness can serve as a proxy for other sea ice characteristics such as ice thickness and ice age. Arctic-wide maps that represent spatial patterns of sea ice roughness can be used to better characterize spatial patterns of ice convergence and divergence processes. Sea ice surface roughness can also control and quantify turbulent exchange between sea ice surface and atmosphere and therefore influence surface energy balance at the basin scale. We have developed a data processing system that produces georeferenced sea ice roughness rasters that can be mosaicked to produce Arctic-wide maps of sea ice roughness. This approach starts with Top-of-Atmosphere radiance data from the Multi-angle Imaging SpectroRadiometer (MISR). We used red-band angular data from three MISR cameras (Ca, Cf, An). We created a training data set in which MISR pixels were matched with co-located and concurrent lidar-derived roughness measurements from the Airborne Topographic Mapper (ATM). We used a K-nearest neighbor algorithm with the training data to calibrate the multi-angle data to values of surface roughness and then applied the algorithm to Arctic-wide MISR data for two 16-day periods in April (spring) and July (summer). After georeferencing the roughness rasters, we then mosaicked each 16-day roughness dataset to produce Arctic-wide maps of sea ice roughness for spring and summer. Assessment of the results shows good agreement with independent ATM roughness data, not used in model development. A preliminary exploration of spatial and seasonal changes in sea ice roughness for two locations shows the ability to characterize the roughness of different ice types and the results align with previous studies. This processing system and its data products can help the sea ice research community to gain insights into the seasonal and interannual changes in sea ice roughness over the Arctic. [ABSTRACT FROM AUTHOR]

A new method to assess land surface fractional open water ( fw ) inundation dynamics was developed using high frequency (89 GHz) brightness temperatures ( T b ) from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E), with other ancillary inputs from AMSR-E and MODIS (Moderate Resolution Imaging Spectroradiometer). The resulting 89 GHz fw ( fw WBand ) retrievals provide 10-day temporal fidelity and enhanced (5 km) resolution relative to other passive microwave sensor based land observations. A look-up table was first established to provide reference microwave emissivities for water and land endmembers under a range of atmosphere and land surface conditions. The fw WBand retrievals were then obtained on a per pixel basis using difference ratio and double difference methods for respective barren and vegetated soil conditions. The resulting fw WBand summer (JJA) seasonal composites correspond favorably ( R ≥ 0.86, p < 0.001) with alternative, finer resolution (≤ 300 m) static open water maps derived from satellite radar and optical-infrared sensors over a pan-Arctic (≥ 45°N) domain. The fw WBand retrievals also capture significant seasonal drought and flooding events inferred from river discharge records within the major Arctic basins. The fw WBand retrievals are found to be reliable for discriminating open water in moderately dense vegetation areas, but with greater uncertainty over barren land. The ability of the fw retrievals to detect surface water under vegetation also decreases exponentially with increasing canopy optical thickness. The new fw WBand dataset provides an improved resolution satellite environmental data record documenting open water patterns and inundation dynamics in boreal-Arctic ecosystems experiencing rapid climate change. [ABSTRACT FROM AUTHOR]

Abstract: Continuous ground-based total ozone measurements were carried out at Ny-Ålesund (78.9°N, 11.9° E, Svalbard islands, Norway), by using a Brewer spectrophotometer, for almost three years (2007–2009). Given the scarcity of high-quality ground-based measurements performed at high latitudes, this dataset provides a rare opportunity to check the quality of satellite-based data recorded in Arctic. Considering the unprecedented Artic ozone loss recently derived from satellite observations, assessing the reliability of satellite readings at such high latitudes is a timely effort. This study focuses on the comparisons between ground-based Brewer total ozone measurements and satellite ozone readings retrieved from EOS Aura Ozone Measurement Instrument (OMI) (by using OMI-TOMS and OMI-DOAS algorithms), retrieved from ERS-2 Global Ozone Monitoring Experiment (GOME) (by using TOGOMI algorithm) and from Envisat SCanning Imaging Absorption spectrometer for Atmospheric CartographY (SCIAMACHY) (by using TOSOMI algorithm). Despite the extreme conditions that affect both ground- and satellite-based measurements, a good agreement was found (r =0.99 for OMI-TOMS, r =0.97 for both OMI-DOAS and GOME datasets and r =0.96 for SCIAMACHY). Nevertheless, space-based total ozone readings were found to underestimate ground-based data, in agreement with prior results. OMI-TOMS data showed the highest negative mean bias (MB=−2.03%) whereas DOAS-based datasets showed a better agreement (MB=−0.47, −0.69 and −0.01% for OMI-DOAS, GOME and SCIAMACHY, respectively) but a greater data scatter. On the other hand, DOAS-based datasets present an important seasonal dependence; an overestimation of the Brewer total ozone up to about 3% is found in April and September with respect to OMI-DOAS and SCIAMACHY while in September with respect to GOME. In contrast, differences between OMI-TOMS and Brewer total ozone do not present significant dependence on the season or on geometrical parameters (i.e., satellite solar and viewing zenith angles). Differences in total ozone values between ground-based and satellite measurements are further discussed attending to the algorithm differences in dealing with cloud cover under high surface albedo (i.e., snow) conditions. Extending the study to Sodankyla station (67.3°N, 26.6°E, Finland), similarities and differences between the two stations have been highlighted. The agreement between satellite and Brewer data slightly improved when Sodankyla station is considered. On the continent, the seasonal dependence and data scatter for DOAS-based datasets and the MB for OMI-TOMS are smaller with respect to Ny-Ålesund. Moreover, the roughly constant ozone summer offset (about 1%) between OMI-TOMS and OMI-DOAS, well evident for Ny-Ålesund, is not present for Sodankyla. Finally, some patterns in the geographic distributions of the satellite-Brewer total ozone differences are evident only for Ny-Ålesund, partly because of the peculiar characteristics of the surface albedo (higher than in Sodankyla, with a strong water/land contrast). [Copyright &y& Elsevier]

Little is known about how satellite imagery can be used to describe burn severity in tundra landscapes. The Anaktuvuk River Fire (ARF) in 2007 burned over 1000 km2 of tundra on the North Slope of Alaska, creating a mosaic of small (1 m2) to large (>100 m2) patches that differed in burn severity. The ARF scar provided us with an ideal landscape to determine if a single-date spectral vegetation index can be used once vegetation recovery began and to independently determine how pixel size influences burn severity assessment. We determine and explore the sensitivity of several commonly used vegetation indices to variation in burn severity across the ARF scar and the influence of pixel size on the assessment and classification of tundra burn severity. We conducted field surveys of spectral reflectance at the peak of the first growing season post-fire (extended assessment period) at 18 field sites that ranged from high to low burn severity. In comparing single-date indices, we found that the two-band enhanced vegetation index (EVI2) was highly correlated with normalized burn ratio (NBR) and better distinguished among three burn severity classes than both the NBR and the normalized difference vegetation index (NDVI). We also show clear evidence that shortwave infrared (SWIR) reflectivity does not vary as a function of burn severity. By comparing a Quickbird scene (2.4 m pixels) to simulated 30 and 250 m pixel scenes, we are able to confirm that while the moderate spatial resolution of the Landsat Thematic Mapper (TM) sensor (30 m) is sufficient for mapping tundra burn severity, the coarser resolution of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor (250 m) is not well matched to the fine scale of spatial heterogeneity in the ARF burn scar. [ABSTRACT FROM AUTHOR]

The article presents a study which uses remote sensing satellites to describe the aerosol distribution in terms of both local, regional and global coverage. The study uses a synergetic approach with moderate resolution imaging spectroradiometer (MODIS) data to observe artic aerosol which is important in the knowledge of the properties of the arctic aerosol. Russian wildfires occurring in the July 2010 and its effect on the arctic environment is also presented.

Arctic sea ice extent has decreased dramatically over the last 30 years, and this trend is expected to continue through the twenty-first century. Changes in sea ice extent impact cloud cover, which in turn influences the surface energy budget. Understanding cloud feedback mechanisms requires an accurate determination of cloud cover over the polar regions, which must be obtained from satellite-based measurements. The accuracy of cloud detection using observations from space varies with surface type, complicating any assessment of climate trends as well as the understanding of ice–albedo and cloud–radiative feedback mechanisms. To explore the implications of this dependence on measurement capability, cloud amounts from the Moderate Resolution Imaging Spectroradiometer (MODIS) are compared with those from the CloudSat and Cloud–Aerosol Lidar and Infrared Pathfinder (CALIPSO) satellites in both daytime and nighttime during the time period from July 2006 to December 2008. MODIS is an imager that makes observations in the solar and infrared spectrum. The active sensors of CloudSat and CALIPSO, a radar and lidar, respectively, provide vertical cloud structures along a narrow curtain. Results clearly indicate that MODIS cloud mask products perform better over open water than over ice. Regional changes in cloud amount from CloudSat/CALIPSO and MODIS are categorized as a function of independent measurements of sea ice concentration (SIC) from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). As SIC increases from 10% to 90%, the mean cloud amounts from MODIS and CloudSat–CALIPSO both decrease; water that is more open is associated with increased cloud amount. However, this dependency on SIC is much stronger for MODIS than for CloudSat–CALIPSO, and is likely due to a low bias in MODIS cloud amount. The implications of this on the surface radiative energy budget using historical satellite measurements are discussed. The quantified ice–water difference in MODIS cloud detection can be used to adjust estimated trends in cloud amount in the presence of changing sea ice cover from an independent dataset. It was found that cloud amount trends in the Arctic might be in error by up to 2.7% per decade. The impact of these errors on the surface net cloud radiative effect (“forcing”) of the Arctic can be significant, as high as 8.5%. [ABSTRACT FROM AUTHOR]

Global climate models predict that the strongest dependences of surface air temperatures on increasing atmospheric carbon dioxide levels will occur in the Arctic. A systematic study of these dependences requires accurate Arctic-wide measurements, especially of cloud coverage. Thus cloud detection in the Arctic is extremely important, but it is also challenging because of the similar remote sensing characteristics of clouds and ice- and snow-covered surfaces. This article proposes two new operational Arctic cloud detection algorithms using Multiangle Imaging SpectroRadiometer (MISR) imagery. The key idea is to identify cloud-free surface pixels in the imagery instead of cloudy pixels as in the existing MISR operational algorithms. Through extensive exploratory data analysis and using domain knowledge, three physically useful features to differentiate surface pixels from cloudy pixels have been identified. The first algorithm, enhanced linear correlation matching (ELCM), thresholds the features with either fixed or data-adaptive cutoff values. Probability labels are obtained by using ELCM labels as training data for Fisher's quadratic discriminant analysis (QDA), leading to the second (ELCM-QDA) algorithm. Both algorithms are automated and computationally efficient for operational processing of the massive MISR data set. Based on 5 million expert-labeled pixels, ELCM results are significantly in terms of both accuracy (92%) and coverage (100%) compared with two MISR operational algorithms, one with an accuracy of 80% and coverage of 27% and the other with an accuracy of 83% and a coverage of 70%. The ELCM-QDA probability prediction is also consistent with the expert labels and is more informative. In conclusion, ELCM and ELCM-QDA provide the best performance to date among all available operational algorithms using MISR data. [ABSTRACT FROM AUTHOR]

Spatial and temporal distributions of snow grain size, mass fraction of snow impurity, and snow surface temperature around the northern polar region were retrieved together with snow and sea ice cover extents from radiance data observed by the Global Imager (GLI), an optical sensor onboard Japan''s Advanced Earth Observing Satellite-II (ADEOS-II). The sensor acquired global radiance data from April 2 to October 24 in 2003. Two grain sizes, one for the shallow (0–20 cm) layer and the other for the top surface (0–2 cm) layer, were retrieved using channels with different light penetration depths. Snow and sea ice related products derived from the Advanced Microwave Scanning Radiometer (AMSR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) were used for comparison. Retrieved seasonal variations of snow and sea ice cover extents were consistent with the MODIS and AMSR products. GLI derived snow surface temperatures were also correlated with the MODIS surface temperatures and exhibited latitudinal dependence over the northern hemisphere. Retrieved snow impurities were considered to be partly inaccurate over the Arctic sea ice in April and over inland areas of the continents possibly due to thin snow and/or dense vegetation. However, the snow cover around the northern Canadian Arctic tundra and Archipelago regions and the Greenland ice sheet in April to June was found to be very clean with a soot-equivalent impurity fraction of around 0.05 ppmw or less. Thus, these areas can be used as reference sites for monitoring pollution levels of the Arctic snow cover. Retrieved snow grain radii in the shallow layer seemed erroneous over thin snow covers, but indicated drastic spatial and seasonal evolution of the snow cover over the northern hemisphere with radius ranging from 50 μm to over 1000 μm synchronized with variations in surface temperature. The spatial variability of the top surface grain radius was partly different from that of the grain size in the shallow layer suggesting the possibility of detecting vertical inhomogeneity of the top 10 cm of the snow cover. [Copyright &y& Elsevier]

Expert labels were used to evaluate arctic cloud detection accuracies of several methods based on MISR angular radiances and MODIS spectral radiances. The accuracy of cloud detections was evaluated relative to 5. 086 million expert labels applied to 7. 114 million 1. 1-km resolution pixels with valid radiances from 57 scenes. The accuracy of the MODIS operational cloud mask was 90. 72% for the 32 partly cloudy scenes and 93. 37% for the 25 completely clear and overcast scenes. An automated, simple threshold algorithm based on three features extracted from MISR radiances and the MODIS operational cloud mask agreed with each other for 74. 91% of the pixels in the 32 partly cloudy scenes and 78. 44% of the pixels in the 25 completely clear and overcast scenes. These subsets of pixels had, relative to the expert labels, classification accuracies of 96. 53% for the 32 partly cloudy scenes and 99. 05% for the 25 completely clear and overcast scenes. Fisher''s quadratic discriminate analysis (QDA) trained on expert labels from the 32 partly cloud scenes was applied to MISR radiances, three features based on MISR radiances, MODIS radiances, and the six features of the MODIS operational cloud mask with accuracies ranging from 87. 51% to 96. 43%. Accuracies increased to about 97% when QDA with expert labels was applied to combined radiances or features from both MISR and MODIS. Operational QDA-based classifiers were developed using as training labels those pixels for which the MISR automated, simple threshold and MODIS operational cloud mask algorithms agreed. Training the QDA classifier on these automatic labels using MISR radiances, three features based on MISR radiances, MODIS radiances, and the six features of the MODIS operational cloud mask led to accuracies ranging from 85. 23% to 93. 62% for the 32 partly cloudy scenes. Classification accuracies increased to 93. 74% (93. 40%) when combined MISR and MODIS radiances (features) were used. The highest accuracy attained with any operational algorithm tested on all 57 scenes was 94. 51%. These results suggest that both MISR and MODIS radiances are sufficient for cloud detection in daytime polar regions. [Copyright &y& Elsevier]

The timing of spring river-ice breakup, a major annual event for physical, biological, and human systems on Arctic rivers, has been used to infer regional climate variations over the past century or more. Most observations of ice breakup are recorded as point data taken from selected ground-based stations. It is unknown whether these point observations are fully representative of breakup patterns elsewhere along the course of a river. Here, daily time series of moderate resolution imaging spectroradiometer (MODIS) and advanced very high resolution radiometer (AVHRR) satellite images are used to remotely sense spatial and temporal patterns in ice breakup along 1600–3300 km lengths of the Lena, Ob'', Yenisey, and Mackenzie Rivers. The first day of predominantly ice-free water is visually identified and mapped for ten years (1992–1993, 1995–1998, and 2000–2003), with a mean precision of ±1.75 days. The derived breakup dates show high correlation with ground-based observations, although a slight trend towards earlier satellite-derived dates can be traced to differences in the way ice breakup date is defined. Large ice jams are often observed, particularly at confluences, although smaller ice jams may not be visible due to the limited spatial resolution of the imagery used. At the watershed scale, spatial patterns in breakup seem to be primarily governed by latitude, timing of the spring flood wave, and location of confluences with major tributaries. Interestingly, channel-scale factors such as slope, width, and radius of curvature, which are known to influence ice breakup at the reach scale, do not appear to be major factors at the scale observed here. The degree of similarity between interannual trends in breakup date at distant points along a river is generally high, which supports the use of point-scale data to infer regional climate variations. This similarity does not hold true for the Mackenzie River, where substantial spatial differences in breakup trends are observed. A new variable, spatially integrated breakup date (di), uses weighted spatial averaging to provide a more encompassing measure of breakup timing. The Ob'' and Yenisey Rivers show similar trends in spatially integrated breakup date from year to year. In contrast, the Mackenzie and Lena show a remarkably consistent negative correlation, here attributed to sea surface temperature anomalies associated with the Pacific Decadal Oscillation Index. [Copyright &y& Elsevier]