Name
Title | ||
|---|---|---|
Super-resolution of subsurface temperature field from remote sensing observations based on machine learning |
Abstract | ||
|---|---|---|
•A new deep neural network approach to reconstruct subsurface temperature.•Super-resolution of subsurface temperature from 1° to 0.25° from remote sensing.•CNN outperformed LightGBM in the case of big training samples.•Higher-resolution data support for global ocean warming and internal variability study. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.jag.2021.102440 | International Journal of Applied Earth Observation and Geoinformation |
Keywords | DocType | Volume |
Super-Resolution,Subsurface Temperature,Remote Sensing,Global Ocean,LightGBM,Convolutional Neural Network | Journal | 102 |
ISSN | Citations | PageRank |
1569-8432 | 1 | 0.38 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hua Su | 1 | 1 | 0.38 |
| An Wang | 2 | 1 | 0.38 |
| Tianyi Zhang | 3 | 1 | 0.38 |
| Tian Qin | 4 | 1 | 0.38 |
| Xiaoping Du | 5 | 30 | 5.96 |
| Xiao-Hai Yan | 6 | 20 | 7.36 |