Name
Title | ||
|---|---|---|
Spatiotemporal assessments of nutrients and water quality in coastal areas using remote sensing and a spatiotemporal deep learning model |
Abstract | ||
|---|---|---|
•A deep-learning based technology was developed to estimate large-scale coastal nutrients.•We achieved robust prediction performance in the remotely sensed nutrient estimation.•Nutrients concentration continually decreased but DIN still higher than the water quality standard.•DIN contributed 93.9% to the worst quality while DIP only accounted for 37.8%.•Yangtze River Diluted Water has seriously affected the nutrient concentration in ZCS. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.jag.2022.102897 | International Journal of Applied Earth Observation and Geoinformation |
Keywords | DocType | Volume |
Aquatic environment,Spatiotemporal deep learning,Water quality,Remote sensing,Coastal restoration | Journal | 112 |
ISSN | Citations | PageRank |
1569-8432 | 0 | 0.34 |
References | Authors | |
3 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sensen Wu | 1 | 0 | 0.34 |
| Jin Qi | 2 | 0 | 0.34 |
| Zhen Yan | 3 | 0 | 1.35 |
| Fangzheng Lyu | 4 | 0 | 0.34 |
| Tao Lin | 5 | 53 | 3.62 |
| Yuanyuan Wang | 6 | 498 | 82.58 |
| Zhenhong Du | 7 | 31 | 16.98 |