scholarly journals Ice Surface Temperature Retrieval from a Single Satellite Imager Band

2018 ◽  
Vol 10 (12) ◽  
pp. 1909 ◽  
Author(s):  
Yinghui Liu ◽  
Richard Dworak ◽  
Jeffrey Key
Keyword(s):  
Surface Temperature ◽  
Infrared Imaging ◽  
Single Band ◽  
Dual Band ◽  
The Arctic ◽  
Landsat 8 ◽  
Ice Surface ◽  
Window Method ◽  
Comparable Performance ◽  
Ice Surface Temperature

Current methods for estimating the surface temperature of sea and lake ice—the ice surface temperature (IST)—utilize two satellite imager thermal bands (11 and 12 μm) at moderate spatial resolution. These “split-window” or dual-band methods have been shown to have low biases and uncertainties. A single-band algorithm would be useful for satellite imagers that have only the 11 μm band at high resolution, such as the Visible Infrared Imaging Radiometer Suite (VIIRS), or that do not have a fully functional 12 μm band, such as the Thermal Infrared Sensor onboard the Landsat 8. This study presents a method for single-band IST retrievals, and validation of the retrievals using IST measurements from an airborne infrared radiation pyrometer during the NASA IceBridge campaign in the Arctic. Results show that IST with a single thermal band from the VIIRS has comparable performance to IST with the VIIRS dual-band (split-window) method, with a bias of 0.22 K and root-mean-square error of 1.03 K.

scholarly journals Comparison of Leads Mapping in the Arctic Ocean Between Landsat and MODIS Ice Surface Temperature Products

2019 ◽  
Vol 1 ◽  
pp. 1-1
Author(s):  
Xiaoping Pang ◽  
Pei Fan ◽  
Xi Zhao ◽  
Qing Ji
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Single Channel ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Landsat 8 ◽  
Ice Surface ◽  
Arctic Sea ◽  
Split Window Algorithm ◽  
Ice Surface Temperature

<p><strong>Abstract.</strong> Leads are linear or wedge-shaped openings in the sea ice cover. They account for about half of the sensible heat transfer from the Arctic Ocean to the atmosphere in winter, though the sea surface area covered by them is only 1%~2% of the total sea ice area, thus monitoring leads changes and mapping leads distributions become an essential role on Arctic researches. Sea ice surface temperature (IST) product from Moderate Resolution Imaging Spectroradiometer (MODIS) is the most used source of leads monitoring and mapping, however, due to the coarse spatial resolution (1km at swath level), it suffers from mixed pixel effect when describes the temperature variations on thin leads (10m~100m), thus an IST product with a finer spatial resolution is needed. Though several surface temperature retrieval algorithms had been introduced based on Landsat 8 thermal imagery, none of them were validated in Arctic sea ice region. Given that the special weather conditions such as air temperature inversion were not taken into consideration, these algorithms may not always suitable for IST acquisition in Arctic. In this paper, we applied five mainstream IST algorithms (three split window algorithms and two single channel methods) on Arctic sea ice, compared the Landsat 8 IST with corresponding MODIS IST product, and validated all the satellite ISTs by in situ temperature measurements from drifting buoys. Compared to the buoy ISTs, the single channel method through web-based atmosphere correction tool provided by Barsi et al. (2003) offers the best accuracy. The split window algorithm proposed by Du et al. (2015) ranks the second, but constrained by the banding effect due to the stripe noise. Split window algorithm introduced by Jiménez-Muñoz et al. (2014) coincides with MODIS IST product best. All of the three methods mentioned above have slightly better accuracy than MODIS IST, particular in thin leads areas, which indicated that Landsat based leads map will provide us a better insight of Arctic sea ice. All the satellite ISTs tend to underestimate the surface temperature than those measured by buoys.</p>

scholarly journals Arctic surface temperatures from Metop AVHRR compared to in situ ocean and land data

2012 ◽  
Vol 9 (2) ◽  
pp. 1009-1043 ◽  
Author(s):  
G. Dybkjær ◽  
R. Tonboe ◽  
J. Høyer
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Temperature Data ◽  
Coupled Systems ◽  
The Arctic ◽  
In Situ Observation ◽  
Clear Sky ◽  
Ice Surface ◽  
Ice Surface Temperature

Abstract. The ice surface temperature (IST) is an important boundary condition for both atmospheric and ocean and sea ice models and for coupled systems. An operational ice surface temperature product using satellite Metop AVHRR infra-red data was developed for MyOcean. The IST can be mapped in clear sky regions using a split window algorithm specially tuned for sea ice. Clear sky conditions are prevailing during spring in the Arctic while persistent cloud cover limits data coverage during summer. The cloud covered regions are detected using the EUMETSAT cloud mask. The Metop IST compares to 2 m temperature at the Greenland ice cap Summit within STD error of 3.14 °C and to Arctic drifting buoy temperature data within STD error of 3.69 °C. A case study reveal that the in situ radiometer data versus satellite IST STD error can be much lower (0.73 °C) and that the different in situ measures complicates the validation. Differences and variability between Metop IST and in situ data are analysed and discussed. An inter-comparison of Metop IST, numerical weather prediction temperatures and in situ observation indicates large biases between the different quantities. Because of the scarcity of conventional surface temperature or surface air temperature data in the Arctic the satellite IST data with its relatively good coverage can potentially add valuable information to model analysis for the Arctic atmosphere.

scholarly journals ASTER-Derived High-Resolution Ice Surface Temperature for the Arctic Coast

2018 ◽  
Vol 10 (5) ◽  
pp. 662
Author(s):  
Young-Sun Son ◽  
Hyun-cheol Kim ◽  
Sung Lee
Keyword(s):  
High Resolution ◽  
Surface Temperature ◽  
The Arctic ◽  
Ice Surface ◽  
Arctic Coast ◽  
Ice Surface Temperature

scholarly journals Arctic surface temperatures from Metop AVHRR compared to in situ ocean and land data

Ocean Science ◽  
2012 ◽  
Vol 8 (6) ◽  
pp. 959-970 ◽  
Author(s):  
G. Dybkjær ◽  
R. Tonboe ◽  
J. L. Høyer
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Temperature Data ◽  
Coupled Systems ◽  
The Arctic ◽  
In Situ Observation ◽  
Clear Sky ◽  
Ice Surface ◽  
Ice Surface Temperature

Abstract. The ice surface temperature (IST) is an important boundary condition for both atmospheric and ocean and sea ice models and for coupled systems. An operational ice surface temperature product using satellite Metop AVHRR infra-red data was developed for MyOcean. The IST can be mapped in clear sky regions using a split window algorithm specially tuned for sea ice. Clear sky conditions prevail during spring in the Arctic, while persistent cloud cover limits data coverage during summer. The cloud covered regions are detected using the EUMETSAT cloud mask. The Metop IST compares to 2 m temperature at the Greenland ice cap Summit within STD error of 3.14 °C and to Arctic drifting buoy temperature data within STD error of 3.69 °C. A case study reveals that the in situ radiometer data versus satellite IST STD error can be much lower (0.73 °C) and that the different in situ measurements complicate the validation. Differences and variability between Metop IST and in situ data are analysed and discussed. An inter-comparison of Metop IST, numerical weather prediction temperatures and in situ observation indicates large biases between the different quantities. Because of the scarcity of conventional surface temperature or surface air temperature data in the Arctic, the satellite IST data with its relatively good coverage can potentially add valuable information to model analysis for the Arctic atmosphere.

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

2015 ◽  
Vol 8 (10) ◽  
pp. 4025-4041 ◽  
Author(s):  
H.-J. Kang ◽  
J.-M. Yoo ◽  
M.-J. Jeong ◽  
Y.-I. Won
Keyword(s):  
Sea Ice ◽  
The Arctic ◽  
Data Sets ◽  
High Latitudes ◽  
Ice Surface ◽  
Moderate Resolution ◽  
Microwave Sounding ◽  
Polar Oceans ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Ice Surface Temperature

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 ice-albedo feedback. The systematic temperature disagreement associated with surface type classification had an impact on the resulting temperature trends.

scholarly journals Melt probabilities and surface temperature trends on the Greenland ice sheet using a Gaussian mixture model

2021 ◽  
Author(s):  
Daniel Clarkson ◽  
Emma Eastoe ◽  
Amber Leeson
Keyword(s):  
Surface Temperature ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Gaussian Mixture ◽  
Ice Surface ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Ice Surface Temperature ◽  
Model Components

Abstract. The Greenland ice sheet has experienced significant melt over the past six decades, with extreme melt events covering large areas of the ice sheet. Melt events are typically analysed using summary statistics, but the nature and characteristics of the events themselves are less frequently analysed. Our work examines melt events from a statistical perspective by modelling 19 years of Moderate Resolution Imaging Spectroradiometer (MODIS) ice surface temperature data using a Gaussian mixture model. We use a mixture model with separate model components for ice and meltwater temperatures at 1139 locations spaced across the ice sheet. By considering the uncertainty of the ice surface temperature measurements, we use the two categories of model components to define a probability of melt for a given observation rather than using a fixed melt threshold. This probability can then be used to estimate the expected number of melt events at a given location. Furthermore, the model can be used to estimate temperature quantiles at a given location, and analyse temperature and melt trends over time by fitting the model to subsets of time. Fitting the model to data from 2001–2009 and 2010–2019 shows increases in melt probability for significant portions of the ice sheet, as well as the yearly expected maximum temperatures.

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