scholarly journals A Study on Brightness Reversal of Internal Waves in the Celebes Sea Using Himawari-8 Images

2021 ◽  
Vol 13 (19) ◽  
pp. 3831
Author(s):  
Beilei Hu ◽  
Junmin Meng ◽  
Lina Sun ◽  
Hao Zhang

A geostationary meteorological satellite is located at a fixed point above the equator, which can continuously observe internal waves and provides great advantages in research on changes in the generation and propagation of internal waves. The scale of internal waves in the Celebes Sea is large, which is still very obvious in geostationary meteorological satellite images with a lower spatial resolution. This study considers continuous remote sensing images of geostationary meteorological satellite Himawari-8 to analyze the bright and dark features of internal waves in the Celebes Sea in optical remote sensing images. The solar zenith angle, sensor zenith angle and relative azimuth angle of internal waves in six images are calculated, and the changes are 12.45°, 0.20° and 3.44°, respectively, within 50 min. Moreover, based on the normalized sunglint radiance theory, the critical solar viewing angle is proposed and verified. The results indicate that the bright and dark features of internal waves when passing through sunglint and non-sunglint areas are greatly reversed, and the critical solar viewing angles are 18.73° and 27.41°, respectively. In this study, geostationary meteorological satellite Himawari-8 images are analyzed to study on the brightness reversal phenomenon of internal waves for the first time, and a unique brightness change in internal waves during the propagation process is revealed, which has not been reported in existing research.

Author(s):  
T. Zhang ◽  
B. Lei ◽  
Y. Hu ◽  
K. Liu ◽  
Y. Gan

Optical remote sensing images have been widely used in feature interpretation and geo-information extraction. All the fundamental applications of optical remote sensing, are greatly influenced by cloud coverage. Generally, the availability of cloudless images depends on the meteorological conditions for a given area. In this study, the cloud total amount (CTA) products of the Fengyun (FY) satellite were introduced to explore the meteorological changes in a year over China. The cloud information of CTA products were tested by using ZY-3 satellite images firstly. CTA products from 2006 to 2017 were used to get relatively reliable results. The window period of cloudless images acquisition for different areas in China was then determined. This research provides a feasible way to get the cloudless images acquisition window by using meteorological observations.


2021 ◽  
Vol 13 (3) ◽  
pp. 441
Author(s):  
Han Fu ◽  
Bihong Fu ◽  
Pilong Shi

The South China Karst, a United Nations Educational, Scientific and Cultural Organization (UNESCO) natural heritage site, is one of the world’s most spectacular examples of humid tropical to subtropical karst landscapes. The Libo cone karst in the southern Guizhou Province is considered as the world reference site for these types of karst, forming a distinctive and beautiful landscape. Geomorphic information and spatial distribution of cone karst is essential for conservation and management for Libo heritage site. In this study, a deep learning (DL) method based on DeepLab V3+ network was proposed to document the cone karst landscape in Libo by multi-source data, including optical remote sensing images and digital elevation model (DEM) data. The training samples were generated by using Landsat remote sensing images and their combination with satellite derived DEM data. Each group of training dataset contains 898 samples. The input module of DeepLab V3+ network was improved to accept four-channel input data, i.e., combination of Landsat RGB images and DEM data. Our results suggest that the mean intersection over union (MIoU) using the four-channel data as training samples by a new DL-based pixel-level image segmentation approach is the highest, which can reach 95.5%. The proposed method can accomplish automatic extraction of cone karst landscape by self-learning of deep neural network, and therefore it can also provide a powerful and automatic tool for documenting other type of geological landscapes worldwide.


2021 ◽  
Vol 30 ◽  
pp. 1305-1317
Author(s):  
Qijian Zhang ◽  
Runmin Cong ◽  
Chongyi Li ◽  
Ming-Ming Cheng ◽  
Yuming Fang ◽  
...  

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