• Korean Journal of Remote Sensing
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• v.33 no.2
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• pp.243-248
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• 2017

Sea ice which is an important component of the global climate system is being actively detected by satellite because it have been distributed to polar and high-latitude region. and the sea ice detection method using satellite uses reflectance and temperature data. the sea ice detection method of Moderate-Resolution Imaging Spectroradiometer (MODIS), which is a technique utilizing Ice Surface Temperature (IST) have been utilized by many studies. In this study, we propose a simple and effective method of sea ice detection using the dynamic threshold technique with no IST calculation process. In order to specify the dynamic threshold, pixels with freezing point of MODIS IST of 273.0 K or less were extracted. For the extracted pixels, we analyzed the relationship between MODIS IST, MODIS $11{\mu}m$ channel brightness temperature($T_{11{\mu}m}$) and Brightness Temperature Difference ($BTD:T_{11{\mu}m}-T_{12{\mu}m}$). As a result of the analysis, the relationship between the three values showed a linear characteristic and the threshold value was designated by using this. In the case ofsea ice detection, if $T_{11{\mu}m}$ is below the specified threshold value, it is detected as sea ice on clear sky. And in order to estimate the performance of the proposed sea ice detection method, the accuracy was analyzed using MODIS Sea ice extent and then validation accuracy was higher than 99% in Producer Accuracy (PA).

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1491-1495
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• 2023

This study utilized automated machine learning (AutoML) to calculate Arctic ice surface temperature (IST). AutoML-derived IST exhibited a strong correlation coefficient (R) of 0.97 and a root mean squared error (RMSE) of 2.51K. Comparative analysis with deep neural network (DNN) models revealed that AutoML IST demonstrated good accuracy, particularly when compared to Moderate Resolution Imaging Spectroradiometer (MODIS) IST and ice mass balance (IMB) buoy IST. These findings underscore the effectiveness of AutoML in enhancing IST estimation accuracy under challenging polar conditions.

• Korean Journal of Remote Sensing
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• v.33 no.6_1
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• pp.901-915
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• 2017

Recently, global climate change has caused a catastrophic event in the Arctic Ocean, directly and indirectly. The air-sea interaction has caused the significant sea-ice reduction in the Arctic Ocean, and has been accelerating the Arctic warming. Many scientists are worried about the Arctic environment change, suggesting that many of anomalous events will produce direct or indirect biophysical effects on the Arctic. The aim of this study is to understand the inter-annual variability of the Arctic Ocean in wide-view using multi-satellite-derived measurements. Sea surface temperature (SST) and sea ice concentration (SIC) data were obtained from Optimum Interpolation Sea Surface Temperature (OISST) and ECMWF ERA-Interim, respectively. Chlorophyll-a concentration (CHL) was obtained from Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) and Aqua sensor from MODerate resolution Imaging Spectroradiometer (MODIS-Aqua) sensor which has continuously observed since 1998. From 1998 to 2016 summer in the Arctic Ocean which was defined as regions over $60^{\circ}N$ in this study, there were three consequences that CHL increase ($0.15mg\;m^{-3}\;decade^{-1}$), SST warming ($0.43^{\circ}C\;decade^{-1}$) and SIC decrease ($-5.37%\;decade^{-1}$). While SST and SIC highly correlated each other (r = -0.76), a relationship between CHL and SIC was very low ($r={\pm}0.1$) because of data limitations. And a relationship between CHL and SST shows meaningful results ($r={\pm}0.66$) with regional differences.

• Journal of the Korean earth science society
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• v.36 no.2
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• pp.139-157
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• 2015

We investigated the relative errors of satellite-observed Surface Skin Temperature (SST) data caused by sea ice in the northern hemispheric ocean ($30-90^{\circ}N$) during April 16-24, 2003-2014 by intercomparing MODerate Resolution Imaging Spectroradiometer (MODIS) Ice Surface Temperature (IST) data with two types of Atmospheric Infrared Sounder (AIRS) SST data including one with the AIRS/Advanced Microwave Sounding Unit-A (AMSU) and the other with 'AIRS only'. The MODIS temperatures, compared to the AIRS/AMSU, were systematically up to ~1.6 K high near the sea ice boundaries but up to ~2 K low in the sea ice regions. The main reason of the difference of skin temperatures is that the MODIS algorithm used infrared channels for the sea ice detection (i.e., surface classification), while microwave channels were additionally utilized in the AIRS/AMSU. The 'AIRS only' algorithm has been developed from NASA's Goddard Space Flight Center (NASA/GSFC) to prepare for the degradation of AMSU-A by revising part of the AIRS/AMSU algorithm. The SST of 'AIRS only' compared to AIRS/AMSU showed a bias of 0.13 K with RMSE of 0.55 K over the $30-90^{\circ}N$ region. The difference between AIRS/AMSU and 'AIRS only' was larger over the sea ice boundary than in other regions because the 'AIRS only' algorithm utilized the GCM temperature product (NOAA Global Forecast System) over seasonally-varying frozen oceans instead of the AMSU microwave data. Three kinds of the skin temperatures consistently showed significant warming trends ($0.23-0.28Kyr^{-1}$) in the latitude band of $70-80^{\circ}N$. The systematic disagreement among the skin temperatures could affect the discrepancies of their trends in the same direction of either warming or cooling.