Correction: Jiang et al. A Cloud Classification Method Based on a Convolutional Neural Network for FY-4A Satellites. Remote Sens. 2022, 14, 2314
by
, , ,
Yuhang Jiang
1, 
Wei Cheng
2,*,
Feng Gao
1,3,
Shaoqing Zhang
1,4,5,6,7
,
Shudong Wang
8,
Chang Liu
1,9 and
Juanjuan Liu
10 1
College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
2
Beijing Institute of Applied Meteorology, Beijing 100029, China
3
Qingdao Innovation and Development Center, Harbin Engineering University, Qingdao 266400, China
4
The College of Ocean and Atmosphere, Ocean University of China, Qingdao 266100, China
5
Key Laboratory of Physical Oceanography, MOE, Institute for Advanced Ocean Study, Frontiers Science Center for Deep Ocean Multispheres and Earth System (DOMES), Ocean University of China, Qingdao 266100, China
6
Ocean Dynamics and Climate Function Lab/Pilot National Laboratory for Marine Science and Technology (QNLM), Qingdao 266237, China
7
International Laboratory for High-Resolution Earth System Prediction (iHESP), Qingdao 266000, China
8
Public Meteorological Service Center, China Meterological Administration, Beijing 100081, China
9
Qingdao Hatran Ocean Intelligence Technology Co., Ltd., Qingdao 266400, China
10
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2023, 15(18), 4368; https://doi.org/10.3390/rs15184368
Submission received: 7 August 2023
/
Accepted: 9 August 2023
/
Published: 5 September 2023
In the original publication [1], while preparing the model training dataset, we initially included data from both 2018 and 2020. During this process, there were problems with the adjustment of the dataset. Part of the data for 2019 was incorrectly included in this dataset, which ultimately affected the data covered in this paper; therefore, the data need to be corrected. In order to address this issue, we have re-executed the experiment and made necessary updates to the data. Consequently, we need to correct both the data and textual description in the original article.
1. Text Correction
There were seven errors in the original article.
1.1. In Abstract, Paragraph 1, on page 1
The sentence “With the Himawari-8 CLTYPE product and the CloudSat 2B-CLDCLASS product as the test comparison target, the CLP-CNN network results match the Himawari-8 product highly, by 84.4%. The probability of detection (POD) is greater than 0.83 for clear skies, deep-convection, and Cirrus–Stratus type clouds” should replace with “With the Himawari-8 CLTYPE product and the CloudSat 2B-CLDCLASS product as the test comparison target, the CLP-CNN network results matched the Himawari-8 product highly by 76.8%. The probability of detection (POD) was greater than 0.709 for clear skies, deep-convection, and Cirrus–Stratus-type clouds.”
1.2. In Section 3.2. Train, Paragraph 2, on page 10
The sentence “The time intervals that are too short have small cloud changes, so the time interval of the dataset is half an hour. The time range of the dataset was 0–9 UTC.” should replace with “The dataset contained periods when reflectance received less impact. Available periods differed in different seasons.”
1.3. In Section 4.1. Evaluation Clp-Cnn with Different Channel Combination Plans, Paragraph 3, on page 13
The sentence “According to Plan 2, it is clear that the model can only achieve an accuracy of 81.2% in the absence of the VIS channels.” should replace with “According to Plan 2, it is clear that the model can only achieve an accuracy of 72.7% in the absence of the VIS channels.”
1.4. In Section 4.2. Comparison of the Results of Different Cloud Classification Models, Paragraph 2, on page 14
The sentence “The CLP-CNN achieved a POD of 0.832 in the classification of clear skies, demonstrating its good cloud detection performance compared with the results of 0.799 for CS-CNN, 0.783 for U-Net++, and 0.796 for U-Net+CBAM.” should replace with “The CLP-CNN achieved a POD of 0.751 in the classification of clear skies, demonstrating its good cloud detection performance compared with the results of 0.741 for CS-CNN, 0.734 for U-Net++, and 0.745 for U-Net+CBAM.”
1.5. In Section 4.4. Cloudsat, Paragraph 2, on page 16
The sentence “Using the classification results from the CloudSat satellite as true values, the accuracy of the CLP-CNN output reached 0.523, which is better than the 0.517 of the Himawari-8 products.” should replace with “Using the classification results from the CloudSat satellite as true values, the accuracy of the CLP-CNN output reached 0.486, which was better than the 0.473 of the Himawari-8 products.”
1.6. In section 4.4. Cloudsat, Paragraph 4, on page 17
The sentence “By comparing the output results of the CLP-CNN and Himawari-8 products shown in Figure 13 with CloudSat, it can be found that the output results of the CLP-CNN network are worse than those of the Himawari-8 products only in the classification of AC-type clouds.” should replace with “By comparing the output results of the CLP-CNN and Himawari-8 products shown in Figure 13 with CloudSat, it can be found that the output results of the CLP-CNN network were worse than those of the Himawari-8 products only in a few situations of AC, Ns, and Cu type clouds and clear.”
1.7. In Section 5. Conclusions, Paragraph 1, on page 18
The sentence “After training, the CLP-CNN achieved an 84.4% accuracy compared with the Himawari-8 cloud classification products;” should replace with “After training, the CLP-CNN achieved a 76.8% accuracy compared with the Himawari-8 cloud classification products.”
2. Error in Figure/Table
2.1. Corrections Have Been Made to Figure 9
In the original publication [1], there was a mistake in Figure 9 as published. The data contained in the figure needs to be corrected. The corrected Figure 9 appears below.
Figure 9.
Variation in the loss and accuracy of training.
2.2. Corrections Have Been Made to Table 3
In the original publication, there was a mistake in Table 3 as published. The data contained in the table needs to be corrected. The corrected Table 3 appear below.
Table 3.
The results of the three plans compared with the Himawari-8 product.
| Type | Plan 1 | Plan 2 | Plan 3 |
|---|---|---|---|
| Clear | 0.751 | 0.735 | 0.745 |
| Ci | 0.501 | 0.448 | 0.490 |
| Cs | 0.709 | 0.609 | 0.687 |
| Dc | 0.721 | 0.568 | 0.701 |
| Ac | 0.305 | 0.272 | 0.304 |
| As | 0.505 | 0.415 | 0.486 |
| Ns | 0.601 | 0.484 | 0.591 |
| Cu | 0.364 | 0.346 | 0.369 |
| Sc | 0.493 | 0.439 | 0.487 |
| St | 0.460 | 0.315 | 0.461 |
| Acc. | 0.768 | 0.727 | 0.759 |
2.3. Corrections Have Been Made to Table 4
In the original publication, there was a mistake in Table 4 as published. The data contained in the table needs to be corrected. The corrected Table 4 appears below.
Table 4.
The POD of the four models compared with the Himawari-8 product.
| Type | CS-CNN | U-Net++ | U-Net + CBAM | CLP-CNN |
|---|---|---|---|---|
| Clear | 0.741 | 0.734 | 0.745 | 0.751 |
| Ci (Cirrus) | 0.461 | 0.447 | 0.480 | 0.501 |
| CS (Cirro-Stratus) | 0.674 | 0.665 | 0.683 | 0.709 |
| Dc (Deep-convection) | 0.682 | 0.677 | 0.692 | 0.721 |
| AC (Alto-Cumulus) | 0.273 | 0.257 | 0.293 | 0.305 |
| AS (Alto-Stratus) | 0.467 | 0.460 | 0.486 | 0.505 |
| NS (Nimbo-Stratus) | 0.574 | 0.567 | 0.588 | 0.601 |
| Cu (Cumulus) | 0.337 | 0.331 | 0.364 | 0.364 |
| SC (Strato-Cumulus) | 0.470 | 0.467 | 0.485 | 0.493 |
| St (Stratus) | 0.429 | 0.420 | 0.441 | 0.460 |
| Acc. | 0.747 | 0.743 | 0.756 | 0.768 |
2.4. Corrections Have Been Made to Figure 11
In the original publication, there was a mistake in Figure 11 as published. The data contained in the figure need to be corrected. The corrected Figure 11 appear below.
Figure 11.
Results of the classification evaluation for the different seasons. (a) CLP-CNN Result Mar-May; (b) CLP-CNN Result Jun-Aug; (c) CLP-CNN Result Sep-Nov; (d) CLP-CNN Result Dec-Feb.
Figure 11.
Results of the classification evaluation for the different seasons. (a) CLP-CNN Result Mar-May; (b) CLP-CNN Result Jun-Aug; (c) CLP-CNN Result Sep-Nov; (d) CLP-CNN Result Dec-Feb.
2.5. Corrections Have Been Made to Figure 13
In the original publication, there was a mistake in Figure 13 as published. The data contained in the figure need to be corrected. The corrected Figure 13 appears below.
Figure 13.
(a) Comparison of CLP-CNN results and Himawari-8 products with CloudSat from January to February; (b) Comparison of CLP-CNN results and Himawari-8 products with CloudSat from March to May; (c) Comparison of CLP-CNN results and Himawari-8 products with CloudSat from June to July.
Figure 13.
(a) Comparison of CLP-CNN results and Himawari-8 products with CloudSat from January to February; (b) Comparison of CLP-CNN results and Himawari-8 products with CloudSat from March to May; (c) Comparison of CLP-CNN results and Himawari-8 products with CloudSat from June to July.
The authors apologize for any inconvenience caused and state that the scientific conclusions are unaffected. The original article has been updated.
Reference
- Jiang, Y.; Cheng, W.; Gao, F.; Zhang, S.; Wang, S.; Liu, C.; Liu, J. A Cloud Classification Method Based on a Convolutional Neural Network for FY-4A Satellites. Remote Sens. 2022, 14, 2314. [Google Scholar] [CrossRef]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
MDPI and ACS Style
Jiang, Y.; Cheng, W.; Gao, F.; Zhang, S.; Wang, S.; Liu, C.; Liu, J. Correction: Jiang et al. A Cloud Classification Method Based on a Convolutional Neural Network for FY-4A Satellites. Remote Sens. 2022, 14, 2314. Remote Sens. 2023, 15, 4368. https://doi.org/10.3390/rs15184368
AMA Style
Jiang Y, Cheng W, Gao F, Zhang S, Wang S, Liu C, Liu J. Correction: Jiang et al. A Cloud Classification Method Based on a Convolutional Neural Network for FY-4A Satellites. Remote Sens. 2022, 14, 2314. Remote Sensing. 2023; 15(18):4368. https://doi.org/10.3390/rs15184368
Chicago/Turabian StyleJiang, Yuhang, Wei Cheng, Feng Gao, Shaoqing Zhang, Shudong Wang, Chang Liu, and Juanjuan Liu. 2023. "Correction: Jiang et al. A Cloud Classification Method Based on a Convolutional Neural Network for FY-4A Satellites. Remote Sens. 2022, 14, 2314" Remote Sensing 15, no. 18: 4368. https://doi.org/10.3390/rs15184368
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.
