Geoclimatic Distribution of Satellite-Observed Salinity Bias Classified by Machine Learning Approach
Abstract
:1. Introduction
2. Data and Methods
2.1. Data
2.2. Unsupervised Machine Learning Classification
3. Geoclimatic Distribution of the Classes
3.1. Environmental Signatures of the Classification Result
3.2. Similar Classes in the Different SST Range
3.3. Classifying the Outliers
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Number of Class | ΔS (PSU) | SST (°C) | RAIN (mm/Day) | WIND (m/s) | ΔS SD (PSU) | Percentage of Total Data Volume |
---|---|---|---|---|---|---|
1 | 0.09 | 12.58 | 0.00 | 6.96 | 0.56 | 19.17% |
2 | 0.24 | 7.53 | 2.43 | 9.82 | 0.55 | 4.63% |
3 | 0.10 | 28.13 | 0.04 | 6.23 | 0.23 | 4.97% |
4 | −0.04 | 22.19 | 24.82 | 7.88 | 0.42 | 0.84% |
5 | −0.37 | 13.19 | 1.11 | 10.12 | 1.50 | 0.67% |
6 | 0.09 | 11.61 | 7.99 | 9.38 | 0.58 | 2.71% |
7 | −0.07 | 21.93 | 2.71 | 7.57 | 0.24 | 2.36% |
8 | 0.02 | 29.10 | 5.96 | 5.66 | 0.30 | 3.20% |
9 | 0.00 | 15.38 | 0.58 | 8.66 | 0.33 | 5.85% |
10 | 0.05 | 4.05 | 0.31 | 10.05 | 1.03 | 2.74% |
11 | 0.07 | 25.26 | 0.00 | 6.25 | 0.22 | 35.04% |
12 | −2.43 | 1.14 | 3.92 | 10.54 | 5.64 | 0.23% |
13 | 0.07 | 26.58 | 0.16 | 6.80 | 0.19 | 5.81% |
14 | 0.05 | 13.61 | 0.07 | 8.53 | 0.43 | 6.43% |
15 | 0.06 | 28.66 | 0.84 | 5.99 | 0.25 | 5.35% |
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Ouyang, Y.; Zhang, Y.; Feng, M.; Boschetti, F.; Du, Y. Geoclimatic Distribution of Satellite-Observed Salinity Bias Classified by Machine Learning Approach. Remote Sens. 2024, 16, 3084. https://doi.org/10.3390/rs16163084
Ouyang Y, Zhang Y, Feng M, Boschetti F, Du Y. Geoclimatic Distribution of Satellite-Observed Salinity Bias Classified by Machine Learning Approach. Remote Sensing. 2024; 16(16):3084. https://doi.org/10.3390/rs16163084
Chicago/Turabian StyleOuyang, Yating, Yuhong Zhang, Ming Feng, Fabio Boschetti, and Yan Du. 2024. "Geoclimatic Distribution of Satellite-Observed Salinity Bias Classified by Machine Learning Approach" Remote Sensing 16, no. 16: 3084. https://doi.org/10.3390/rs16163084