A study of the feasibility of using KOMPSAT-5 SAR data to map sea ice in the Chukchi Sea in late summer

Han, Hyangsun; Hong, Sunhwa; Kim, Hyun Cheol; Chae, Tae-Byeong; Choi, Hae-Jin

doi:10.1080/2150704x.2017.1285501

Cited by 15 publications

(15 citation statements)

References 12 publications

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“…The SIC values computed from the SAR-derived ice/water maps were validated by using the Russian Arctic and Antarctic Research Institute (AARI) ice charts. The mean value of the difference between the SIC values from the RF ice/water maps and the ice charts was −8.85%, which could possibly be caused by the uncertainty in the SIC values of the ice charts given as coarse ice concentration categories (10% or 20% SIC increments) in large polygons [37]. Such high performance of the classification is attributed to the striking differences in SAR intensities of ice and water in X-band SAR images.…”

Section: Sar-derived Ice/water Mapsmentioning

confidence: 96%

“…Sea ice and open water were classified from the KOMPSAT-5 SAR images by using the sea ice mapping model developed by Han et al [37]. The sea ice mapping model was developed for KOMPSAT-5 EW SAR images in HH polarization based on the classification of GLCM textures of the images by RF, which produces a sea ice map with a grid size of 125 m. The model showed a very high overall classification accuracy and kappa coefficient (98% and 99%).…”

Section: Sar-derived Ice/water Mapsmentioning

confidence: 99%

“…Such high performance of the classification is attributed to the striking differences in SAR intensities of ice and water in X-band SAR images. Detailed methodologies for generating ice/water maps from the KOMPSAT-5 EW SAR images are described in Han et al [37] and Han et al [26]. We generated ice/water maps from the KOMPSAT-5 EW SAR images by using the sea ice mapping model developed by Han et al [37] (Figure 2), which was used to compute reference SIC values for training and validating the machine learning based summer SIC retrieval model.…”

Section: Sar-derived Ice/water Mapsmentioning

confidence: 99%

“…In particular, satellite SARs that are hardly affected by weather conditions and sun altitudes but provide high-resolution (a few meters to tens of meters) images have been effectively used to develop SIC estimation models in summer based on machine learning approaches. Han et al [37] developed a new method for the classification of sea ice and open water in summer from the Korean Multi-purpose Satellite-5 (KOMPSAT-5) X-band SAR images by implementing Random Forest, a rule-based machine learning approach, in which the gray level co-occurrence matrix (GLCM) features computed from the SAR imagery were used as input variables for the classification. The values of SIC estimated from the classifications were used as reliable data for the assessment of the PM SIC algorithms [26].…”

Section: Introductionmentioning

confidence: 99%

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Retrieval of Summer Sea Ice Concentration in the Pacific Arctic Ocean from AMSR2 Observations and Numerical Weather Data Using Random Forest Regression

et al. 2021

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The Arctic sea ice concentration (SIC) in summer is a key indicator of global climate change and important information for the development of a more economically valuable Northern Sea Route. Passive microwave (PM) sensors have provided information on the SIC since the 1970s by observing the brightness temperature (TB) of sea ice and open water. However, the SIC in the Arctic estimated by operational algorithms for PM observations is very inaccurate in summer because the TB values of sea ice and open water become similar due to atmospheric effects. In this study, we developed a summer SIC retrieval model for the Pacific Arctic Ocean using Advanced Microwave Scanning Radiometer 2 (AMSR2) observations and European Reanalysis Agency-5 (ERA-5) reanalysis fields based on Random Forest (RF) regression. SIC values computed from the ice/water maps generated from the Korean Multi-purpose Satellite-5 synthetic aperture radar images from July to September in 2015–2017 were used as a reference dataset. A total of 24 features including the TB values of AMSR2 channels, the ratios of TB values (the polarization ratio and the spectral gradient ratio (GR)), total columnar water vapor (TCWV), wind speed, air temperature at 2 m and 925 hPa, and the 30-day average of the air temperatures from the ERA-5 were used as the input variables for the RF model. The RF model showed greatly superior performance in retrieving summer SIC values in the Pacific Arctic Ocean to the Bootstrap (BT) and Arctic Radiation and Turbulence Interaction STudy (ARTIST) Sea Ice (ASI) algorithms under various atmospheric conditions. The root mean square error (RMSE) of the RF SIC values was 7.89% compared to the reference SIC values. The BT and ASI SIC values had three times greater values of RMSE (20.19% and 21.39%, respectively) than the RF SIC values. The air temperatures at 2 m and 925 hPa and their 30-day averages, which indicate the ice surface melting conditions, as well as the GR using the vertically polarized channels at 23 GHz and 18 GHz (GR(23V18V)), TCWV, and GR(36V18V), which accounts for atmospheric water content, were identified as the variables that contributed greatly to the RF model. These important variables allowed the RF model to retrieve unbiased and accurate SIC values by taking into account the changes in TB values of sea ice and open water caused by atmospheric effects.

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Section: Sar-derived Ice/water Mapsmentioning

confidence: 96%

Section: Sar-derived Ice/water Mapsmentioning

confidence: 99%

Section: Sar-derived Ice/water Mapsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Retrieval of Summer Sea Ice Concentration in the Pacific Arctic Ocean from AMSR2 Observations and Numerical Weather Data Using Random Forest Regression

et al. 2021

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“…The Korea Multi-Purpose Satellite-5 (KOMPSAT-5) is South Korea's first satellite equipped with X-band SAR. The enhanced observation mode of KOMPSAT-5 can be considered as suitable option for constant monitoring of seaice areas, which has not been investigated completely [37]. According to the DWT algorithm, a multiresolution TFD formulation and modeling of the electromagnetic interaction and HMN multiscale probability distribution function (PDF) within the KOMPSAT-5 SAR sea-ice image is a novel key point for such fractal analysis.…”

Section: Introductionmentioning

confidence: 99%

Fractal Analysis and Texture Classification of High-Frequency Multiplicative Noise in SAR Sea-Ice Images Based on a Transform- Domain Image Decomposition Method

Shahrezaei

Kim

2020

IEEE Access

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Texture in synthetic aperture radar (SAR) images is a combination of the intrinsic texture of scene backscattering and the texture due to noncoherent high-frequency multiplicative noise (HMN) interactions that reflect erroneous information and lead to observation misinterpretation. The focus of this paper is the fractal analysis of KOMPSAT-5 SAR images of noncoherent sea-ice textures while being decomposed by discrete wavelet transform (DWT) processing. As a novel approach, fractal analysis relies on SAR sea-ice spatial backscattering data generation and time-frequency domain (TFD) formulations from the perspective of uncorrelated HMN. To the best of our knowledge, this is the first time that the extraction of the resolution profile and raw data for the reference KOMPSAT-5 SAR sea-ice image have been derived, evaluated and compared both qualitatively and quantitatively at each scale of DWT decomposition on the basis of the presence of HMN. This paper also presents a novel detailed modeling of the multiresolution probability distribution function of the HMN and its power spectral density function modeling at each scale of the decomposition. Other quality assessment techniques, such as two K-means clustering algorithms and several visualized verification methods, such as gradient vector field, advection mapping and tensor field mapping, have been implemented in this regard to investigate embedded HMN suppression and its adverse effects on the presence of pixel anomalies. As a result, as the decomposition continues, the HMN at each scale of decomposition is constantly altering from high-frequency uncorrelated anomalies to low-frequency joint spatial information within the observed 2-D data. In other words, excessive multiscale HMN suppression will result in spatial information loss that makes the DWT scale selection quite important for texture classification. The results also show that the importance of HMN suppression in SAR sea-ice images in the form of pixel anomaly decomposition for the purpose of further texture investigation should be in accordance with the spectral behavior of the HMN. The results are helpful for SAR remote sensing image restoration and data preservation when dealing with high-resolution SAR images, such as in time series analysis, sea-ice texture change detection, and polar structural mapping. The proposed approach is implemented on real KOMPSAT-5 SAR satellite sea-ice images, while fractal spatial resolution profile simulations are carried out based on the inversed equalized hybrid domain image formation algorithm. INDEX TERMS Discrete wavelet transform, fractal analysis, high-frequency multiplicative noise, raw data generation, synthetic aperture radar.

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Evaluation of summer passive microwave sea ice concentrations in the Chukchi Sea based on KOMPSAT-5 SAR and numerical weather prediction data

Han

Kim

2018

Remote Sensing of Environment

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A study of the feasibility of using KOMPSAT-5 SAR data to map sea ice in the Chukchi Sea in late summer

Cited by 15 publications

References 12 publications

Retrieval of Summer Sea Ice Concentration in the Pacific Arctic Ocean from AMSR2 Observations and Numerical Weather Data Using Random Forest Regression

Retrieval of Summer Sea Ice Concentration in the Pacific Arctic Ocean from AMSR2 Observations and Numerical Weather Data Using Random Forest Regression

Fractal Analysis and Texture Classification of High-Frequency Multiplicative Noise in SAR Sea-Ice Images Based on a Transform- Domain Image Decomposition Method

Evaluation of summer passive microwave sea ice concentrations in the Chukchi Sea based on KOMPSAT-5 SAR and numerical weather prediction data

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