Sea Ice Surface Temperature Estimation Using MODIS and AMSR-E Data Within a Guided Variational Model Along the Labrador Coast

Scott, K. Andrea; Li, Edward; Wong, Alexander

doi:10.1109/jstars.2013.2292795

Cited by 4 publications

(2 citation statements)

References 32 publications

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“…The theoretical bias was found to be within 1 K for 70% of vegetated land areas worldwide. In 2014, Scott et al [13] proposed the multimodality guided variational (MGV) approach, which combines data captured by a passive microwave sensor with data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) in order to estimate IST. An evaluation of the MGV method's performance was conducted by comparing the sea ice thickness derived from the swath surface temperature and the sea ice thickness obtained from the surface temperature by MGV.…”

Section: Introductionmentioning

confidence: 99%

Arctic Sea Ice Surface Temperature Inversion Using FY-3D/MWRI Brightness Temperature Data

Meng,

Chen,

Liu

et al. 2024

Remote Sensing

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The Arctic plays a crucial role in the intricate workings of the global climate system. With the rapid development of information technology, satellite remote sensing technology has emerged as the main method for sea ice surface temperature (IST) observation. To obtain Arctic IST, we used the FengYun-3D Microwave Radiation Imager (FY-3D/MWRI) brightness temperature (Tb) data for IST inversion using multiple linear regressions. Measured data on IST parameters in the Arctic are difficult to obtain. We used the Moderate-Resolution Imaging Spectroradiometer (MODIS) MYD29 IST data as the baseline to obtain the coefficients for the MWRI IST inversion function. The relation between MWRI Tb data and MODIS MYD29 IST product was established and the microwave IST inversion equation was obtained for the months of January to December 2019. Based on the R2 results and the IST inversion results, we compared and analyzed the MWRI IST data from the months of January to April, November, and December with the Operation IceBridge KT19 IR Surface Temperature data and the Northern High Latitude Level 3 Sea and Sea Ice Surface Temperature (NHL L3 SST/IST). We found that compared MWRI IST with NHL L3 IST, the correlation coefficients (Corr) > 0.72, mean bias ranged from −1.82°C to −0.67 °C, and the standard deviation (Std) ranged from 3.61 °C to 4.54 °C; comparing MWRI IST with KT19 IST, the Corr was 0.69, the bias was 0.51 °C, and the Std was 4.34 °C. The obtained error conforms to the precision requirement. From these results, we conclude that the FY-3D/MWRI Tb data are suitable for IST retrieval in the Arctic using multiple linear regressions.

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Section: Introductionmentioning

confidence: 99%

Arctic Sea Ice Surface Temperature Inversion Using FY-3D/MWRI Brightness Temperature Data

Meng,

Chen,

Liu

et al. 2024

Remote Sensing

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“…However, the AIRS only algorithm using only AIRS observations has been developed due to the degradation of the AMSU-A. Microwave and multispectral radiometers were used for global mapping of the sea ice extent and dynamics, while the visible, near-infrared, and infrared sensors could obtain details on the ice concentration, snow/ice albedo, thickness, and IST during clear-sky conditions (Hall et al, 2004;Scott et al, 2014). The MODIS on Earth Observing System (EOS) Aqua, used as infrared measurements, was influenced by water and cloud contamination, but had a higher spatial resolution (Dong et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Uncertainties of satellite-derived surface skin temperatures in the polar oceans: MODIS, AIRS/AMSU, and AIRS only

et al. 2015

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Abstract. Uncertainties in the satellite-derived surface skin temperature (SST) data in the polar oceans during two periods (16-24 April and 15-23 September) 2003-2014 were investigated and the three data sets were intercompared as follows: MODerate Resolution Imaging Spectroradiometer Ice Surface Temperature (MODIS IST), the SST of the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit-A (AIRS/AMSU), and AIRS only. The AIRS only algorithm was developed in preparation for the degradation of the AMSU-A. MODIS IST was systematically warmer up to 1.65 K at the sea ice boundary and colder down to −2.04 K in the polar sea ice regions of both the Arctic and Antarctic than that of the AIRS/AMSU. This difference in the results could have been caused by the surface classification method. The spatial correlation coefficient of the AIRS only to the AIRS/AMSU (0.992-0.999) method was greater than that of the MODIS IST to the AIRS/AMSU (0.968-0.994). The SST of the AIRS only compared to that of the AIRS/AMSU had a bias of 0.168 K with a RMSE of 0.590 K over the Northern Hemisphere high latitudes and a bias of −0.109 K with a RMSE of 0.852 K over the Southern Hemisphere high latitudes. There was a systematic disagreement between the AIRS retrievals at the boundary of the sea ice, because the AIRS only algorithm utilized a less accurate GCM forecast over the seasonally varying frozen oceans than the microwave data. The three data sets (MODIS, AIRS/AMSU and AIRS only) showed significant warming rates (2.3 ± 1.7 ∼ 2.8 ± 1.9 K decade −1 ) in the northern high regions (70-80 • N) as expected from the icealbedo feedback. The systematic temperature disagreement associated with surface type classification had an impact on the resulting temperature trends.

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Uncertainties of satellite-derived surface skin temperatures in the polar oceans: MODIS, AIRS/AMSU, and AIRS only

Kang¹,

Yoo²,

Jeong³

et al. 2015

Preprint

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Abstract. Uncertainties in the satellite-derived Surface Skin Temperature (SST) data in the polar oceans during two periods (16–24 April and 15–23 September) of 2003–2014 were investigated and the three datasets were intercompared as follows: MODerate Resolution Imaging Spectroradiometer Ice Surface Temperature (MODIS IST), the SST of the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit-A (AIRS/AMSU), and AIRS only. AIRS only algorithm was developed in preparation for the degradation of the AMSU-A. MODIS IST was systematically up to 1.65 K warmer at the sea ice boundary and up to 2.04 K colder in the polar sea ice regions of both the Arctic and Antarctic than that of the AIRS/AMSU. This difference in the results could have been caused by the surface classification method. The spatial correlation coefficient of the AIRS only to the AIRS/AMSU (0.992–0.999) method was greater than that of the MODIS IST to the AIRS/AMSU (0.968–0.994). The SST of the AIRS only compared to that of the AIRS/AMSU had a bias of 0.168 K with a RMSE of 0.590 K over the Northern Hemisphere high latitudes and a bias of −0.109 K with a RMSE of 0.852 K over the Southern Hemisphere high latitudes. There was a systematic disagreement between the AIRS retrievals at the boundary of the sea ice, because the AIRS only algorithm utilized a~less accurate GCM forecast over the seasonally-varying frozen oceans than the microwave data. The three datasets (MODIS, AIRS/AMSU and AIRS only) showed significant warming rates (2.3 ± 1.7 ~2.8 ± 1.9 K decade−1) in the northern high latitude regions (70–80° N) as expected from the ice-albedo feedback. The systematic temperature disagreement associated with surface type classification had an impact on the resulting temperature trends.

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Sea Ice Surface Temperature Estimation Using MODIS and AMSR-E Data Within a Guided Variational Model Along the Labrador Coast

Cited by 4 publications

References 32 publications

Arctic Sea Ice Surface Temperature Inversion Using FY-3D/MWRI Brightness Temperature Data

Arctic Sea Ice Surface Temperature Inversion Using FY-3D/MWRI Brightness Temperature Data

Uncertainties of satellite-derived surface skin temperatures in the polar oceans: MODIS, AIRS/AMSU, and AIRS only

Uncertainties of satellite-derived surface skin temperatures in the polar oceans: MODIS, AIRS/AMSU, and AIRS only

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