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

Tanaka, Yasuhiro; Tateyama, Kazuyoshi; Kameda, Takao; Hutchings, Jennifer K.

doi:10.1002/2016jc011876

Cited by 19 publications

(26 citation statements)

References 42 publications

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“…The ice charts were resampled into 25 km × 25 km NSIDC stereographic projection grids (NSIDC grids). The ice charts used by Tanaka et al [21] were paper maps, using areas with "9+/10" and "10/10". Each area is defined as having a sea ice concentration exceeding 95%, which was manually extracted.…”

Section: Canadian Ice Service Regional Ice Chartsmentioning

confidence: 99%

“…This study aims to derive this DO and DD using the time series of MPF retrieved data from the Advanced Microwave Scanning Radiometer-Earth observing system (AMSR-E MPF), an estimate proposed by Tanaka et al [21] (see Supplementary Text S1). The AMSR-E MPF estimate is valid in the Beaufort Sea with pack ice and Canadian Arctic Archipelago (CAA) areas with landfast ice.…”

Section: Introductionmentioning

confidence: 99%

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Estimating Meltwater Drainage Onset Timing and Duration of Landfast Ice in the Canadian Arctic Archipelago Using AMSR-E Passive Microwave Data

Tanaka

2020

Remote Sensing

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Meltwater drainage onset (DO) timing and drainage duration (DD) related to snowmelt-water redistribution are both important for understanding not only the Arctic energy and heat budgets but also the salt/heat balance of the mixed layer in the ocean and sea-ice ecosystem. We present DO and DD as determined from the time series of Advanced Microwave Scanning Radiometer-Earth observing system (AMSR-E) melt pond fraction (MPF) estimates in an area with Canadian landfast ice. To address the lack of evaluation on a day-by-day basis for the AMSR-E MPF estimate, we first compared AMSR-E MPF with the daily Medium Resolution Imaging Spectrometer (MERIS) MPF. The AMSR-E MPF estimate correlates significantly with the MERIS MPF (r = 0.73–0.83). The estimate has a product quality similar to the MERIS MPF only when the albedo is around 0.5–0.7 and a positive bias of up to 10% in areas with an albedo of 0.7–0.9, including melting snow. The DO/DD estimates are determined by using a polynomial regression curve fitted on the time series of the AMSR-E MPF. The DOs/DDs from time series of the AMSR-E and MERIS MPFs are compared, revealing consistency in both DD and DO. The DO timing from 2006 to 2011 is correlated with melt onset timing. To the best of our knowledge, our study provides the first large-scale information on both DO timing and DD.

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Section: Canadian Ice Service Regional Ice Chartsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimating Meltwater Drainage Onset Timing and Duration of Landfast Ice in the Canadian Arctic Archipelago Using AMSR-E Passive Microwave Data

Tanaka

2020

Remote Sensing

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“…The root-mean-squared error (RMSE) for optical approaches is 0.08 to 0.16. Approaches using passive microwave data [19] are limited by the sensor resolution, especially in narrow channels where land contamination is likely. They are also only applicable to very high sea ice concentrations; even small areas of open water would dominate the signal.…”

Section: Of 21mentioning

confidence: 99%

Predicting Melt Pond Fraction on Landfast Snow Covered First Year Sea Ice from Winter C-Band SAR Backscatter Utilizing Linear, Polarimetric and Texture Parameters

et al. 2018

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Early-summer melt pond fraction is predicted using late-winter C-band backscatter of snow-covered first-year sea ice. Aerial photographs were acquired during an early-summer 2012 field campaign in Resolute Passage, Nunavut, Canada, on smooth first-year sea ice to estimate the melt pond fraction. RADARSAT-2 Synthetic Aperture Radar (SAR) data were acquired over the study area in late winter prior to melt onset. Correlations between the melt pond fractions and late-winter linear and polarimetric SAR parameters and texture measures derived from the SAR parameters are utilized to develop multivariate regression models that predict melt pond fractions. The results demonstrate substantial capability of the regression models to predict melt pond fractions for all SAR incidence angle ranges. The combination of the most significant linear, polarimetric and texture parameters provide the best model at far-range incidence angles, with an R 2 of 0.62 and a pond fraction RMSE of 0.09. Near- and mid- range incidence angle models provide R 2 values of 0.57 and 0.61, respectively, with an RMSE of 0.11. The strength of the regression models improves when SAR parameters are combined with texture parameters. These predictions also serve as a proxy to estimate snow thickness distributions during late winter as higher pond fractions evolve from thinner snow cover.

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“…More importantly, it serves as an important parameter for Arctic climate study [6], [7], [8]. Because of better coverage both in observation areas and period [9], satellite remote sensing has become the main technique for Arctic MPF retrieval [10], [11], [12], [13], [14].…”

Section: Introductionmentioning

confidence: 99%

“…It should be noted that Rösel et al [15] used the same fixed reflectivity as Tschudi et al [10] and expanded the MPF retrieval results to the whole Arctic. Their results were validated with National Snow and Ice Data Center (NSIDC) melt pond data [16] [9] also used some prior information from ship-based MPF to build a linear model between AMSR-E brightness temperature (TB) and MPF. As a result, their model can only be applicable to areas with high sea ice concentrations and RMSE is 8.9%.…”

Section: Introductionmentioning

confidence: 99%

(Online First)Applying high-resolution visible imagery to satellite melt pond fraction retrieval: A neural network approach

Liu

Zhang²,

Lv³

et al. 2023

som

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During summer, melt ponds have a significant influence on Arctic sea-ice albedo. The melt pond fraction (MPF) also has the ability to forecast the Arctic sea-ice in a certain period. It is important to retrieve accurate melt pond fraction (MPF) from satellite data for Arctic research. This paper proposes a satellite MPF retrieval model based on the multi-layer neural network, named MPF-NN. Our model uses multi-spectral satellite data as model input and MPF information from multi-site and multiperiod visible imagery as prior knowledge for modeling. It can effectively model melt ponds evolution of different regions and periods over the Arctic. Evaluation results show that the MPF retrieved from MODIS data using the proposed model has an RMSE of 3.91% and a correlation coefficient of 0.73. The seasonal distribution of MPF is also consistent with previous results.Index Terms-Multi-layer neural network, high-resolution imagery, melt pond fraction

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

Cited by 19 publications

References 42 publications

Estimating Meltwater Drainage Onset Timing and Duration of Landfast Ice in the Canadian Arctic Archipelago Using AMSR-E Passive Microwave Data

Estimating Meltwater Drainage Onset Timing and Duration of Landfast Ice in the Canadian Arctic Archipelago Using AMSR-E Passive Microwave Data

Predicting Melt Pond Fraction on Landfast Snow Covered First Year Sea Ice from Winter C-Band SAR Backscatter Utilizing Linear, Polarimetric and Texture Parameters

(Online First)Applying high-resolution visible imagery to satellite melt pond fraction retrieval: A neural network approach

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