Cloud probability distribution of typical urban agglomerations in China based on Sentinel-2 satellite remote sensing
Cloud distribution significantly impacts global climate change, ecosystem health, urban environments, and satellite remote sensing observations. However, past research has primarily focused on the meteorological characteristics of clouds with limitations in scale and resolution, leading to an insuff...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843224006101 |
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| author | Jing Ling Rui Liu Shan Wei Shaomei Chen Luyan Ji Yongchao Zhao Hongsheng Zhang |
| author_facet | Jing Ling Rui Liu Shan Wei Shaomei Chen Luyan Ji Yongchao Zhao Hongsheng Zhang |
| author_sort | Jing Ling |
| collection | DOAJ |
| description | Cloud distribution significantly impacts global climate change, ecosystem health, urban environments, and satellite remote sensing observations. However, past research has primarily focused on the meteorological characteristics of clouds with limitations in scale and resolution, leading to an insufficient understanding of large-scale cloud distribution and its relationship with land surface cover and urbanization. This study investigates the cloud distribution characteristics of typical urban agglomerations in different climatic regions of China using high-resolution Sentinel-2 satellite imagery and the Google Earth Engine platform. A cloud probability descriptor was constructed based on three years of high spatiotemporal resolution observations. The results revealed significant differences in cloud distribution among urban agglomerations, challenging the traditional understanding based on climate zoning. The Northeast urban agglomeration in the temperate zone exhibited high cloud coverage (37.54%), while the Chengdu-Chongqing urban agglomeration in the subtropical zone and the Qinghai-Tibet Plateau urban agglomeration in the plateau climate zone had even higher average cloud probabilities (50.72% and 43.27%, respectively). The analysis suggests land surface cover, urbanization, and other surface factors may influence cloud distribution patterns. These findings emphasize the ubiquity of cloud cover and highlight the importance of considering the complex interactions among geographical factors when characterizing cloud cover diversity. This study contributes to providing new insights for enhancing meteorological models and remote sensing observation strategies in cloudy environments across different climate zones. |
| format | Article |
| id | doaj-art-bac311dad34a427e8d48122e0dc999b4 |
| institution | DOAJ |
| issn | 1569-8432 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-bac311dad34a427e8d48122e0dc999b42025-08-20T02:52:23ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322024-12-0113510425410.1016/j.jag.2024.104254Cloud probability distribution of typical urban agglomerations in China based on Sentinel-2 satellite remote sensingJing Ling0Rui Liu1Shan Wei2Shaomei Chen3Luyan Ji4Yongchao Zhao5Hongsheng Zhang6Department of Geography, The University of Hong Kong, Pokfulam, Hong Kong SAR, China; School of Information Engineering, Guangdong University of Technology, Guangzhou, China; The University of Hong Kong Shenzhen Institute of Research and Innovation, Shenzhen, ChinaDepartment of Geography, The University of Hong Kong, Pokfulam, Hong Kong SAR, China; The University of Hong Kong Shenzhen Institute of Research and Innovation, Shenzhen, ChinaDepartment of Geography, The University of Hong Kong, Pokfulam, Hong Kong SAR, China; The University of Hong Kong Shenzhen Institute of Research and Innovation, Shenzhen, ChinaDepartment of Geography, The University of Hong Kong, Pokfulam, Hong Kong SAR, China; The University of Hong Kong Shenzhen Institute of Research and Innovation, Shenzhen, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaDepartment of Geography, The University of Hong Kong, Pokfulam, Hong Kong SAR, China; The University of Hong Kong Shenzhen Institute of Research and Innovation, Shenzhen, China; Corresponding author.Cloud distribution significantly impacts global climate change, ecosystem health, urban environments, and satellite remote sensing observations. However, past research has primarily focused on the meteorological characteristics of clouds with limitations in scale and resolution, leading to an insufficient understanding of large-scale cloud distribution and its relationship with land surface cover and urbanization. This study investigates the cloud distribution characteristics of typical urban agglomerations in different climatic regions of China using high-resolution Sentinel-2 satellite imagery and the Google Earth Engine platform. A cloud probability descriptor was constructed based on three years of high spatiotemporal resolution observations. The results revealed significant differences in cloud distribution among urban agglomerations, challenging the traditional understanding based on climate zoning. The Northeast urban agglomeration in the temperate zone exhibited high cloud coverage (37.54%), while the Chengdu-Chongqing urban agglomeration in the subtropical zone and the Qinghai-Tibet Plateau urban agglomeration in the plateau climate zone had even higher average cloud probabilities (50.72% and 43.27%, respectively). The analysis suggests land surface cover, urbanization, and other surface factors may influence cloud distribution patterns. These findings emphasize the ubiquity of cloud cover and highlight the importance of considering the complex interactions among geographical factors when characterizing cloud cover diversity. This study contributes to providing new insights for enhancing meteorological models and remote sensing observation strategies in cloudy environments across different climate zones.http://www.sciencedirect.com/science/article/pii/S1569843224006101Cloud distribution characteristicsClimate changeRemote sensing observationUrbanizationLand coverSubtropical |
| spellingShingle | Jing Ling Rui Liu Shan Wei Shaomei Chen Luyan Ji Yongchao Zhao Hongsheng Zhang Cloud probability distribution of typical urban agglomerations in China based on Sentinel-2 satellite remote sensing International Journal of Applied Earth Observations and Geoinformation Cloud distribution characteristics Climate change Remote sensing observation Urbanization Land cover Subtropical |
| title | Cloud probability distribution of typical urban agglomerations in China based on Sentinel-2 satellite remote sensing |
| title_full | Cloud probability distribution of typical urban agglomerations in China based on Sentinel-2 satellite remote sensing |
| title_fullStr | Cloud probability distribution of typical urban agglomerations in China based on Sentinel-2 satellite remote sensing |
| title_full_unstemmed | Cloud probability distribution of typical urban agglomerations in China based on Sentinel-2 satellite remote sensing |
| title_short | Cloud probability distribution of typical urban agglomerations in China based on Sentinel-2 satellite remote sensing |
| title_sort | cloud probability distribution of typical urban agglomerations in china based on sentinel 2 satellite remote sensing |
| topic | Cloud distribution characteristics Climate change Remote sensing observation Urbanization Land cover Subtropical |
| url | http://www.sciencedirect.com/science/article/pii/S1569843224006101 |
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