Oil spills frequently occur on ocean surfaces and may cause great harm to marine life, the environment, and international economies. Oil spills can kill marine animals and plants and seriously pollute the air with toxic volatiles. Accurately detecting oil spills via remote sensing observations plays an important role in environmental protection and emergency responses to marine accidents. Meanwhile, the scope of pollution from oil spills continues to expand mainly because of oil spill drift and diffusion. It is necessary to dynamically predict and (back)track oil spill trajectory via remote sensing observations and numerical prediction models. Prediction and tracking of oil spills can evaluate the pollution trend and timely warn related areas. Backtracking of oil spills can find the source of the oil spills in time to reduce the pollution output as little as possible.
Oil spill trajectory is generally driven by some marine meteorological components such as winds, currents, waves, temperatures, etc. Numerical prediction models are conducted by comprehensively integrating the impacts of these components and output predicted oil spill trajectory. Newly observed oil spill images can be continuously fed into the numerical prediction models as location initialization. Therefore, abundant observations, accurate winds, currents, etc, and comprehensive models are critical in the prediction and (back)tracking of oil spill trajectory.
The goal of this research topic is to collect a series of papers aiming at improving the accuracy of the prediction and (back)tracking of oil spills. Prediction and (back)tracking of oil spill heavily depend on the accuracies of meteorological components that are generally obtained from numerical forecasts such that their accuracies are limited in existing research. How to improve the accuracy of numerical forecasts is an urgent problem to tackle in this research topic. More advanced oil spill detection methods and comprehensive numerical prediction models will contribute to accurate oil spill trajectory prediction. Research on oil spill remote sensing properties and numerical prediction models is urgently needed in this research topic.
This Research Topic welcomes the following themes:
1. Multi-source remote sensing observation and detection for oil spill trajectory tracking
2. Meteorological components forecast for prediction and (back)tracking of oil spills
3. Oil spill trajectory prediction based on machine learning technology
4. Oil spill backtracking based on machine learning technology
5. Research of numerical prediction models for oil spill drift and diffusion
6. Reviews and analysis of oil spill prediction and (back)tracking methods
Oil spills frequently occur on ocean surfaces and may cause great harm to marine life, the environment, and international economies. Oil spills can kill marine animals and plants and seriously pollute the air with toxic volatiles. Accurately detecting oil spills via remote sensing observations plays an important role in environmental protection and emergency responses to marine accidents. Meanwhile, the scope of pollution from oil spills continues to expand mainly because of oil spill drift and diffusion. It is necessary to dynamically predict and (back)track oil spill trajectory via remote sensing observations and numerical prediction models. Prediction and tracking of oil spills can evaluate the pollution trend and timely warn related areas. Backtracking of oil spills can find the source of the oil spills in time to reduce the pollution output as little as possible.
Oil spill trajectory is generally driven by some marine meteorological components such as winds, currents, waves, temperatures, etc. Numerical prediction models are conducted by comprehensively integrating the impacts of these components and output predicted oil spill trajectory. Newly observed oil spill images can be continuously fed into the numerical prediction models as location initialization. Therefore, abundant observations, accurate winds, currents, etc, and comprehensive models are critical in the prediction and (back)tracking of oil spill trajectory.
The goal of this research topic is to collect a series of papers aiming at improving the accuracy of the prediction and (back)tracking of oil spills. Prediction and (back)tracking of oil spill heavily depend on the accuracies of meteorological components that are generally obtained from numerical forecasts such that their accuracies are limited in existing research. How to improve the accuracy of numerical forecasts is an urgent problem to tackle in this research topic. More advanced oil spill detection methods and comprehensive numerical prediction models will contribute to accurate oil spill trajectory prediction. Research on oil spill remote sensing properties and numerical prediction models is urgently needed in this research topic.
This Research Topic welcomes the following themes:
1. Multi-source remote sensing observation and detection for oil spill trajectory tracking
2. Meteorological components forecast for prediction and (back)tracking of oil spills
3. Oil spill trajectory prediction based on machine learning technology
4. Oil spill backtracking based on machine learning technology
5. Research of numerical prediction models for oil spill drift and diffusion
6. Reviews and analysis of oil spill prediction and (back)tracking methods
