Melt pond fraction and spectral sea ice albedo retrieval from MERIS data – Part 1: Validation against in situ, aerial, and ship cruise data

Istomina, Larysa; Heygster, Georg; Huntemann, Marcus; Schwarz, Pascal; Birnbaum, Gerit; Scharien, Randall K.; Polashenski, Chris; Perovich, Donald K.; Zege, Eleonora P.; Malinka, Aleksey; Prikhach, Alexander S.; Katsev, I. L.

doi:10.5194/tc-9-1551-2015

Cited by 78 publications

(131 citation statements)

References 27 publications

(36 reference statements)

Supporting

Mentioning

125

Contrasting

Unclassified

Order By: Relevance

“…6 for July 2012. Comparing our simulated melt pond fraction with satellite products shows that the mean July pond fraction of CICE-mw (25 %) is closer to the mean values based on MERIS (Istomina et al, 2015) and MODIS data (Roesel et al, 2012) (24 % for both) than the mean pond fraction of CICE-default (28 %). Furthermore, the RMS error with respect to MERIS is reduced from 16 % (CICE-default) to 14 % (CICE-mw) justifying our increased release of melt water.…”

Section: Improving Cice Simulation By Varying Model Physicsmentioning

confidence: 95%

New insight from CryoSat-2 sea ice thickness for sea ice modelling

Feltham¹,

Schröder²,

Tsamados³

2018

Preprint

View full text Add to dashboard Cite

Abstract. Estimates of Arctic sea ice thickness are available from the CryoSat-2 (CS2) radar altimetry mission during ice growth seasons since 2010. We derive the sub-grid scale ice thickness distribution (ITD) with respect to 5 ice thickness categories used in a sea ice component (CICE) of climate simulations. This allows us to initialize the ITD in stand-alone 10 simulations with CICE and to verify the simulated cycle of ice thickness. We find that a default CICE simulation strongly underestimates ice thickness, despite reproducing the inter-annual variability of summer sea ice extent. We can identify the underestimation of winter ice growth as being responsible and show that increasing the ice conductive flux for lower temperatures (bubbly brine scheme) and accounting for the loss of drifting snow results in the simulated sea ice growth being more realistic. Sensitivity studies provide insight into the impact of initial and atmospheric conditions and, thus, on the role of 15 positive and negative feedback processes. During summer, atmospheric conditions are responsible for 50% of September sea ice thickness variability through the positive sea ice and melt pond albedo feedback. However, atmospheric winter conditions have little impact on winter ice growth due to the dominating negative conductive feedback process: the thinner the ice and snow in autumn, the stronger the ice growth in winter. We conclude that the fate of Arctic summer sea ice is largely controlled by atmospheric conditions during the melting season rather than by winter temperature. Our optimal model configuration does 20 not only improve the simulated sea ice thickness, but also summer sea ice concentration, melt pond fraction, and length of the melt season. It is the first time CS2 sea ice thickness data have been applied successfully to improve sea ice model physics.

show abstract

Section: Improving Cice Simulation By Varying Model Physicsmentioning

confidence: 95%

New insight from CryoSat-2 sea ice thickness for sea ice modelling

Feltham¹,

Schröder²,

Tsamados³

2018

Preprint

View full text Add to dashboard Cite

show abstract

“…The proposed models for melt pond detection are only applicable to situations where individual melt ponds are larger than the pixel size of SAR data, which is an important distinction from the previous studies that developed techniques for unmixing the melt pond fraction from mixed pixels [17,32]. The TerraSAR-X dual-polarization data can be effectively used for regular monitoring of the melt pond fraction on a local scale (typically less than 100 km 2 ) based on the revisit time of the satellite, but not enough to observe melt ponds at a regional scale due to the limitation of the swath of the polarimetric SAR data.…”

Section: Retrieved Melt Pond Statisticsmentioning

confidence: 99%

“…In order to study the relationship between sea ice ponding and climate change, the melt pond fractions at a much larger spatial scale should be monitored. Satellite optical data, such as MODIS and MERIS, can be used to retrieve the melt pond fraction over a regional scale [17,19,21,22]. However, the prevailing cloudy days in the summer season make it difficult to observe melt ponds using optical data.…”

Section: Retrieved Melt Pond Statisticsmentioning

confidence: 99%

“…Based on the distinctive spectral albedo of melt pond between sea ice and open water [22], the previous studies estimated the fraction of ponds by analyzing the albedo of ponded ice. The melt pond fraction estimated from the optical data resulted in a root mean square error (RMSE) of~15% when compared to in situ observation and aerial photography [17,[19][20][21]23], and it was used as an important predictor of sea ice cover and climate change [16,24,25]. However, the applicability of such optical images is restricted to daytime and clear skies, which provides few chances to observe the sea ice surface in the Arctic.…”

Section: Introductionmentioning

confidence: 99%

“…Optical satellite data with medium-low resolution (30 m-1 km), such as the Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS), the ENVISAT Medium Resolution Imaging Spectrometer (MERIS) and the Landsat-7 Enhanced Thematic Mapper Plus (ETM+), multispectral images have been used to observe melt ponds on widely-distributed sea ice in the Arctic [17][18][19][20][21][22][23]. Based on the distinctive spectral albedo of melt pond between sea ice and open water [22], the previous studies estimated the fraction of ponds by analyzing the albedo of ponded ice.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Retrieval of Melt Ponds on Arctic Multiyear Sea Ice in Summer from TerraSAR-X Dual-Polarization Data Using Machine Learning Approaches: A Case Study in the Chukchi Sea with Mid-Incidence Angle Data

Han

Kim

et al. 2016

Remote Sensing

View full text Add to dashboard Cite

Melt ponds, a common feature on Arctic sea ice, absorb most of the incoming solar radiation and have a large effect on the melting rate of sea ice, which significantly influences climate change. Therefore, it is very important to monitor melt ponds in order to better understand the sea ice-climate interaction. In this study, melt pond retrieval models were developed using the TerraSAR-X dual-polarization synthetic aperture radar (SAR) data with mid-incidence angle obtained in a summer multiyear sea ice area in the Chukchi Sea, the Western Arctic, based on two rule-based machine learning approaches-decision trees (DT) and random forest (RF)-in order to derive melt pond statistics at high spatial resolution and to identify key polarimetric parameters for melt pond detection. Melt ponds, sea ice and open water were delineated from the airborne SAR images (0.3-m resolution), which were used as a reference dataset. A total of eight polarimetric parameters (HH and VV backscattering coefficients, co-polarization ratio, co-polarization phase difference, co-polarization correlation coefficient, alpha angle, entropy and anisotropy) were derived from the TerraSAR-X dual-polarization data and then used as input variables for the machine learning models. The DT and RF models could not effectively discriminate melt ponds from open water when using only the polarimetric parameters. This is because melt ponds showed similar polarimetric signatures to open water. The average and standard deviation of the polarimetric parameters based on a 15ˆ15 pixel window were supplemented to the input variables in order to consider the difference between the spatial texture of melt ponds and open water. Both the DT and RF models using the polarimetric parameters and their texture features produced improved performance for the retrieval of melt ponds, and RF was superior to DT. The HH backscattering coefficient was identified as the variable contributing the most, and its spatial standard deviation was the next most contributing one to the classification of open water, sea ice and melt ponds in the RF model. The average of the co-polarization phase difference and the alpha angle in a mid-incidence angle were also identified as the important variables in the RF model. The melt pond fraction and sea ice concentration retrieved from the RF-derived melt pond map showed root mean square deviations of 2.4% and 4.9%, respectively, compared to those from the reference melt pond maps. This indicates that there is potential to accurately monitor melt ponds on multiyear sea ice in the summer season at a local scale using high-resolution dual-polarization SAR data.

show abstract

Winter Sentinel‐1 Backscatter as a Predictor of Spring Arctic Sea Ice Melt Pond Fraction

Scharien

Segal

Nasonova

et al. 2017

Geophysical Research Letters

Self Cite

View full text Add to dashboard Cite

Spring melt pond fraction (f p) has been shown to influence September sea ice extent and, with a growing need to improve melt pond physics in climate and forecast models, observations at large spatial scales are needed. We present a novel technique for estimating f p on sea ice at high spatial resolution from the Sentinel-1 satellite during the winter period leading up to spring melt. A strong correlation (r = À0.85) is found between winter radar backscatter and f p from first-year and multiyear sea ice data collected in the Canadian Arctic Archipelago (CAA) in 2015. Observations made in the CAA in 2016 are used to validate a f p retrieval algorithm, and a f p prediction for the CAA in 2017 is made. The method is effective using the horizontal transmit and receive polarization channel only and shows promise for providing seasonal, pan-Arctic, f p maps for improved understanding of melt pond distributions and forecast model skill. Plain Language Summary Recent and well-documented changes in Arctic sea ice have introduced the need for timely and accurate seasonal forecasts of ice conditions. Seasonal forecasts of ice conditions will reduce the risks to humans and help preserve the fragile Arctic ecosystem by preventing accidents and spills. Recent studies have shown a link between the amount of surface meltwater flooding that occurs on sea ice in the spring, termed melt pond fraction, and the extent of sea ice that remains at the end of summer. This link is due to the ability of surface meltwater to absorb more sunlight compared to bare ice and snow. This study provides a new way to estimate the amount of surface meltwater flooding expected to occur on the sea ice in spring, using satellite data collected during the winter period. The results presented here provide a key link between winter and late summer sea ice conditions that will enhance the ability of forecasters to make accurate seasonal predictions several months in advance of the active summer period.

show abstract

Melt pond fraction and spectral sea ice albedo retrieval from MERIS data – Part 1: Validation against in situ, aerial, and ship cruise data

Cited by 78 publications

References 27 publications

New insight from CryoSat-2 sea ice thickness for sea ice modelling

New insight from CryoSat-2 sea ice thickness for sea ice modelling

Retrieval of Melt Ponds on Arctic Multiyear Sea Ice in Summer from TerraSAR-X Dual-Polarization Data Using Machine Learning Approaches: A Case Study in the Chukchi Sea with Mid-Incidence Angle Data

Winter Sentinel‐1 Backscatter as a Predictor of Spring Arctic Sea Ice Melt Pond Fraction

Contact Info

Product

Resources

About