Alvaro Ivanoff

Assessment of EOS Aqua AMSR-E Arctic Sea Ice Concentrations Using Landsat-7 and Airborne Microwave Imagery

An assessment of Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E) sea ice concentrations under winter conditions using ice concentrations derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) imagery obtained during the March 2003 Arctic sea ice validation field campaign is presented. The National Oceanic and Atmospheric Administration Environmental Technology Laboratorys Airborne Polarimetric Scanning Radiometer Measurements, which were made from the National Aeronautics and Space Administration P 3B aircraft during the campaign, were used primarily as a diagnostic tool to understand the comparative results and to suggest improvements to the AMSR-E ice concentration algorithm. Based on the AMSR-E/ETM+ comparisons, a good overall agreement with little bias (~1%) for areas of first year and young sea ice was found. Areas of new ice production result in a negative bias of about 5% in the AMSR-E ice concentration retrievals, with a root mean square error of 8%. Some areas of deep snow also resulted in an underestimate of the ice concentration (~10%). For all ice types combined and for the full range of ice concentrations, the bias ranged from 0% to 3%, and the rms errors ranged from 1% to 7%, depending on the region. The new-ice and deep-snow biases are expected to be reduced through an adjustment of the new-ice and ice-type C algorithm tie points

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.

Intersensor Calibration Between F13 SSMI and F17 SSMIS for Global Sea Ice Data Records

An intercalibration between F13 Special Sensor Microwave Imager (SSMI) and F17 Special Sensor Microwave Imager Sounder (SSMIS) sea ice extents and areas for a full year of overlap is undertaken preparatory to extending the 1979-2007 National Aeronautics and Space Administration (NASA) Goddard Space Flight Center NASA Team algorithm time series of global sea ice extents and areas. The 1979-2007 time series was created from Scanning Multichannel Microwave Radiometer (SMMR) and SSMI data. After intercalibration, the yearly mean F17 and F13 difference in northern hemisphere (NH) sea ice extents is - 0.0156%, with a standard deviation (SD) of the differences of 0.6204%, and the yearly mean difference in NH sea ice areas is 0.5433%, with an SD of 0.3519%. For the southern hemisphere, the yearly mean difference in sea ice extents is 0.0304% ±0.4880%, and the mean difference in sea ice areas is 0.1550% ±0.3753%. This F13/F17 intercalibration enables the extension of the 29-year 1979-2007 SMMR/SSMI sea ice time series for as long as there are stable F17 SSMIS brightness temperatures available.

A Comparison of Snow Depth on Sea Ice Retrievals Using Airborne Altimeters and an AMSR-E Simulator

A comparison of snow depths on sea ice was made using airborne altimeters and an Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) simulator. The data were collected during the March 2006 National Aeronautics and Space Administration (NASA) Arctic field campaign utilizing the NASA P-3B aircraft. The campaign consisted of an initial series of coordinated surface and aircraft measurements over Elson Lagoon, Alaska and adjacent seas followed by a series of large-scale (100 km × 50 km) coordinated aircraft and AMSR-E snow depth measurements over portions of the Chukchi and Beaufort seas. This paper focuses on the latter part of the campaign. The P-3B aircraft carried the University of Colorado Polarimetric Scanning Radiometer (PSR-A), the NASA Wallops Airborne Topographic Mapper (ATM) lidar altimeter, and the University of Kansas Delay-Doppler (D2P) radar altimeter. The PSR-A was used as an AMSR-E simulator, whereas the ATM and D2P altimeters were used in combination to provide an independent estimate of snow depth. Results of a comparison between the altimeter-derived snow depths and the equivalent AMSR-E snow depths using PSR-A brightness temperatures calibrated relative to AMSR-E are presented. Data collected over a frozen coastal polynya were used to intercalibrate the ATM and D2P altimeters before estimating an altimeter snow depth. Results show that the mean difference between the PSR and altimeter snow depths is -2.4 cm (PSR minus altimeter) with a standard deviation of 7.7 cm. The RMS difference is 8.0 cm. The overall correlation between the two snow depth data sets is 0.59.

Assessment of AMSR-E Antarctic Winter Sea-Ice Concentrations Using Aqua MODIS

An assessment of the standard Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) sea-ice concentrations for the Antarctic winter is made from a comparison of nearly 40 000 AMSR-E sea-ice concentration values with geolocated sea-ice concentrations derived from ten Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) scenes acquired on October 1st and 2nd of 2005 and 2006. The standard AMSR-E sea-ice concentration products are produced using the National Aeronautics and Space Administration Team 2 sea-ice algorithm. The ten MODIS scenes cover portions of almost all the sea-ice regions surrounding the Antarctic continent. The AMSR-E averaged ice concentration biases relative to MODIS (AMSR-E minus MODIS) ranged from less than -0.5% to - 18%, and the corresponding averaged root-mean-square (rms) errors ranged from 2% to 24%. One scene [October 1, 2006 (0550 UT)] had both the largest bias (-18%) and rms error (24%), whereas the other nine scenes had an average bias of - 1.5% and an average rms error of 4.9%. The biases and rms errors are correlated with the fractions of new ice and open water. This is consistent with the findings that the largest errors in ice concentration derived from the AMSR-E occur in the marginal ice zone (MIZ) and along the ice edge and are likely caused by sea-ice flooding in the MIZ and new-ice production at the ice edge.

The Potential of Using Landsat 7 Data for the Classification of Sea Ice Surface Conditions During Summer

Abstract During spring and summer, the surface of the Arctic sea-ice cover undergoes rapid changes that greatly affect the surface albedo and significantly impact the further decay of the sea ice. These changes are primarily the development of a wet snow cover and the development of melt ponds. As melt ponds generally do not exceed a couple of meters in diameter, the spatial resolutions of sensors like the Advanced Very High Resolution Radiometer and Moderate Resolution Imaging Spectroradiometer are too coarse for their identification. Landsat 7, on the other hand, has a spatial resolution of 30 m (15 m for the panchromatic band) and thus offers the best chance to map the distribution of melt ponds from space. The different wavelengths (bands) from blue to near-infrared offer the potential to distinguish among different surface conditions. Landsat 7 data for the Baffin Bay region for June 2000 have been analyzed. The analysis shows that different surface conditions, such as wet snow and melt-ponded areas, have different signatures in the individual Landsat bands. Consistent with in situ albedo measurements, melt ponds show up as blueish, whereas dry and wet ice have a white to gray appearance in the Landsat true-color image. These spectral differences enable areas with high fractions of melt ponds to be distinguished.

NASA Team 2 Sea Ice Concentration Algorithm Retrieval Uncertainty

Satellite microwave radiometers are widely used to estimate sea ice cover properties (concentration, extent, and area) through the use of sea ice concentration (IC) algorithms. Rare are the algorithms providing associated IC uncertainty estimates. Algorithm uncertainty estimates are needed to assess accurately global and regional trends in IC (and thus extent and area), and to improve sea ice predictions on seasonal to interannual timescales using data assimilation approaches. This paper presents a method to provide relative IC uncertainty estimates using the enhanced NASA Team (NT2) IC algorithm. The proposed approach takes advantage of the NT2 calculations and solely relies on the brightness temperatures (TBs) used as input. NT2 IC and its associated relative uncertainty are obtained for both the Northern and Southern Hemispheres using the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) TB. NT2 IC relative uncertainties estimated on a footprint-by-footprint swath-by-swath basis were averaged daily over each 12.5-km grid cell of the polar stereographic grid. For both hemispheres and throughout the year, the NT2 relative uncertainty is <;5%. In the Southern Hemisphere, it is low in the interior ice pack, and it increases in the marginal ice zone up to 5%. In the Northern Hemisphere, areas with high uncertainties are also found in the high IC area of the Central Arctic. Retrieval uncertainties are greater in areas corresponding to NT2 ice types associated with deep snow and new ice. Seasonal variations in uncertainty show larger values in summer as a result of melt conditions and greater atmospheric contributions. Our analysis also includes an evaluation of the NT2 algorithm sensitivity to AMSR-E sensor noise. There is a 60% probability that the IC does not change (to within the computed retrieval precision of 1%) due to sensor noise, and the cumulated probability shows that there is a 90% chance that the IC varies by less than ±3%. We also examined the daily IC variability, which is dominated by sea ice drift and ice formation/melt. Daily IC variability is the highest, year round, in the MIZ (often up to 20%, locally 30%). The temporal and spatial distributions of the retrieval uncertainties and the daily IC variability is expected to be useful for algorithm intercomparisons, climate trend assessments, and possibly IC assimilation in models.

Shape-constrained segmentation approach for arctic multiyear sea ice floe analysis

The melting of sea ice is correlated to increases in sea surface temperature and associated climatic changes. Therefore, it is important to investigate how rapidly sea ice floes melt. For this purpose, a new TempoSeg method for multitemporal segmentation of multiyear ice floes is proposed. The microwave radiometer is used to track the position of an ice floe. Then, a time series of MODIS images are created with the ice floe in the image center. A TempoSeg method is performed to segment these images into two regions: Floe and Background. First, morphological feature extraction is applied. Then, the central image pixel is marked as Floe, and shape-constrained best merge region growing is performed. The resulting two-region map is post-filtered by applying morphological operators. We have successfully tested our method on a set of MODIS images and estimated the area of a sea ice floe as a function of time.