Multi-spectral Remote Sensing of Sea Fog with Simultaneous Passive Infrared and Microwave Sensors

2017 ◽  
pp. 511-526 ◽  
Author(s):  
Eric M. Wilcox
Keyword(s):  
Remote Sensing ◽  
Sea Fog ◽  
Microwave Sensors ◽  
Passive Infrared

Optical Radiance Characteristics of Sea Fog Based on Remote Sensing

2008 ◽  
Vol 28 (12) ◽  
pp. 2420-2426 ◽  
Author(s):  
郝增周 Hao Zengzhou ◽  
潘德炉 Pan Delu ◽  
龚芳 Gong Fang ◽  
朱乾坤 Zhu Qiankun
Keyword(s):  
Remote Sensing ◽  
Sea Fog

Evaluation of Sea fog Detection Accuracy Based on Geostationary Satellite Image Using Machine Learning

2020 ◽  
Author(s):  
NaKyeong Kim ◽  
Suho Bak ◽  
Minji Jeong ◽  
Hongjoo Yoon
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Geostationary Satellite ◽  
Machine Learning Algorithms ◽  
Detection Accuracy ◽  
Sea Fog ◽  
Short Period ◽  
Wide Range ◽  
Fog Detection

<p><span>A sea fog is a fog caused by the cooling of the air near the ocean-atmosphere boundary layer when the warm sea surface air moves to a cold sea level. Sea fog affects a variety of aspects, including maritime and coastal transportation, military activities and fishing activities. In particular, it is important to detect sea fog as they can lead to ship accidents due to reduced visibility. Due to the wide range of sea fog events and the lack of constant occurrence, it is generally detected through satellite remote sensing. Because sea fog travels in a short period of time, it uses geostationary satellites with higher time resolution than polar satellites to detect fog. A method for detecting fog by using the difference between 11 μm channel and 3.7 μm channel was widely used when detecting fog by satellite remote sensing, but this is difficult to distinguish between lower clouds and fog. Traditional algorithms are difficult to find accurate thresholds for fog and cloud. However, machine learning algorithms can be used as a useful tool to determine this. In this study, based on geostationary satellite imaging data, a comparative analysis of sea fog detection accuracy was conducted through various methods of machine learning, such as Random Forest, Multi-Layer Perceptron, and Convolutional Neural Networks.</span></p>

scholarly journals Supplementing Oscat winds with Saral Altika observations for cyclone studies

2014 ◽  
Vol XL-8 ◽  
pp. 1059-1064 ◽  
Author(s):  
K. Niharika ◽  
H. S. V. Usha Sundari ◽  
A. V. V. Prasad ◽  
E. V. S. Sita Kumari ◽  
V. K. Dadhwal ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Life Cycle ◽  
Wind Speed ◽  
Storm Surge ◽  
Disaster Management ◽  
Management Practices ◽  
In Situ Observations ◽  
Remote Sensing Techniques ◽  
Microwave Sensors

Accurate prediction of life cycle of cyclone is very critical to the disaster management practices. Since the cyclones originate over the oceans where in situ observations are limited, we have to resort to the remote sensing techniques. Both optical and microwave sensors help studying the cyclones. While scatterometer provide wind vectors, altimeters can give only wind speed. In this paper we present how altimeter measurements can supplement the scatterometer observations in determining the radius of maximum winds (RMW). Sustained maximum winds, indicator for the intensity of the cyclone, are within the eye wall of a cyclone at a distance of RMW. This parameter is also useful in predicting right time of the storm surge. In this paper we used the wind speed estimations from AltiKa, an altimeter operating at Ka band.

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