气候变化领域集成服务门户(CCPortal): A time-continuous land surface temperature (LST) data fusion approach based on deep learning with microwave remote sensing and high-density ground truth observations

CCPortal

DOI	10.1016/j.scitotenv.2024.169992
	A time-continuous land surface temperature (LST) data fusion approach based on deep learning with microwave remote sensing and high-density ground truth observations
	Han, Jiahao; Fang, Shibo; Mi, Qianchuan; Wang, Xinyu; Yu, Yanru; Zhuo, Wen; Peng, Xiaofeng
发表日期	2024
ISSN	0048-9697
EISSN	1879-1026
起始页码	914
卷号	914
英文摘要	Land surface temperature (LST) is a crucial parameter in the circulation of water, exchange of land-atmosphere energy, and turbulence. Currently, most LST products rely heavily on thermal infrared remote sensing, which is susceptible to cloud and rain interference, leading to inferior temporal continuity. Microwave remote sensing has the advantage of being available all-weather due to strong penetration capability, which provides the possibility to simulate time-continuous LST data. In addition, the continuous increase in high-density station observations (>10,000 stations) provides reliable measured data for the remote sensing monitoring of LST in China. This study aims to adopt the Earth big data generated from high-density station observation and microwave remote sensing data to monitor LST based on deep learning (U-Net family) for the first time. Given the significant spatial and temporal variability of LST and its sensitivity to various factors according to radiation transmission equations, this study incorporated climatic, anthropogenic, geographical, and vegetation datasets to facilitate a multi-source data fusion approach for LST estimation. The results showed that the U-Net++ model with modified skip connections better minimized the semantic discrepancy between the feature maps of the encoder and decoder subnetworks for 0.1 degrees daily LST mapping across China than the U-Net and U-2-Net deep learning models. The accuracy of the LST simulation exhibited favorable outcomes in the spatial and temporal dimensions. The station density met the requirements of monitoring air-ground integration monitoring in China. Additionally, the temporal change in the simulation accuracy fluctuated in a W-shape owing to the limited simulation capability of deep learning in extreme scenarios. Anthropogenic factors had the largest influence on LST changes in China, followed by climate, geography, and vegetation. This study highlighted the application of deep learning in remote sensing monitoring against the background of big data and provided a scientific foundation for the response of climate change to human activities, ecological environmental protection, and sustainable social and economic development.
英文关键词	Land surface temperature; Earth big data; Deep learning; FY-3C/3D microwave remote sensing; Climate driven; Anthropogenic driven
语种	英语
WOS研究方向	Environmental Sciences & Ecology
WOS类目	Environmental Sciences
WOS记录号	WOS:001167901600001
来源期刊	SCIENCE OF THE TOTAL ENVIRONMENT
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/305607
作者单位	Nanjing University of Information Science & Technology; China Meteorological Administration; Chinese Academy of Meteorological Sciences (CAMS)
推荐引用方式 GB/T 7714	Han, Jiahao,Fang, Shibo,Mi, Qianchuan,et al. A time-continuous land surface temperature (LST) data fusion approach based on deep learning with microwave remote sensing and high-density ground truth observations[J],2024,914.
APA	Han, Jiahao.,Fang, Shibo.,Mi, Qianchuan.,Wang, Xinyu.,Yu, Yanru.,...&Peng, Xiaofeng.(2024).A time-continuous land surface temperature (LST) data fusion approach based on deep learning with microwave remote sensing and high-density ground truth observations.SCIENCE OF THE TOTAL ENVIRONMENT,914.
MLA	Han, Jiahao,et al."A time-continuous land surface temperature (LST) data fusion approach based on deep learning with microwave remote sensing and high-density ground truth observations".SCIENCE OF THE TOTAL ENVIRONMENT 914(2024).