气候变化领域集成服务门户(CCPortal): Atmospheric ammonia point source detection technique at regional scale using high resolution satellite imagery and deep learning

CCPortal

DOI	10.1016/j.atmosres.2021.105587
	Atmospheric ammonia point source detection technique at regional scale using high resolution satellite imagery and deep learning
	Lei M.; Cheng T.; Li X.; Shi S.; Zuo X.; Guo H.; Wu Y.
发表日期	2021
ISSN	0169-8095
卷号	257
英文摘要	Ammonia (NH3) is a precursor of atmospheric aerosols. The lack of real-time updated and high spatial resolution atmospheric NH3 point source information is one of the critical problems in the study of atmospheric pollution. Satellite-based NH3 observation data provide a new approach to NH3 point source detection. However current spatial resolution of NH3 concentration satellite data is still not sufficient to extract NH3 point sources at regional scale with high accuracy. Based on the traditional oversampling method to improve the spatial resolution of NH3 satellite data, this paper used high-resolution satellite image data with the geometric morphological features of NH3 point sources to detect the NH3 point sources at regional scale under deep learning techniques. The high-precision NH3 point source location and category information at regional scale is extracted concurrently, solving the current problem that multiple point sources cannot be detected in a single satellite NH3 concentration hotspot. After analyzing a priori knowledge, the industrial sources were further subdivided. The atmospheric ammonia point source detection technique in this paper were used in Northeast China, where the number and types of NH3 emission sources are abundant. The detection results significantly increased the number of NH3 potential point sources detected in Northeast China compared with the traditional oversampling method. The deep learning-based technology combines NH3 concentration remote sensing products with high resolution satellite image data to effectively detect the spatial distribution and types of NH3 point sources at regional scale, providing powerful technical support for NH3 emission regulation and control. © 2021 Elsevier B.V.
英文关键词	Deep learning; High resolution satellite imagery; IASI-NH3; NH3 point resources; Regional scale
来源期刊	Atmospheric Research
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/236757
作者单位	University of Chinese Academy of Sciences, Beijing, 100049, China; Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China
推荐引用方式 GB/T 7714	Lei M.,Cheng T.,Li X.,et al. Atmospheric ammonia point source detection technique at regional scale using high resolution satellite imagery and deep learning[J],2021,257.
APA	Lei M..,Cheng T..,Li X..,Shi S..,Zuo X..,...&Wu Y..(2021).Atmospheric ammonia point source detection technique at regional scale using high resolution satellite imagery and deep learning.Atmospheric Research,257.
MLA	Lei M.,et al."Atmospheric ammonia point source detection technique at regional scale using high resolution satellite imagery and deep learning".Atmospheric Research 257(2021).