Estimation of turbulent heat flux over leads using satellite thermal images

Qu, Meng; Pang, Xiaoping; Zhao, Xi; Zhang, Jinlun; Ji, Qing; Fan, Pei

doi:10.5194/tc-13-1565-2019

Cited by 28 publications

(18 citation statements)

References 54 publications

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“…We introduce for the first time a lead-width distribution for Antarctic sea ice using Weddell sea ice as a case study. We confirm that the lead-width distribution for Weddell sea ice follows a power law, showing similar behavior to the lead-width distribution in the Arctic (Wadhams, 1981;Wadhams et al, 1985;Lindsay and Rothrock, 1995;Marcq and Weiss, 2012;Wernecke and Kaleschke, 2015;Qu et al, 2019), but with a smaller exponent.…”

Section: Availability Of Sentinel-2 For Observing Leadssupporting

confidence: 76%

A lead-width distribution for Antarctic sea ice: a case study for the Weddell Sea with high resolution Sentinel-2 images

Muchow¹,

Tetzlaff²,

Kaleschke³

2020

Preprint

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Abstract. We derive for the first time a statistical lead-width distribution for Antarctic sea ice using Weddell sea ice as a case study. Therefore, we transfer previous approaches for Arctic sea ice with a power law with a positive exponent (p(xwidth) xwidth−a, a > 1) to Antarctic sea ice. We use 20 carefully selected cloud-free Copernicus Sentinel-2 images from November 2016 until February 2018, covering only the months from November to April. In doing so we compare exponents given in the literature for the Arctic sea ice, who do not agree with each other, to Antarctic sea ice. To detect leads we create a sea ice surface type classification for the Sentinel-2 Level 1C data products, which are selected due to their high spatial resolution of 10 m. We apply two different fitting methods to the measured lead widths, which have been used in previous studies for Arctic sea ice. The first fitting method is a linear fit, while the second method is based on a maximum likelihood approach. Here, we use both methods for the same lead-width data set to observe differences in the calculated power law exponent. To further investigate influences on the power law exponent, we define two different lead thresholds for open water and nilas. The influence of the lead threshold on the exponent is bigger for the linear fit than for the method based on the maximum likelihood approach. We show that the exponent of the lead-width distribution ranges between 1.16 to 1.41 depending on the applied fitting method and lead threshold. This exponent for the Weddell sea ice is smaller than the previously observed exponents for the Arctic sea ice.

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Section: Availability Of Sentinel-2 For Observing Leadssupporting

confidence: 76%

A lead-width distribution for Antarctic sea ice: a case study for the Weddell Sea with high resolution Sentinel-2 images

Muchow¹,

Tetzlaff²,

Kaleschke³

2020

Preprint

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“…Specifically, a unified framework comprising two CNNs that simultaneously produce Landsat-like SST imagery and a corresponding binary map of leads, which are used in the estimation of the THF over leads, from the MODIS SST imagery, is proposed. The generated Landsat-like SST imagery and lead map are then employed to estimate the THF with an aerodynamic bulk formula (Marcq and Weiss, 2012;Qu et al, 2019). This study will provide a new perspective for solving the mixed pixel problem in the estimation of THF from remotely sensed imagery by extracting subpixel level information.…”

Section: Introductionmentioning

confidence: 99%

Estimating subpixel turbulent heat flux over leads from MODIS thermal infrared imagery with deep learning

Yin¹,

Li²,

Ge³

et al. 2021

Preprint

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Abstract. Turbulent heat flux (THF) over leads is an important variable used for monitoring climate change in the Arctic. Presently, THF over leads is often calculated from satellite imagery. The accuracy of the estimated THF is low for mixed pixels that consist of ice and leads, because the mixed pixels along lead boundaries will lower the accuracy of the surface temperature measured over leads and the corresponding lead map. To address this problem, a deep residual convolutional neural network (CNN)-based framework is proposed to estimate THF over leads at the subpixel scale (DeepSTHF) with remotely sensed imagery. The DeepSTHF allows the production of a sea surface temperature (SST) image and a corresponding lead map with a finer spatial resolution than the input SST image using two CNNs, so that the subpixel scale THF can be estimated from them. The proposed approach is assessed using simulated and real MODIS imagery and compared against the conventional bicubic interpolation and pixel-based methods. The results demonstrate that the proposed CNN-based method can effectively estimate subpixel-scale information from the coarse data and performs well in producing fine spatial resolution SST images and lead maps, thereby allowing researchers to obtain more accurate and reliable THF over leads.

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“…Remotely sensed satellite images have become a promising data source that is often used to estimate the THF occurring over leads (Qu et al, 2019), because on-site measurement is always difficult given the harsh weather conditions typical of polar regions. To calculate the THF over leads with remotely sensed data, both a lead map and associated sea surface temperature (SST) are required.…”

Section: Introductionmentioning

confidence: 99%

Comment on tc-2020-363

2021

View full text Add to dashboard Cite

Turbulent heat flux (THF) over leads is an important variable used for monitoring climate change in the Arctic.Presently, THF over leads is often calculated from satellite imagery. The accuracy of the estimated THF is low for mixed pixels that consist of ice and leads, because the mixed pixels along lead boundaries will lower the accuracy of the surface temperature measured over leads and the corresponding lead map. To address this problem, a deep residual convolutional neural network (CNN)-based framework is proposed to estimate THF over leads at the subpixel scale (DeepSTHF) with remotely sensed imagery. The DeepSTHF allows the production of a sea surface temperature (SST) image and a corresponding lead map with a finer spatial resolution than the input SST image using two CNNs, so that the subpixel scale THF can be estimated from them. The proposed approach is assessed using simulated and real MODIS imagery and compared against the conventional bicubic interpolation and pixel-based methods. The results demonstrate that the proposed CNN-based method can effectively estimate subpixel-scale information from the coarse data and performs well in producing fine spatial resolution SST images and lead maps, thereby allowing researchers to obtain more accurate and reliable THF over leads.

show abstract

Estimation of turbulent heat flux over leads using satellite thermal images

Cited by 28 publications

References 54 publications

A lead-width distribution for Antarctic sea ice: a case study for the Weddell Sea with high resolution Sentinel-2 images

A lead-width distribution for Antarctic sea ice: a case study for the Weddell Sea with high resolution Sentinel-2 images

Estimating subpixel turbulent heat flux over leads from MODIS thermal infrared imagery with deep learning

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