Arguably, the most remarkable manifestation of change in the polar regions is the rapid decline (of about -10 %/decade) in the Arctic perennial ice cover. Changes in the global sea ice cover, however, are more modest, being slightly positive in the Southern Hemisphere and slightly negative in the Northern Hemisphere, the significance of which has not been adequately assessed because of unknown errors in the satellite historical data. We take advantage of the recent and more accurate AMSR-E data to evaluate the true seasonal and interannual variability of the sea ice cover, assess the accuracy of historical data, and determine the real trend. Consistently derived ice concentrations from AMSR-E, SSM/I, and SMMR data were analyzed and a slight bias is observed between AMSR-E and SSM/I data mainly because of differences in resolution. Analysis of the combine SMMR, SSM/I and AMSR-E data set, with the bias corrected, shows that the trends in extent and area of sea ice in the Arctic region is -3.4 +/- 0.2 and -4.0 +/- 0.2 % per decade, respectively, while the corresponding values for the Antarctic region is 0.9 +/- 0.2 and 1.7 .+/- 0.3 % per decade. The higher resolution of the AMSR-E provides an improved determination of the location of the ice edge while the SSM/I data show an ice edge about 6 to 12 km further away from the ice pack. Although the current record of AMSR-E is less than 5 years, the data can be utilized in combination with historical data for more accurate determination of the variability and trends in the ice cover.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2007JC004257

Trends in the sea ice cover using enhanced and compatible AMSR‐E, SSM/I, and SMMR data

. A long-term, consistent, and reproducible satellite-based passive microwave sea ice concentration climate data record (CDR) is available for climate studies, monitoring, and model validation with an initial operation capability (IOC). The daily and monthly sea ice concentration data are on the National Snow and Ice Data Center (NSIDC) polar stereographic grid with nominal 25 km × 25 km grid cells in both the Southern and Northern Hemisphere polar regions from 9 July 1987 to 31 December 2007. The data files are available in the NetCDF data format at http://nsidc.org/data/g02202.html and archived by the National Climatic Data Center (NCDC) of the National Oceanic and Atmospheric Administration (NOAA) under the satellite climate data record program ( http://www.ncdc.noaa.gov/cdr/operationalcdrs.html ). The description and basic characteristics of the NOAA/NSIDC passive microwave sea ice concentration CDR are presented here. The CDR provides similar spatial and temporal variability as the heritage products to the user communities with the additional documentation, traceability, and reproducibility that meet current standards and guidelines for climate data records. The data set, along with detailed data processing steps and error source information, can be found at http://dx.doi.org/10.7265/N55M63M1 .

/pdf/a-long-term-and-reproducible-passive-microwave-sea-ice-vexz6uglh2.pdf

A long-term and reproducible passive microwave sea ice concentration data record for climate studies and monitoring

Bootstrap Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS, Version 3

Variability and trend studies of sea ice in the Arctic have been conducted using products derived from the same raw passive microwave data but by different groups using different algorithms. This study provides consistency assessment of four of the leading products, namely, Goddard Bootstrap (SB2), Goddard NASA Team (NT1), EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI-SAF 1.2), and Hadley HadISST 2.2 data in evaluating variability and trends in the Arctic sea ice cover. All four provide generally similar ice patterns but significant disagreements in ice concentration distributions especially in the marginal ice zone and adjacent regions in winter and meltponded areas in summer. The discrepancies are primarily due to different ways the four techniques account for occurrences of new ice and meltponding. However, results show that the different products generally provide consistent and similar representation of the state of the Arctic sea ice cover. Hadley and NT1 data usually provide the highest and lowest monthly ice extents, respectively. The Hadley data also show the lowest trends in ice extent and ice area at negative 3.88 percent decade and negative 4.37 percent decade, respectively, compared to an average of negative 4.36 percent decade and negative 4.57 percent decade for all four. Trend maps also show similar spatial distribution for all four with the largest negative trends occurring at the Kara/Barents Sea and Beaufort Sea regions, where sea ice has been retreating the fastest. The good agreement of the trends especially with updated data provides strong confidence in the quantification of the rate of decline in the Arctic sea ice cover.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/2017JC012768

Variability and trends in the Arctic Sea ice cover: Results from different techniques

The Antarctic sea ice extent has been slowly increasing contrary to expected trends due to global warming and results from coupled climate models. After a record high extent in 2012 the extent was even higher in 2014 when the magnitude exceeded 20 × 106 km2 for the first time during the satellite era. The positive trend is confirmed with newly reprocessed sea ice data that addressed inconsistency issues in the time series. The variability in sea ice extent and ice area was studied alongside surface ice temperature for the 34-yr period starting in 1981, and the results of the analysis show a strong correlation of −0.94 during the growth season and −0.86 during the melt season. The correlation coefficients are even stronger with a one-month lag in surface temperature at −0.96 during the growth season and −0.98 during the melt season, suggesting that the trend in sea ice cover is strongly influenced by the trend in surface temperature. The correlation with atmospheric circulation as represented by th...

/pdf/positive-trend-in-the-antarctic-sea-ice-cover-and-associated-1nj7i8qw5j.pdf

Positive Trend in the Antarctic Sea Ice Cover and Associated Changes in Surface Temperature

https://www.jstage.jst.go.jp/article/rssj1981/16/2/16_2_133/_pdf

Evaluation and improvement of SSM/I sea ice concentration algorithms for the Sea of Okhotsk

Although satellite data are useful for obtaining ice-thickness distribution for perennial sea ice or in stable thin-sea-ice areas, they are still largely an unresolved issue for the seasonal ice zone (SIZ). We address this problem using L-band synthetic aperture radar (SAR). In the SIZ, ice-thickness growth is closely related to deformation, so surface roughness is expected to correlate with ice thickness. L-band SAR, suitable for detecting such surface roughness, is a promising tool for obtaining thickness distribution. This idea was supported by an airborne polarimetric and interferometric SAR (Pi-SAR) validation. To extend this result to spaceborne L-band SAR with coarser resolution, we conducted in situ measurements of ice thickness and surface roughness in February 2008 in the southern Sea of Okhotsk with an icebreaker in coordination with the Advanced Land Observing Satellite (ALOS)/Phased Array-type L-band SAR (PALSAR) orbit. A helicopter-borne laser profiler was used to improve the estimation of surface roughness. It was found that backscatter coefficients (HH) correlated well with ice thickness (R = 0.86) and surface roughness (R = 0.70), which confirms the possibility of determining ice-thickness distribution in the SIZ. the interannual variation of PALSAR-derived ice-thickness distribution in the southern Sea of Okhotsk is also discussed.

/pdf/retrieval-of-sea-ice-thickness-distribution-in-the-sea-of-17hm1ayp0w.pdf

Retrieval of sea-ice thickness distribution in the Sea of Okhotsk from ALOS/PALSAR backscatter data

Image compression is a key technology to realize on-line satellite image transmission economically and quickly Among various image compression algorithms, the JPEG algorithm is the international standard for still color image compression In this study, various kinds of satellite images were compressed with the JPEG algorithm The relation between compression ratio and image quality were evaluated As for the image quality evaluation, both subjective evaluation and objective evaluation were performed It was determined that all the test satellite images could be compressed to at least 1/10 of the original data volume preserving high visual image quality The degradation of spatial distribution quality of the compressed images were evaluated using power spectrum of original and compressed images >

An evaluation of JPEG compression for on-line satellite images transmission

A software package for NOAA/HRPT data processing, PaNDA (Package for NOAA Data Analysis), was developed in 1989 by a number of Japanese scientists. The PaNDA had been distributed to the science community as a freeware. The software package includes format conversion, radiometric correction, atmospheric correction, geometric correction of the NOAA/HRPT data, and run under the UNIX environment. The authors are now upgrading the software package to process the ADEOS/OCTS data as well. The outline of the software package development is presented.

Development of a software package for ADEOS and NOAA data analysis

The tsunami that occurred on March 11, 2011, in Japan caused widespread devastation of infrastructure and rice cultivation areas. This study assessed the rice growing areas damaged due to this event and the restoration progress using moderate resolution imaging spectroradiometer data. The data were processed for 2010 to 2013, comprising four main steps: (1) data preprocessing to construct the smooth time-series enhanced vegetation index 2 data, (2) rice crop mapping using the dynamic time warping algorithm, (3) accuracy assessment, and (4) change analysis of rice cultivation areas. The mapping results validated using the ground reference data indicated overall accuracies and kappa coefficients generally >84.3% and 0.69, respectively. The results, also verified with the government’s rice area statistics, reaffirmed a close correlation between these two datasets (R2>0.77). When examining changes of rice cultivation between 2010 and 2013, the rice area damaged by the tsunami during 2010 to 2011 was ∼17,550  ha. The rice area restored after one year (2012) was ∼6663  ha, but this regressed slightly in 2013 (5656 ha). Such information could be used by officials to better understand the tsunami-damaged rice area and the restoration progress for crop management in the study region.

Assessing rice crop damage and restoration using remote sensing in tsunami-affected areas, Japan

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