Next Article in Journal
Machine Learning Automatic Model Selection Algorithm for Oceanic Chlorophyll-a Content Retrieval
Previous Article in Journal
Assessment of Machine Learning Algorithms for Automatic Benthic Cover Monitoring and Mapping Using Towed Underwater Video Camera and High-Resolution Satellite Images
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Remote Sensing
Volume 10
Issue 5
10.3390/rs10050774
remotesensing-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
Erratum to Remote Sens. 2018, 10(1), 37.
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Erratum

Erratum: Nasonova, S. et al. Linking Regional Winter Sea Ice Thickness and Surface Roughness to Spring Melt Pond Fraction on Landfast Arctic Sea Ice. Remote Sens. 2018, 10, 37

by
Sasha Nasonova
1,*,
Randall K. Scharien
1,
Christian Haas
2 and
Stephen E. L. Howell
3
1
Department of Geography, University of Victoria, 3800 Finnerty Road, Victoria, BC V8P 5C2, Canada
2
Department of Earth and Space Sciences and Engineering, York University, 4700 Keele Street, Toronto, ON M3J 1P3, Canada
3
Climate Research Division, Environment and Climate Change Canada, 4905 Dufferin Street, Toronto, ON M3H 5T4, Canada
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(5), 774; https://doi.org/10.3390/rs10050774
Submission received: 12 May 2018 / Revised: 15 May 2018 / Accepted: 15 May 2018 / Published: 17 May 2018
Browse Figures
Versions Notes
After publication of the research paper [1], the authors wish to make the following correction. In Figures 7, 9, 11, 12, the legend depicted first-year ice (FYI) in red, and multiyear ice (MYI) in blue. The legend should have been reversed with FYI shown in blue and MYI in red. Both original and corrected versions of Figures 7, 9, 11, and 12 are given below.
Original Figure 7:
Corrected Figure 7:
Original Figure 9:
Corrected Figure 9:
Original Figure 11:
Corrected Figure 11:
Original Figure 12:
Corrected Figure 12:
The blue points correspond to first-year ice (FYI) and red points to multiyear (MYI); however, a typo was made in the legends. This correction will not change the results or any statements in the paper. The authors apologize for any inconvenience caused for readers.

Conflicts of Interest

The authors declare no conflicts of interest.

Reference

  1. Nasonova, S.; Scharien, R.K.; Haas, C.; Howell, S.E.L. Linking Regional Winter Sea Ice Thickness and Surface Roughness to Spring Melt Pond Fraction on Landfast Arctic Sea Ice. Remote Sens. 2018, 10, 37. [Google Scholar] [CrossRef]
Remotesensing 10 00774 g001 550
Figure 7. Scatter plots of fp as a function of mean thickness at the medium scale using object (left), hybrid object (middle) and 240 m grid-cell (right) aggregation approaches. Spearman correlation coefficients (rs) and corresponding p-values are shown for pooled FYI and MYI data.
Figure 7. Scatter plots of fp as a function of mean thickness at the medium scale using object (left), hybrid object (middle) and 240 m grid-cell (right) aggregation approaches. Spearman correlation coefficients (rs) and corresponding p-values are shown for pooled FYI and MYI data.
Remotesensing 10 00774 g001
Remotesensing 10 00774 g002 550
Figure 7. Scatter plots of fp as a function of mean thickness at the medium scale using object (left), hybrid object (middle) and 240 m grid-cell (right) aggregation approaches. Spearman correlation coefficients (rs) and corresponding p-values are shown for pooled FYI and MYI data.
Figure 7. Scatter plots of fp as a function of mean thickness at the medium scale using object (left), hybrid object (middle) and 240 m grid-cell (right) aggregation approaches. Spearman correlation coefficients (rs) and corresponding p-values are shown for pooled FYI and MYI data.
Remotesensing 10 00774 g002
Remotesensing 10 00774 g003 550
Figure 9. Scatter plots of fp as a function of standard deviation of roughness at the medium scale using object (left), hybrid object (middle) and 240 m grid-cell (right) aggregation approaches. Spearman correlation coefficients (rs) and corresponding p-values are shown for pooled FYI and MYI data.
Figure 9. Scatter plots of fp as a function of standard deviation of roughness at the medium scale using object (left), hybrid object (middle) and 240 m grid-cell (right) aggregation approaches. Spearman correlation coefficients (rs) and corresponding p-values are shown for pooled FYI and MYI data.
Remotesensing 10 00774 g003
Remotesensing 10 00774 g004 550
Figure 9. Scatter plots of fp as a function of standard deviation of roughness at the medium scale using object (left), hybrid object (middle) and 240 m grid-cell (right) aggregation approaches. Spearman correlation coefficients (rs) and corresponding p-values are shown for pooled FYI and MYI data.
Figure 9. Scatter plots of fp as a function of standard deviation of roughness at the medium scale using object (left), hybrid object (middle) and 240 m grid-cell (right) aggregation approaches. Spearman correlation coefficients (rs) and corresponding p-values are shown for pooled FYI and MYI data.
Remotesensing 10 00774 g004
Remotesensing 10 00774 g005 550
Figure 11. Scatter plots of fp as a function of smoothed minimum surface roughness at the medium scale using object (left), hybrid object (middle) and 240 m grid-cell (right) aggregation approaches. Spearman correlation coefficients (rs) and corresponding p-values are shown for pooled FYI and MYI data.
Figure 11. Scatter plots of fp as a function of smoothed minimum surface roughness at the medium scale using object (left), hybrid object (middle) and 240 m grid-cell (right) aggregation approaches. Spearman correlation coefficients (rs) and corresponding p-values are shown for pooled FYI and MYI data.
Remotesensing 10 00774 g005
Remotesensing 10 00774 g006 550
Figure 11. Scatter plots of fp as a function of smoothed minimum surface roughness at the medium scale using object (left), hybrid object (middle) and 240 m grid-cell (right) aggregation approaches. Spearman correlation coefficients (rs) and corresponding p-values are shown for pooled FYI and MYI data.
Figure 11. Scatter plots of fp as a function of smoothed minimum surface roughness at the medium scale using object (left), hybrid object (middle) and 240 m grid-cell (right) aggregation approaches. Spearman correlation coefficients (rs) and corresponding p-values are shown for pooled FYI and MYI data.
Remotesensing 10 00774 g006
Remotesensing 10 00774 g007 550
Figure 12. Scatter plots of maximum roughness as a function of maximum thickness at the medium scale using object (left) and 240 m grid-cell (right) aggregation approaches. Spearman correlation coefficients (rs) and corresponding p-values are shown for pooled FYI and MYI data.
Figure 12. Scatter plots of maximum roughness as a function of maximum thickness at the medium scale using object (left) and 240 m grid-cell (right) aggregation approaches. Spearman correlation coefficients (rs) and corresponding p-values are shown for pooled FYI and MYI data.
Remotesensing 10 00774 g007
Remotesensing 10 00774 g008 550
Figure 12. Scatter plots of maximum roughness as a function of maximum thickness at the medium scale using object (left) and 240 m grid-cell (right) aggregation approaches. Spearman correlation coefficients (rs) and corresponding p-values are shown for pooled FYI and MYI data.
Figure 12. Scatter plots of maximum roughness as a function of maximum thickness at the medium scale using object (left) and 240 m grid-cell (right) aggregation approaches. Spearman correlation coefficients (rs) and corresponding p-values are shown for pooled FYI and MYI data.
Remotesensing 10 00774 g008

Share and Cite

MDPI and ACS Style

Nasonova, S.; Scharien, R.K.; Haas, C.; Howell, S.E.L. Erratum: Nasonova, S. et al. Linking Regional Winter Sea Ice Thickness and Surface Roughness to Spring Melt Pond Fraction on Landfast Arctic Sea Ice. Remote Sens. 2018, 10, 37. Remote Sens. 2018, 10, 774. https://doi.org/10.3390/rs10050774

AMA Style

Nasonova S, Scharien RK, Haas C, Howell SEL. Erratum: Nasonova, S. et al. Linking Regional Winter Sea Ice Thickness and Surface Roughness to Spring Melt Pond Fraction on Landfast Arctic Sea Ice. Remote Sens. 2018, 10, 37. Remote Sensing. 2018; 10(5):774. https://doi.org/10.3390/rs10050774

Chicago/Turabian Style

Nasonova, Sasha, Randall K. Scharien, Christian Haas, and Stephen E. L. Howell. 2018. "Erratum: Nasonova, S. et al. Linking Regional Winter Sea Ice Thickness and Surface Roughness to Spring Melt Pond Fraction on Landfast Arctic Sea Ice. Remote Sens. 2018, 10, 37" Remote Sensing 10, no. 5: 774. https://doi.org/10.3390/rs10050774

APA Style

Nasonova, S., Scharien, R. K., Haas, C., & Howell, S. E. L. (2018). Erratum: Nasonova, S. et al. Linking Regional Winter Sea Ice Thickness and Surface Roughness to Spring Melt Pond Fraction on Landfast Arctic Sea Ice. Remote Sens. 2018, 10, 37. Remote Sensing, 10(5), 774. https://doi.org/10.3390/rs10050774

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop