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Correction

Correction: Wang, L., et al. Assessment of the Dual Polarimetric Sentinel-1A Data for Forest Fuel Moisture Content Estimation. Remote Sensing 2019, 11(13), 1568

1
School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China
2
Center for Information Geoscience, University of Electronic Science and Technology of China, Chengdu 611731, China
3
Fenner School of Environment and Society, The Australian National University, Canberra, ACT 2601, Australia
4
Bushfire & Natural Hazards Cooperative Research Centre, Melbourne, VIC 3002, Australia
*
Authors to whom correspondence should be addressed.
Remote Sens. 2020, 12(2), 206; https://doi.org/10.3390/rs12020206
Submission received: 19 December 2019 / Accepted: 6 January 2020 / Published: 7 January 2020
(This article belongs to the Special Issue Remote Sensing and Image Processing for Fire Science and Management)
The authors wish to make the following corrections to this paper [1]:
1.
Change in main body paragraphs
There is a mistake in this article. On page 6, lines 20–22 the sentence “Here, it should note that the σ s o i l o in bare soil backscatter Linear model (Equation (6)) is expressed in linear unit while that in WCM (Equations (2) and (5)) is expressed in dB unit, and therefore the transformation between these two units was required.” should be “Here, it should be noted that the σ s o i l o in the bare soil backscatter linear model (Equation (6)) is expressed in dB units while that in the WCM (Equations (2) and (5)) is expressed in linear units, and therefore transformation between these two units was required.”.
2.
Change in figures
The authors wish to make the following corrections to this paper. Due to mislabeling, replace:
Figure 5. Linear relationship between backscatter in dB unit and that in linear unit over a small variation range for VV (a) and VH (b) polarization mode, and corresponding conversion formulas (c) used to reduce the model complexity.
Figure 5. Linear relationship between backscatter in dB unit and that in linear unit over a small variation range for VV (a) and VH (b) polarization mode, and corresponding conversion formulas (c) used to reduce the model complexity.
Remotesensing 12 00206 g001
With:
Figure 5. Linear relationship between backscatter in dB unit and that in linear unit over a small variation range for VV (a) and VH (b) polarization mode, and corresponding conversion formulas (c) used to reduce the model complexity.
Figure 5. Linear relationship between backscatter in dB unit and that in linear unit over a small variation range for VV (a) and VH (b) polarization mode, and corresponding conversion formulas (c) used to reduce the model complexity.
Remotesensing 12 00206 g002
The authors clarify that the above errors do not affect the formula of the final model (Equation (7)) and the experimental result of this article. The authors would like to apologize for any inconvenience caused to the readers by these changes.

Reference

  1. Wang, L.; Quan, X.; He, B.; Yebra, M.; Xing, M.; Liu, X. Assessment of the Dual Polarimetric Sentinel-1A Data for Forest Fuel Moisture Content Estimation. Remote Sens. 2019, 11, 1568. [Google Scholar] [CrossRef] [Green Version]

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MDPI and ACS Style

Wang, L.; Quan, X.; He, B.; Yebra, M.; Xing, M.; Liu, X. Correction: Wang, L., et al. Assessment of the Dual Polarimetric Sentinel-1A Data for Forest Fuel Moisture Content Estimation. Remote Sensing 2019, 11(13), 1568. Remote Sens. 2020, 12, 206. https://doi.org/10.3390/rs12020206

AMA Style

Wang L, Quan X, He B, Yebra M, Xing M, Liu X. Correction: Wang, L., et al. Assessment of the Dual Polarimetric Sentinel-1A Data for Forest Fuel Moisture Content Estimation. Remote Sensing 2019, 11(13), 1568. Remote Sensing. 2020; 12(2):206. https://doi.org/10.3390/rs12020206

Chicago/Turabian Style

Wang, Long, Xingwen Quan, Binbin He, Marta Yebra, Minfeng Xing, and Xiangzhuo Liu. 2020. "Correction: Wang, L., et al. Assessment of the Dual Polarimetric Sentinel-1A Data for Forest Fuel Moisture Content Estimation. Remote Sensing 2019, 11(13), 1568" Remote Sensing 12, no. 2: 206. https://doi.org/10.3390/rs12020206

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