Name
Abstract | ||
|---|---|---|
Sea surface temperature (SST) prediction is not only theoretically important but also has a number of practical applications across a variety of ocean-related fields. Although a large amount of SST data obtained via remote sensor are available, previous work rarely attempted to predict future SST values from history data in spatiotemporal perspective. This letter regards SST prediction as a sequen... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/LGRS.2017.2780843 | IEEE Geoscience and Remote Sensing Letters |
Keywords | Field | DocType |
Predictive models,Logic gates,Ocean temperature,Convolution,Data models,Sea surface | Spatial analysis,Data modeling,Time series,Data set,Sea surface temperature,Remote sensing,Advanced very-high-resolution radiometer,Pixel,Artificial neural network,Mathematics | Journal |
Volume | Issue | ISSN |
15 | 2 | 1545-598X |
Citations | PageRank | References |
6 | 0.48 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuting Yang | 1 | 44 | 10.79 |
| Junyu Dong | 2 | 99 | 23.43 |
| Xin Sun | 3 | 51 | 10.45 |
| Estanislau Lima | 4 | 12 | 1.65 |
| Quanquan Mu | 5 | 8 | 1.26 |
| Xinhua Wang | 6 | 7 | 0.88 |