Melt Pond Mapping With High-Resolution SAR: The First View

Kim, Duk‐jin; Hwang, Byongjun; Chung, Kyung Ho; Lee, Sang Hoon; Jung, Hyung-Sup; Moon, Wooil M.

doi:10.1109/jproc.2012.2226411

Cited by 27 publications

(46 citation statements)

References 26 publications

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“…The airborne SAR system was equipped with a Global Positioning System/inertial measurement units (GPS/IMUs) and a gimbal-mounted phased array antenna, which was mounted on a helicopter (Bell 206 Jet Ranger) and surveyed from 04:48 coordinated universal time (UTC) for 1 h. Due to technical problems, the airborne SAR images could not be combined into a perfectly continuous strip image [28]. Each original airborne SAR image has a size of approximately 600 m in the range direction by 350 m in the azimuth direction.…”

Section: Airborne Sar Survey Datamentioning

confidence: 99%

“…Each original airborne SAR image has a size of approximately 600 m in the range direction by 350 m in the azimuth direction. However, the near-range part of the SAR images was cropped, leaving a size of approximately 400 m in the range direction by 350 m in the azimuth direction, due to no backscattered signal in a predetermined time domain [28]. In order to reduce speckle noise, the resolution of the original SAR images was downgraded to 0.6 m. …”

Section: Airborne Sar Survey Datamentioning

confidence: 99%

“…In the 2010 TerraSAR-X image, large multiyear ice floes that hold numerous melt ponds, seen as dark in small sized and some large and dark open water, were observed. Kim et al [28] provided sea ice conditions in the area of the TerraSAR-X data obtained in 2011 through the analysis of the aerial photographs acquired coincident with the airborne SAR survey (see Figures 4 and 9 in [28]). The study area consists of two sea ice conditions: one is large and thick multiyear ice floes that hold …”

Section: Sea Ice Conditionsmentioning

confidence: 99%

“…In the 2010 TerraSAR-X image, large multiyear ice floes that hold numerous melt ponds, seen as dark in small sized and some large and dark open water, were observed. Kim et al [28] of two sea ice conditions: one is large and thick multiyear ice floes that hold many melt ponds (more than 1000 per square kilometer) on the surface, while the other is heavily-melted small ice fragments of about 100 m in size that hold few melt ponds on the surface [28]. The aerial photographs showed that most of the melt ponds were interconnected by small channels.…”

Section: Sea Ice Conditionsmentioning

confidence: 99%

“…Their results showed that the correlation between the backscattering coefficient and melt pond fraction was very low due to the low resolution of the WSM images (100 m). Kim et al [28] retrieved the melt pond fraction of sea ice in the northern Chukchi Sea from a TerraSAR-X HH-polarized SAR amplitude image obtained in Stripmap mode. Although the TerraSAR-X Stripmap image has a fine spatial resolution (3 m), the melt pond fraction was largely underestimated.…”

Section: Introductionmentioning

confidence: 99%

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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

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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.

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Section: Airborne Sar Survey Datamentioning

confidence: 99%

Section: Airborne Sar Survey Datamentioning

confidence: 99%

Section: Sea Ice Conditionsmentioning

confidence: 99%

“…In the 2010 TerraSAR-X image, large multiyear ice floes that hold numerous melt ponds, seen as dark in small sized and some large and dark open water, were observed. Kim et al [28] of two sea ice conditions: one is large and thick multiyear ice floes that hold many melt ponds (more than 1000 per square kilometer) on the surface, while the other is heavily-melted small ice fragments of about 100 m in size that hold few melt ponds on the surface [28]. The aerial photographs showed that most of the melt ponds were interconnected by small channels.…”

Section: Sea Ice Conditionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 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

Self Cite

View full text Add to dashboard Cite

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Estimation of melt pond fraction over high‐concentration Arctic sea ice using AMSR‐E passive microwave data

et al. 2016

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Melt pond fraction (MPF) on sea ice is an important factor for ice‐albedo feedback throughout the Arctic Ocean. We propose an algorithm to estimate MPF using satellite passive microwave data in this study. The brightness temperature (TB) data obtained from the Advanced Microwave Scanning Radiometer‐Earth observing system (AMSR‐E) were compared to the ship‐based MPF in the Beaufort Sea and Canadian Arctic Archipelago. The difference between the TB at horizontal and vertical polarizations of 6.9 and 89.0 GHz (MP06H–89V), respectively, depends on the MPF. The correlation between MP06H–89V and ship‐based MPF was higher than that between ship‐based MPF and two individual channels (6.9 and 89.0 GHz of horizontal and vertical polarizations, respectively). The MPF determined with the highest resolution channel, 89.0 GHz (5 km × 5 km), provides spatial information with more detail than the 6.9 GHz channel. The algorithm estimates the relative fraction of ice covered by water (1) over areas where sea ice concentration is higher than 95%, (2) during late summer, and (3) in areas with low atmospheric humidity. The MPF estimated from AMSR‐E data (AMSR‐E MPF) in early summer was underestimated at lower latitudes and overestimated at higher latitudes, compared to the MPF obtained from the Moderate Resolution Image Spectrometer (MODIS MPF). The differences between AMSR‐E MPF and MODIS MPF were less than 5% in most the regions and the periods. Our results suggest that the proposal algorithm serves as a basis for building time series of MPF in regions of consolidated ice pack.

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Estimation of Melt Pond Fractions on First Year Sea Ice Using Compact Polarization SAR

Perrie

et al. 2017

JGR Oceans

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Melt ponds are a common feature on Arctic sea ice. They are linked to the sea ice surface albedo and transmittance of energy to the ocean from the atmosphere and thus constitute an important process to parameterize in Arctic climate models and simulations. This paper presents a first attempt to retrieve the melt pond fraction from hybrid‐polarized compact polarization (CP) SAR imagery, which has wider swath and shorter revisit time than the quad‐polarization systems, e.g., from RADARSAT‐2 (RS‐2). The co‐polarization (co‐pol) ratio has been verified to provide estimates of melt pond fractions. However, it is a challenge to link CP parameters and the co‐pol ratio. The theoretical possibility is presented, for making this linkage with the CP parameter C22/C11 (the ratio between the elements of the coherence matrix of CP SAR) for melt pond detection and monitoring with the tilted‐Bragg scattering model for the ocean surface. The empirical transformed formulation, denoted as the “compact polarization and quad‐pol” (“CPQP”) model, is proposed, based on 2062 RS‐2 quad‐pol SAR images, collocated with in situ measurements. We compared the retrieved melt pond fraction with CP parameters simulated from quad‐pol SAR data with results retrieved from the co‐pol ratio from quad‐pol SAR observations acquired during the Arctic‐Ice (Arctic‐Ice Covered Ecosystem in a Rapidly Changing Environment) field project. The results are shown to be comparable for observed melt pond measurements in spatial and temporal distributions. Thus, the utility of CP mode SAR for melt pond fraction estimation on first year level ice is presented.

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Melt Pond Mapping With High-Resolution SAR: The First View

Cited by 27 publications

References 26 publications

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

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

Estimation of melt pond fraction over high‐concentration Arctic sea ice using AMSR‐E passive microwave data

Estimation of Melt Pond Fractions on First Year Sea Ice Using Compact Polarization SAR

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